Methods and systems are provided for use in mapping related to high-resolution land cover. One example computer-implemented method includes receiving a first satellite image of a scene at a first resolution, and receiving second satellite images of the scene at a second resolution over a period of time. The method also includes performing a segmentation of the first satellite image, performing transformations on the second satellite images, and creating a temporal stack layer for multiple temporal statistics for each of the transformations on the second satellite images. The method further includes intersecting the temporal stack layer with the segmented first satellite image using multiple spatial statistics to produce spatio-temporal layers and classifying, using a Machine Learning (ML) model, based on the spatio-temporal layers, pixels of the first satellite image, as grouped by the segmentation, into respective ones of a plurality of classifications of land cover.
G06V 10/77 - Traitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source
G06T 7/35 - Détermination des paramètres de transformation pour l'alignement des images, c.-à-d. recalage des images utilisant des procédés statistiques
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
Systems and methods for mitigating shadow segments from images are provided. One example computer-implemented method includes accessing an original image of a geospatial location including a first shadow segment and generating, using a model architecture, a matte for the original image. The method also includes generating a first histogram of tones of shadow pixels of the first shadow segment in the original image, generating a second histogram of tones of an adjacent region of the original image, which is proximate to the first shadow segment yet outside a boundary of the first shadow segment, defining a lookup table based on histogram matching between the first histogram and the second histogram, and relighting the original image based on the lookup table.
G06V 10/60 - Extraction de caractéristiques d’images ou de vidéos relative aux propriétés luminescentes, p. ex. utilisant un modèle de réflectance ou d’éclairage
G06T 5/40 - Amélioration ou restauration d'image utilisant des techniques d'histogrammes
3.
METHODS AND SYSTEMS FOR USE IN FEATURE-SPECIFIC CHANGE DETECTION FOR IMAGERY
Systems and methods for detecting changes in features, based on images of geographic regions of interest. One example computer-implemented method includes accessing, by a computing device, a plurality of images of a region of interest (ROI) and compiling, by the computing device, a temporal stack of images, which includes the plurality of images of the ROI arranged in temporal order over a period of time. The computer-implemented method also includes classifying, by the computing device, using semantic segmentation, pixels of each of the plurality of images into one of multiple feature classes, to define a segmentation raster, and performing, by the computing device, change point detection change point detection on a temporal stack of the segmentation rasters. The computer-implemented-method further includes identifying, by the computing device, a change in one of the multiple feature classes, based on the change point detection output.
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
Examples of the present disclosure include a method for approximating a non-carth imaging camera model for a non-earth sensor includes obtaining a physical camera model, receiving a target range value, determining a plurality of space-based coordinates of a plurality of points about a line of sight of the sensor in proximity to a distance corresponding to the received target range value, for each of the plurality of locations, determining line and sample coordinates on an image thereof via the physical camera model based on the space-based coordinates of the plurality of points, and based on the determined line and sample coordinates for each of the plurality of points, fitting an approximate camera model thereto.
A computer-implemented method is provided for estimating velocity of a moving object. The method includes receiving an image, which includes a first strip generated by a first sensor and a second strip generated by a second sensor, and recognizing a moving object in the first strip of the image. The method also includes selecting a plurality of stationary objects that are captured with the moving object in the first strip of the image, while excluding stationary object(s) in the second strip of the image, wherein the plurality of stationary objects are non-moving features. The method further includes combining misregistration values for the plurality of stationary objects in the first strip of the image and calculating a velocity of the moving object, based on a raw velocity of the moving object and the combined misregistration values.
G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p. ex. véhicules ou piétonsReconnaissance des objets de la circulation, p. ex. signalisation routière, feux de signalisation ou routes
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p. ex. le suivi des coins ou des segments
G06T 7/30 - Détermination des paramètres de transformation pour l'alignement des images, c.-à-d. recalage des images
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
H04N 23/68 - Commande des caméras ou des modules de caméras pour une prise de vue stable de la scène, p. ex. en compensant les vibrations du boîtier de l'appareil photo
A method for performing land classification operations, the method can comprise receiving, by one or more servers, a plurality of High Resolution Land Cover (“HRLC”) final land cover layers; training, by one or more servers, a model using the plurality of HRLC final land cover layers to form a trained deep learning HRLC (“DL-HRLC”) model; and transferring, by the one or more servers, the trained DL-HRLC model for land cover prediction, wherein a land cover inference engine of the trained DL-HRLC model classifies a single image, which comprises a plurality of pixels, to generate an output land cover layer prediction.
G06T 11/40 - Remplissage d'une surface plane par addition d'attributs de surface, p. ex. de couleur ou de texture
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/82 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les réseaux neuronaux
In some embodiments, a method for training a machine learning device includes: ascertaining geographic location information of at least one portion of a first image associated with a label; associating with the label a second image including at least a portion having substantially the same geographic location information as the at least one portion of the first image; optional alignment or coregistration of the first and second image to maximize mutual information overlap; forming a training dataset comprising the first and second images as input images and the label that the first and second images are associated with as outputs; optional binary categorization and curation of the resulting training dataset to ensure accuracy; and training the machine learning model using the augmented dataset.
G06V 10/772 - Détermination de motifs de référence représentatifs, p. ex. motifs de valeurs moyennes ou déformantsGénération de dictionnaires
G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
Methods and systems for performing cloud and shadow matching for a high-altitude image of a portion of earth, the method including performing segmentation of clouds and shadows in the high-altitude image, determining, on an image of the portion of earth, determining a cloud-to-shadow vector, performing maximum bipartite matching to associate one shadow to at least one cloud, grouping at least one cloud and at least one shadow into at least one cluster, and estimating false positives and false negatives in the cloud and shadow segmentation.
G06V 10/74 - Appariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques
G06V 10/26 - Segmentation de formes dans le champ d’imageDécoupage ou fusion d’éléments d’image visant à établir la région de motif, p. ex. techniques de regroupementDétection d’occlusion
G06V 10/75 - Organisation de procédés de l’appariement, p. ex. comparaisons simultanées ou séquentielles des caractéristiques d’images ou de vidéosApproches-approximative-fine, p. ex. approches multi-échellesAppariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques utilisant l’analyse de contexteSélection des dictionnaires
G06V 10/762 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant le regroupement, p. ex. de visages similaires sur les réseaux sociaux
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
G06V 10/86 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant les représentations syntaxiques ou structurelles du motif d’image ou vidéo, p. ex. reconnaissance des chaînes symboliquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant des correspondances graphiques
Satellite images have inherent geo-positional errors of orders a few meters. Corrections are achieved by adjusting a sensor model which maps ground coordinates of control features into image coordinates and establishing a correspondence between the ground and image features, in this case a road network. The ground coordinates are obtained from mobile pose points. To adjust the sensor model we rely on the fact that the roads are typically much more uniform than surrounding features, and therefore have smaller entropy. The sensor model is adjusted so that the image pixels, obtained from projecting ground coordinates of the mobile pose points onto the image, minimize the entropy of the pixels that represent the road network.
When generating ortho rectified imagery or products, DEM height error can result in distortions and ortho shift. Using satellite images taken from an off-nadir angle and adjusting them to be from an ortho perspective in particular creates building lean effects and other unwanted artifacts. By accounting for a DEM error, which is to say a difference in DEM height and GCP height, satellite images can be used to make images with improved ortho accuracy.
G01S 13/90 - Radar ou systèmes analogues, spécialement adaptés pour des applications spécifiques pour la cartographie ou la représentation utilisant des techniques d'antenne synthétique
Plate tectonic movements can cause drift in the position of common points used in generating maps, such as ground control points and tie points. Over time, if not corrected for, plate tectonic movement results in older images no longer accurately reflecting the ground position of the imaged features. Furthermore, plate tectonic movements are not uniform across the world and can vary significantly in direction and velocity from region to region across the globe, or between images, or even within a single image. Several methods for accounting for these movements are described herein that can use images at different times to more accurately indicate current ground coordinates by applying plate tectonic movement information.
G06V 10/766 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la régression, p. ex. en projetant les caractéristiques sur des hyperplans
Methods and systems for avoiding buildings and other structures using semantic segmentation, the method including obtaining a plurality of image strips of a geographic location, identifying one or more structures that are present in one or more image strips, and generating one or more seamlines between adjacent image strips using a machine learning model. When two or more adjacent image strips include one or more structures, the method includes circumventing the one or more structures with the generated seamlines therebetween, wherein the generated one or more seamlines form a visual connection between the adjacent image strips.
G06V 10/26 - Segmentation de formes dans le champ d’imageDécoupage ou fusion d’éléments d’image visant à établir la région de motif, p. ex. techniques de regroupementDétection d’occlusion
G06T 3/4038 - Création de mosaïques d’images, p. ex. composition d’images planes à partir de sous-images planes
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 10/74 - Appariement de motifs d’image ou de vidéoMesures de proximité dans les espaces de caractéristiques
Examples of the present disclosure include a method of collecting a non-earth image of an object in space, the method including identifying a first orbital direction of the object in space, identifying a second orbital direction of an image capture device, determining a line of apparent motion between the object in space and the image capture device based on the identified first orbital direction and the identified second orbital direction, determining a residual motion across the image capture device, assessing an encounter window between the image capture device and the object in space, aligning sensors of the image capture device along the line of apparent motion, and when the object in space and the image capture device are within the encounter window, scanning at least the object in space by the image capture device along the line of apparent motion.
Examples of the present disclosure include a method for approximating a non-earth imaging camera model for a non-earth sensor includes obtaining a physical camera model, receiving a target range value, determining a plurality of space-based coordinates of a plurality of points about a line of sight of the sensor in proximity to a distance corresponding to the received target range value, for each of the plurality of locations, determining line and sample coordinates on an image thereof via the physical camera model based on the space-based coordinates of the plurality of points, and based on the determined line and sample coordinates for each of the plurality of points, fitting an approximate camera model thereto.
Methods, systems and computer program products are provided for generating shape representations corresponding to features in an image involving receiving image data, a 2-dimensional array of coordinates representing edges of one or more features in the image, and a list of path descriptions indicating the connectivity of points in the 2-D array to form a preliminary skeleton; interpolating orientation coefficients across the entire image using the extracted image line data and preliminary skeleton line data, thereby generating a frame field; and feeding the generated frame field to an optimization processor.
G06T 5/20 - Amélioration ou restauration d'image utilisant des opérateurs locaux
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
Extraction of depth from satellite images using neural radiance fields may be provided. Satellite image data may be received. Then a plurality of nadir rays, each with a plurality of points, in a first space may be constructed. A Machine Learning (ML) model may then be used to map the plurality of points along the plurality of nadir rays in the first space to a second space. Then the plurality of points along the plurality of nadir rays in the second space may be rendered into images and depths.
A system for broad area geospatial object detection includes a processor configured to retrieve training data including a first plurality of orthorectified geospatial training images each including at least one labeled instance of the object of interest, and a second plurality of orthorectified geospatial images each including at least one labeled instance of the object of interest and/or at least one unlabeled instance of the object of interest, and apply at least one type of image correction to the training data. The processor is also configured to train a plurality of machine learning classifier elements, based on the first plurality of orthorectified geospatial training images and subsequently based on the second plurality of orthorectified geospatial images, each of the plurality of machine learning classifier elements being defined by a machine learning protocol parameterized based on one or more visually unique features of the object of interest.
G06F 18/21 - Conception ou mise en place de systèmes ou de techniquesExtraction de caractéristiques dans l'espace des caractéristiquesSéparation aveugle de sources
G06F 18/214 - Génération de motifs d'entraînementProcédés de Bootstrapping, p. ex. ”bagging” ou ”boosting”
G06V 10/50 - Extraction de caractéristiques d’images ou de vidéos en effectuant des opérations dans des blocs d’imagesExtraction de caractéristiques d’images ou de vidéos en utilisant des histogrammes, p. ex. l’histogramme de gradient orienté [HoG]Extraction de caractéristiques d’images ou de vidéos en utilisant l’addition des valeurs d’intensité d’imageAnalyse de projection
G06V 10/774 - Génération d'ensembles de motifs de formationTraitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source méthodes de Bootstrap, p. ex. "bagging” ou “boosting”
G06V 10/776 - ValidationÉvaluation des performances
High Resolution Land Cover (HRLC) may be provided. A first high resolution image of a scene may be received and a plurality of second medium resolution images of the scene taken over a period of time may be received. A segmentation of the first image may then be performed. Next, a plurality of transformations may be performed on the plurality of second images. Then a temporal stack layer for a plurality of temporal statistics for each of the plurality of transformations on the plurality of second images may be created. The temporal stack layer may then be intersected with the segmented first image using a plurality of spatial statistics to produce a plurality of spatio-temporal layers. A Machine Learning (ML) model may be used on the spatio-temporal layers to classify pixels in the first image into respective ones of a plurality of classifications.
G06V 10/77 - Traitement des caractéristiques d’images ou de vidéos dans les espaces de caractéristiquesDispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant l’intégration et la réduction de données, p. ex. analyse en composantes principales [PCA] ou analyse en composantes indépendantes [ ICA] ou cartes auto-organisatrices [SOM]Séparation aveugle de source
G06T 7/35 - Détermination des paramètres de transformation pour l'alignement des images, c.-à-d. recalage des images utilisant des procédés statistiques
G06V 10/764 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos utilisant la reconnaissance de formes ou l’apprentissage automatique utilisant la classification, p. ex. des objets vidéo
Systems and methods for large-scale geospatial mosaic generation with image processing in a cloud computing environment. The approaches described herein specifically leverage scalable cloud computing features to facilitate highly parallel, granular image processing. Front-end image processing techniques allow for generation of a user interface that may provide automated material selection with human operator refinement. The user interface may be a web-based design that provides browse version images at lower resolutions to improve performance of the user interface and generating mosaic recipe. In turn, the mosaic recipe may be provided to back-end image processing that coordinates strip-level jobs and tile-level jobs and highly parallel fashion scalable cloud computing nodes. In turn, very large-scale mosaic images may be generated from geospatial images in computationally and cost-effective manner.
G06T 3/4038 - Création de mosaïques d’images, p. ex. composition d’images planes à partir de sous-images planes
G06N 7/01 - Modèles graphiques probabilistes, p. ex. réseaux probabilistes
G06T 7/187 - DécoupageDétection de bords impliquant des croissances de zonesDécoupageDétection de bords impliquant des fusions de zonesDécoupageDétection de bords impliquant un étiquetage de composantes connexes
G06T 7/207 - Analyse du mouvement pour l’estimation de mouvement sur une hiérarchie des résolutions
Space environment characterization may be provided. First, an area of sky may be selected. Then an image of the area of sky may be captured. Next, a plurality of image objects in the image may be identified. A plurality of uncharacterized objects within the plurality of image objects may then be identified.
G06V 10/60 - Extraction de caractéristiques d’images ou de vidéos relative aux propriétés luminescentes, p. ex. utilisant un modèle de réflectance ou d’éclairage
This disclosure presents a novel method and system for remotely identifying physical locations that meet specific criteria. The method involves obtaining source input data and satellite imagery, which are pre-processed and normalized to form a digital representation of the Earth. Three-dimensional and mosaic data are then produced, corresponding to the digital earth, and geospatial analysis is performed based on the defined criteria. The analysis includes object recognition and the application of machine learning algorithms to identify features of interest. The method further involves identifying candidate locations that meet the criteria and optionally monitoring them through satellite tasking. A computer system and non-transitory storage medium are also described, incorporating artificial intelligence algorithms and spatial statistics visualization to facilitate the identification and monitoring processes. This innovative approach offers a comprehensive and efficient means of remotely identifying and assessing potential locations for various applications such as site selection, monitoring, and decision-making.
Systems and methods for detecting vehicle behavior patterns in real-time. One embodiment is a method of continuous vehicle behavior detection. The method includes receiving a vehicle behavior profile including one or more travel patterns that define a vehicle behavior, receiving track data of one or more vehicles, hashing the track data as it is received to generate hash values that uniquely identify cells that approximate locations of the one or more vehicles, and storing the hash values in a hash library. The method also includes analyzing the hash library as the hash values are stored to compare the cells with the one or more travel patterns in the vehicle behavior profile, and in response to determining a group of the cells match the one or more travel patterns, generating a message indicating the vehicle behavior is detected.
Systems and methods for detecting vehicle behavior patterns in real-time. One embodiment is a method of continuous vehicle behavior detection. The method includes receiving a vehicle behavior profile including one or more travel patterns that define a vehicle behavior, receiving track data of one or more vehicles, hashing the track data as it is received to generate hash values that uniquely identify cells that approximate locations of the one or more vehicles, and storing the hash values in a hash library. The method also includes analyzing the hash library as the hash values are stored to compare the cells with the one or more travel patterns in the vehicle behavior profile, and in response to determining a group of the cells match the one or more travel patterns, generating a message indicating the vehicle behavior is detected.
Onboard geolocation of target pixels in images captured by a spacecraft is disclosed. By performing the geolocation onboard the spacecraft considerable time is saved. The geolocation may be based on timestamped ephemeris data and timestamped attitude data of the spacecraft. In one aspect, initial geolocation data is refined based on a digital construct. In one aspect, the accuracy of the ephemeris data and/or the attitude data are improved based on refinements made to the initial geolocation data. For example, corrections may be made to attitude and/or orbit filter parameters based on the refinements made to the initial geolocation data. In one aspect, the accuracy of the attitude data and/or the ephemeris data may be improved by the use of state information. The state information may be determined at a ground computing station based on information not available to the spacecraft such as Ground Control Points and/or Star Control Points.
G01S 19/48 - Détermination de position en combinant ou en commutant entre les solutions de position dérivées du système de positionnement par satellite à radiophares et les solutions de position dérivées d'un autre système
G01S 19/05 - Éléments coopérantsInteraction ou communication entre les différents éléments coopérants ou entre les éléments coopérants et les récepteurs fournissant des données d'assistance
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
H04N 7/18 - Systèmes de télévision en circuit fermé [CCTV], c.-à-d. systèmes dans lesquels le signal vidéo n'est pas diffusé
A method is provided for estimating velocity of a moving object. The method includes generating a raw velocity for the moving object from a position change between a first image generated by a first sensor and a second image generated by a second sensor of a sensor assembly and selecting a plurality of stationary background features that are captured with the moving object in one or more images. A background misregistration value is generated from apparent movement of the plurality of stationary background features and is used to correct the raw velocity.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06T 7/246 - Analyse du mouvement utilisant des procédés basés sur les caractéristiques, p. ex. le suivi des coins ou des segments
G06T 7/30 - Détermination des paramètres de transformation pour l'alignement des images, c.-à-d. recalage des images
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
G06V 20/58 - Reconnaissance d’objets en mouvement ou d’obstacles, p. ex. véhicules ou piétonsReconnaissance des objets de la circulation, p. ex. signalisation routière, feux de signalisation ou routes
H04N 23/68 - Commande des caméras ou des modules de caméras pour une prise de vue stable de la scène, p. ex. en compensant les vibrations du boîtier de l'appareil photo
26.
Some automated and semi-automated tools for linear feature extraction in two and three dimensions
A system for vector extraction comprising a vector extraction engine stored and operating on a network-connected computing device that loads raster images from a database stored and operating on a network-connected computing device, identifies features in the raster images, and computes a vector based on the features, and methods for feature and vector extraction.
G06V 10/00 - Dispositions pour la reconnaissance ou la compréhension d’images ou de vidéos
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06F 18/40 - Dispositions logicielles spécialement adaptées à la reconnaissance des formes, p. ex. interfaces utilisateur ou boîtes à outils à cet effet
G06V 10/94 - Architectures logicielles ou matérielles spécialement adaptées à la compréhension d’images ou de vidéos
G06V 10/46 - Descripteurs pour la forme, descripteurs liés au contour ou aux points, p. ex. transformation de caractéristiques visuelles invariante à l’échelle [SIFT] ou sacs de mots [BoW]Caractéristiques régionales saillantes
Estimating absolute geospatial accuracy in input images without the use of surveyed control points is disclosed. For example, the absolute geospatial accuracy of a satellite images may be estimated without the use of control points (GCPs). The absolute geospatial accuracy of the input images may be estimated based on a statistical measure of relative accuracies between pairs of overlapping images. The estimation of the absolute geospatial accuracy may include determining a root mean square error of the relative accuracies between pairs of overlapping images. For example, the absolute geospatial accuracy of the input images may be estimated by determining a root mean square error of the shears of respective pairs of overlapping images. The estimated absolute geospatial accuracy may be used to curate GCPs, evaluate a digital elevation map, generate a heatmap, or determine whether the adjust the images until a target absolute geospatial accuracy is met.
A set of input images from satellites (or other remote sensors) can be orthorectified and stitched together to create a mosaic. If the resulting mosaic is not of suitable quality, the input images can be adjusted and the processes of orthorectifying and creating the mosaic can be repeated. However, orthorectifying and creating the mosaic uses a large amount of computational resources and takes a lot of time. Therefore, performing numerous iterations is expensive and sometimes not practical. To overcome these issues, it is proposed to generate an indication of accuracy of the resulting mosaic prior to orthorectifying and creating the mosaic by accessing a set of points in the plurality of input images, projecting the points to a model, determining residuals for the projected points, and generating the indication of accuracy of the orthorectified mosaic based on the determined residuals.
G06T 3/40 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement
G06T 7/33 - Détermination des paramètres de transformation pour l'alignement des images, c.-à-d. recalage des images utilisant des procédés basés sur les caractéristiques
29.
Automated process for building material detection in remotely sensed imagery
A system and method for automatically (without human intervention) identifying a material in an image that comprises a building material for buildings in the image. Building side polygons which may be used to identify building sides in off-nadir imagery are generated. Off-nadir, multispectral images, building footprint data and elevation data for a geographic area are taken as input. Building heights for buildings in the geographic area are determined by clipping the elevation data using the building footprint data and then calculating building heights. A candidate set of polygons representing visible side faces of each building in the images is created from the known building heights, and based on the viewpoint direction, using vector analysis. After culling occluded polygons and polygons too small for analysis, the polygons are associated with a building footprint. Building materials for each building having visible polygons can then be identified.
An automated system is provided for classifying materials in remotely-sensed imagery based on automated construction of a dynamic classifier—namely, a classifier that is automatically trained on the same image to which it is then subsequently applied. A first automated process identifies high confidence exemplars of each class using tailored classification techniques. This data is then used to train a supervised classification model (e.g., discriminant analysis), and the resultant classifier is applied to other pixels in the image that are unclassified or uncertain. Dynamic classification is automatically customized to the current image and can yield a more accurate and efficient material classification versus a static (image-independent) or manually trained classifier. It can overcome various confounding factors including inconsistencies in radiometric calibration, atmospheric conditions, and atmospheric distortions of ground spectra; different viewing and illumination geometries; and regional variations in the composition of certain materials like asphalt and concrete.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
31.
Automated concrete/asphalt detection based on sensor time delay
Technology is provided for identifying concrete and/or asphalt (or other materials) in a multispectral satellite image that has multiple bands including a first set of bands from a first sensor and a second set of bands from a second sensor. The second sensor is at a different position on a focal plane as compared to the first sensor so that a single location depicted in the multispectral image will have been sensed at different times by the first sensor and the second sensor. The system identifies moving vehicles in the multispectral image and subsequently identifies sample pixels in the multispectral image that are near the moving vehicles. These pixels are high confidence samples of roads made of concrete and/or asphalt. Additional pixels are identified in the multispectral image having spectral characteristics that are within a threshold of spectral characteristics of the sample pixels. These additional pixels also depict concrete and/or asphalt.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
H04N 7/18 - Systèmes de télévision en circuit fermé [CCTV], c.-à-d. systèmes dans lesquels le signal vidéo n'est pas diffusé
H04L 67/12 - Protocoles spécialement adaptés aux environnements propriétaires ou de mise en réseau pour un usage spécial, p. ex. les réseaux médicaux, les réseaux de capteurs, les réseaux dans les véhicules ou les réseaux de mesure à distance
Described herein are methods and systems for detecting clouds in satellite imagery captured using first and second sensor arrays that are carried by a satellite and physically offset from one another on the satellite. Movement mask data is produced based first image data and the second image data, obtained, respectively, using the first and second sensor arrays carried by the satellite. Cloud mask data is produced based on spectral information included one of the first and second image data. Cloud detection data is produced based on the movement mask data and the cloud mask data, the cloud detection data indicating where it is likely, based on both the movement mask data and the cloud mask data, that one or more clouds are represented within one of the first and second image data. The cloud detection data can be used in various ways to account for the clouds included within the satellite imagery.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06T 7/254 - Analyse du mouvement impliquant de la soustraction d’images
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
33.
Advanced cloud detection using neural networks and optimization techniques
Techniques for automatically determining, on a pixel by pixel basis, whether imagery includes ground images or is obscured by cloud cover. The techniques include training a Neural Network, making an initial determination of cloud or ground by using the Neural Network, and performing a max-flow, min-cut operation on the image to determine whether each pixel is a cloud or ground imagery.
G06V 10/50 - Extraction de caractéristiques d’images ou de vidéos en effectuant des opérations dans des blocs d’imagesExtraction de caractéristiques d’images ou de vidéos en utilisant des histogrammes, p. ex. l’histogramme de gradient orienté [HoG]Extraction de caractéristiques d’images ou de vidéos en utilisant l’addition des valeurs d’intensité d’imageAnalyse de projection
Systems and methods for large-scale geospatial mosaic generation with image processing in a cloud computing environment. The approaches described herein specifically leverage scalable cloud computing features to facilitate highly parallel, granular image processing. Front-end image processing techniques allow for generation of a user interface that may provide automated material selection with human operator refinement. The user interface may be a web-based design that provides browse version images at lower resolutions to improve performance of the user interface and generating mosaic recipe. In turn, the mosaic recipe may be provided to back-end image processing that coordinates strip-level jobs and tile-level jobs and highly parallel fashion scalable cloud computing nodes. In turn, very large-scale mosaic images may be generated from geospatial images in computationally and cost-effective manner.
G06T 3/4038 - Création de mosaïques d’images, p. ex. composition d’images planes à partir de sous-images planes
G06N 7/01 - Modèles graphiques probabilistes, p. ex. réseaux probabilistes
G06T 7/187 - DécoupageDétection de bords impliquant des croissances de zonesDécoupageDétection de bords impliquant des fusions de zonesDécoupageDétection de bords impliquant un étiquetage de composantes connexes
G06T 7/207 - Analyse du mouvement pour l’estimation de mouvement sur une hiérarchie des résolutions
35.
System for simplified generation of systems for broad area geospatial object detection
A system for simplified generation of systems for analysis of satellite images to geolocate one or more objects of interest. A plurality of training images labeled for a study object or objects with irrelevant features loaded into a preexisting feature identification subsystem causes automated generation of models for the study object. This model is used to parameterize pre-engineered machine learning elements that are running a preprogrammed machine learning protocol. Training images with the study are used to train object recognition filters. This filter is used to identify the study object in unanalyzed images. The system reports results in a requestor's preferred format.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
G06V 10/50 - Extraction de caractéristiques d’images ou de vidéos en effectuant des opérations dans des blocs d’imagesExtraction de caractéristiques d’images ou de vidéos en utilisant des histogrammes, p. ex. l’histogramme de gradient orienté [HoG]Extraction de caractéristiques d’images ou de vidéos en utilisant l’addition des valeurs d’intensité d’imageAnalyse de projection
A system for vector extraction comprising a vector extraction engine stored and operating on a network-connected computing device that loads raster images from a database stored and operating on a network-connected computing device, identifies features in the raster images, and computes a vector based on the features, and methods for feature and vector extraction.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06V 10/44 - Extraction de caractéristiques locales par analyse des parties du motif, p. ex. par détection d’arêtes, de contours, de boucles, d’angles, de barres ou d’intersectionsAnalyse de connectivité, p. ex. de composantes connectées
G06V 10/46 - Descripteurs pour la forme, descripteurs liés au contour ou aux points, p. ex. transformation de caractéristiques visuelles invariante à l’échelle [SIFT] ou sacs de mots [BoW]Caractéristiques régionales saillantes
Techniques for improving the quality of images captured by a remote sensing overhead platform such as a satellite. Sensor shifting is employed in an open-loop fashion to compensate for relative motion of the remote sensing overhead platform to the Earth. Control signals are generated for the sensor shift mechanism by an orbital motion compensation calculation that uses the predicted ephemeris (including orbit dynamics) and image geometry (overhead platform to target). Optionally, the calculation may use attitude and rate errors that are determined from on-board sensors.
H04N 5/232 - Dispositifs pour la commande des caméras de télévision, p.ex. commande à distance
B64G 1/10 - Satellites artificielsSystèmes de tels satellitesVéhicules interplanétaires
B64G 1/36 - Appareils de guidage ou de commande, p. ex. de commande d'assiette par des capteurs, p. ex. par des capteurs solaires, des capteurs d'horizon
H04N 3/14 - Détails des dispositifs de balayage des systèmes de télévisionLeur combinaison avec la production des tensions d'alimentation par des moyens non exclusivement optiques-mécaniques au moyen de dispositifs à l'état solide à balayage électronique
G01C 11/02 - Dispositions de prises de vues spécialement adaptées pour la photogrammétrie ou les levers photographiques, p. ex. pour commander le recouvrement des photos
G01S 3/786 - Systèmes pour déterminer une direction ou une déviation par rapport à une direction prédéterminée utilisant le réglage d'une orientation des caractéristiques de directivité d'un détecteur ou d'un système de détecteurs afin d'obtenir une valeur désirée du signal provenant de ce détecteur ou de ce système de détecteurs la valeur désirée étant maintenue automatiquement
G01C 11/06 - Restitution des photos par comparaison de plusieurs photos de la même zone
H04N 5/341 - Extraction de données de pixels provenant d'un capteur d'images en agissant sur les circuits de balayage, p.ex. en modifiant le nombre de pixels ayant été échantillonnés ou à échantillonner
A system for incorporating geographical data into a map-related system, adding objects being tracked while moving within a specific geographic area and automatically analyzing their movement characteristics; wherein the tracking is done by visual means from a sky-borne platform.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06F 16/29 - Bases de données d’informations géographiques
G06F 16/56 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet de données d’images fixes en format vectoriel
G06F 16/58 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
G06K 9/32 - Alignement ou centrage du capteur d'image ou de la zone image
39.
Broad area geospatial object detection using autogenerated deep learning models
A system for automated geospatial image analysis comprising a deep learning model that receives orthorectified geospatial images, pre-labeled to demarcate objects of interest. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to a convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives orthorectified geospatial images from one or more geospatial image caches. Using a multi-scale sliding window submodule, image analysis software scans geospatial images, detects objects present and geospatially locates them.
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06T 7/33 - Détermination des paramètres de transformation pour l'alignement des images, c.-à-d. recalage des images utilisant des procédés basés sur les caractéristiques
40.
System for simplified generation of systems for broad area geospatial object detection
A system for simplified generation of systems for analysis of satellite images to geolocate one or more objects of interest. A plurality of training images labeled for a study object or objects with irrelevant features loaded into a preexisting feature identification subsystem causes automated generation of models for the study object. This model is used to parameterize pre-engineered machine learning elements that are running a preprogrammed machine learning protocol. Training images with the study are used to train object recognition filters. This filter is used to identify the study object in unanalyzed images. The system reports results in a requestor's preferred format.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
A system for large-scale crowd sourcing of map data cleanup and correction, comprising an application server that generates image data, sends image data to a user device, receives tagging data provided by the device user, and provides tags to a crowdsourced search and locate server based on tagging data from a user device, a crowdsourced search and locate server that receives tags from an application server, computes agreement and disagreement values and performs expectation-maximization analysis, and a map data server that stores and provides map data, and a method for estimating location and quality of a set of geolocation data.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail
G06F 16/9537 - Recherche à dépendance spatiale ou temporelle, p. ex. requêtes spatio-temporelles
G06F 16/58 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
G06F 16/29 - Bases de données d’informations géographiques
42.
Automated registration of three-dimensional vectors to three-dimensional linear features in remotely-sensed data
A system for advanced vector editing, comprising a routing calculation server and a rendering engine. The routing calculation server calculates a radius around a visual cursor associated with a raster image; determines a three-dimensional location at the cursor from the raster image; determines a three-dimensional path using epipolar geometry; and recalculates a vector path through the cursor location according to the three-dimensional path.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06F 3/0481 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p. ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comportement ou d’aspect
A system for vector extraction comprising a vector extraction engine stored and operating on a network-connected computing device that loads raster images from a database stored and operating on a network-connected computing device, identifies features in the raster images, and computes a vector based on the features, and methods for feature and vector extraction.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/48 - Extraction d'éléments ou de caractéristiques de l'image en codant le contour de la forme
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
A system for broad area geospatial object recognition, identification, classification, location and quantification, comprising an image manipulation module to create synthetically-generated images to imitate and augment an existing quantity of orthorectified geospatial images; together with a deep learning module and a convolutional neural network serving as an image analysis module, to analyze a large corpus of orthorectified geospatial images, identify and demarcate a searched object of interest from within the corpus, locate and quantify the identified or classified objects from the corpus of geospatial imagery available to the system. The system reports results in a requestor's preferred format.
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
A system for incorporating geographical data into a map-related system, adding objects being tracked while moving within a specific geographic area and automatically analyzing their movement characteristics; wherein the tracking is done by visual means from a sky-borne platform.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06F 16/58 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
G06F 16/56 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet de données d’images fixes en format vectoriel
G06F 16/29 - Bases de données d’informations géographiques
G06K 9/32 - Alignement ou centrage du capteur d'image ou de la zone image
Techniques for improving the quality of images captured by a remote sensing overhead platform such as a satellite. Sensor shifting is employed in an open-loop fashion to compensate for relative motion of the remote sensing overhead platform to the Earth. Control signals are generated for the sensor shift mechanism by an orbital motion compensation calculation that uses the predicted ephemeris (including orbit dynamics) and image geometry (overhead platform to target). Optionally, the calculation may use attitude and rate errors that are determined from on-board sensors.
H04N 3/14 - Détails des dispositifs de balayage des systèmes de télévisionLeur combinaison avec la production des tensions d'alimentation par des moyens non exclusivement optiques-mécaniques au moyen de dispositifs à l'état solide à balayage électronique
B64G 1/10 - Satellites artificielsSystèmes de tels satellitesVéhicules interplanétaires
B64G 1/36 - Appareils de guidage ou de commande, p. ex. de commande d'assiette par des capteurs, p. ex. par des capteurs solaires, des capteurs d'horizon
G01C 11/02 - Dispositions de prises de vues spécialement adaptées pour la photogrammétrie ou les levers photographiques, p. ex. pour commander le recouvrement des photos
G01S 3/786 - Systèmes pour déterminer une direction ou une déviation par rapport à une direction prédéterminée utilisant le réglage d'une orientation des caractéristiques de directivité d'un détecteur ou d'un système de détecteurs afin d'obtenir une valeur désirée du signal provenant de ce détecteur ou de ce système de détecteurs la valeur désirée étant maintenue automatiquement
G01C 11/06 - Restitution des photos par comparaison de plusieurs photos de la même zone
H04N 5/341 - Extraction de données de pixels provenant d'un capteur d'images en agissant sur les circuits de balayage, p.ex. en modifiant le nombre de pixels ayant été échantillonnés ou à échantillonner
Techniques for atmospheric compensation in satellite imagery that include converting an image including an array of radiance values to an array of surface reflectance values. The conversion is performed in an automated fashion by identifying one or more portions of the image for which the surface reflectance can be estimated and determining the Aerosol Optical Depth (AOD) by iteratively comparing the radiance value captured by the image sensor to a calculated radiance value (based on the known surface reflectance, historical values for other atmospheric parameters, and the AOD) and adjusting the AOD until the calculated radiance value is substantially the same as the captured radiance value.
A system for automated geospatial image analysis comprising a deep learning model that receives orthorectified geospatial images, pre-labeled to demarcate objects of interest. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to a convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives orthorectified geospatial images from one or more geospatial image caches. Using a multi-scale sliding window submodule, image analysis software scans geospatial images, detects objects present and geospatially locates them.
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
G06T 7/33 - Détermination des paramètres de transformation pour l'alignement des images, c.-à-d. recalage des images utilisant des procédés basés sur les caractéristiques
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
Techniques for automatically determining, on a pixel by pixel basis, whether imagery includes ground images or is obscured by cloud cover. The techniques include training a Neural Network, making an initial determination of cloud or ground by using the Neural Network, and performing a max-flow, min-cut operation on the image to determine whether each pixel is a cloud or ground imagery.
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06T 7/162 - DécoupageDétection de bords impliquant des procédés basés sur des graphes
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
A system for semi-automated feature extraction comprising an image analysis server that receives and initializes a plurality of raster images, a feature extraction server that identifies and extracts image features, a mosaic server that assembles mosaics from multiple images, and a rendering engine that provides visual representations of images for review by a human user, and a method for generating a cost raster utilizing the system of the invention.
G06T 3/40 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement
G06F 16/56 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet de données d’images fixes en format vectoriel
G06F 16/532 - Formulation de requêtes, p. ex. de requêtes graphiques
G06F 16/583 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
51.
Shape-based segmentation using hierarchical image representations for automatic training data generation and search space specification for machine learning algorithms
A system and various methods for processing an image to produce a hierarchical image representation model, segment the image model using shape criteria to produce positive and negative training data sets as well as a search-space data set comprising shapes matched to a search query provided as input, and using the training data sets to train a machine learning model to improve recognition of shapes that are similar to an input query without being exact matches, to improve object recognition.
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06T 7/90 - Détermination de caractéristiques de couleur
G06F 16/583 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
A system and methods for muddy water detection using normalized semantic layers, wherein a spectrum analyzer isolates spectrum bands within an image to produce a set of three normalized differential index images from which a composite color image is created, from which a power band is computed, from which a two-color image is produced, and then filters image components within the two-color representation based on defined criteria.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06T 7/90 - Détermination de caractéristiques de couleur
G06F 16/583 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
A system for simplified generation of systems for analysis of satellite images to geolocate one or more objects of interest. A plurality of training images labeled for a study object or objects with irrelevant features loaded into a preexisting feature identification subsystem causes automated generation of models for the study object. This model is used to parameterize pre-engineered machine learning elements that are running a preprogrammed machine learning protocol. Training images with the study are used to train object recognition filters. This filter is used to identify the study object in unanalyzed images. The system reports results in a requestor's preferred format.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
A system for broad area geospatial object recognition, identification, classification, location and quantification, comprising an image manipulation module to create synthetically-generated images to imitate and augment an existing quantity of orthorectified geospatial images; together with a deep learning module and a convolutional neural network serving as an image analysis module, to analyze a large corpus of orthorectified geospatial images, identify and demarcate a searched object of interest from within the corpus, locate and quantify the identified or classified objects from the corpus of geospatial imagery available to the system. The system reports results in a requestor's preferred format.
G06N 3/04 - Architecture, p. ex. topologie d'interconnexion
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06T 7/90 - Détermination de caractéristiques de couleur
Techniques for automatically determining, on a pixel by pixel basis, whether imagery includes ground images or is obscured by cloud cover. The techniques include training a cloud dictionary and a ground dictionary, determining whether a given pixel is best represented by “words” from the cloud dictionary or “words” from the ground dictionary to make an initial determination of cloud or ground, and performing a max-flow, min-cut operation on the image to determine whether each pixel is a cloud or ground imagery.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
A system for vector extraction comprising a vector extraction engine stored and operating on a network-connected computing device that loads raster images from a database stored and operating on a network-connected computing device, identifies features in the raster images, and computes a vector based on the features, and methods for feature and vector extraction.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/48 - Extraction d'éléments ou de caractéristiques de l'image en codant le contour de la forme
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
Techniques for improving image co-registration. One aspect relates to improving Mutual Information techniques by making the histogram bin widths used therein depend on the amount of signal noise. Another aspect relates to populating the bins by integrating the signal probability in each bin. A third aspect relates to converting top-of-the-atmosphere image data to surface reflectance data, and then using surface reflectance data in the Mutual Information technique for determining a correlation surface. The fourth aspect relates to registering higher-resolution images with lower-resolution images by down-sampling the higher-resolution image. The last aspect relates to a technique for determining the accuracy of the co-registration by synthesizing a perfectly-registered image in a second wavelength band from an image in a first wavelength band and then using Mutual Information between the two images to create a correlation surface.
G06K 9/32 - Alignement ou centrage du capteur d'image ou de la zone image
G06T 7/32 - Détermination des paramètres de transformation pour l'alignement des images, c.-à-d. recalage des images utilisant des procédés basés sur la corrélation
G06T 7/207 - Analyse du mouvement pour l’estimation de mouvement sur une hiérarchie des résolutions
G06T 7/35 - Détermination des paramètres de transformation pour l'alignement des images, c.-à-d. recalage des images utilisant des procédés statistiques
58.
System for simplified generation of systems for broad area geospatial object detection
A system for simplified generation of systems for analysis of satellite images to geolocate one or more objects of interest. A plurality of training images labeled for a study object or objects with irrelevant features loaded into a preexisting feature identification subsystem causes automated generation of models for the study object. This model is used to parameterize pre-engineered machine learning elements that are running a preprogrammed machine learning protocol. Training images with the study are used to train object recognition filters. This filter is used to identify the study object in unanalyzed images. The system reports results in a requestor's preferred format.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
G06T 7/73 - Détermination de la position ou de l'orientation des objets ou des caméras utilisant des procédés basés sur les caractéristiques
A system for automated conversion of two-dimensional hydrology vector models into valid three-dimensional hydrology vector models, comprising a vector extraction engine that retrieves vectors from, and sends vectors to, a vector storage, a DSM server that retrieves a DSM from a DSM storage and computes a DSM from stereo disparity measurements of a stereo pair retrieved from a raster storage, and a rendering engine that provides visual representations of images for review by a human user, and a method for automated hydrology vector model development utilizing the system of the invention.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
A system for automated tonal balancing, comprising a rectification server that groups and processes images for use in tone-matching and provides them to a tone-matching server, that then performs tone-matching operations on the images and provides them as output for review or storage, and methods for tonal balancing using the system of the invention.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
A system for automated geospatial image analysis comprising a deep learning model module and a convolutional neural network serving as an automated image analysis software module. The deep learning module receives a plurality of orthorectified geospatial images, pre-labeled to demarcate objects of interest, and optimized for the purpose of training the neural network of the image analysis software module. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to the convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives a plurality of orthorectified geospatial images from one or more geospatial image caches. Using multi-scale sliding window submodule, image analysis modules scan geospatial images, detect objects present and locate them on the geographical latitude-longitude system. The system reports the results in the requestor's preferred format.
A system for modular image mining and search, comprising a plurality of image capture devices, a search engine, an indexing engine, a database, and user interface software. A plurality of image capture devices capture images and store them to a database. The indexing engine indexes images to create image data for each image with associated metadata and stores the information to the database. The interface software is used to request search queries via a search engine. The search engine accesses the image data to find images or parts of images that satisfy the search query.
G06F 16/00 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet
G06F 16/583 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement utilisant des métadonnées provenant automatiquement du contenu
G06F 16/51 - IndexationStructures de données à cet effetStructures de stockage
G06F 16/56 - Recherche d’informationsStructures de bases de données à cet effetStructures de systèmes de fichiers à cet effet de données d’images fixes en format vectoriel
G06F 16/9535 - Adaptation de la recherche basée sur les profils des utilisateurs et la personnalisation
A system for automated geospatial image analysis comprising a deep learning model module and a convolutional neural network serving as an automated image analysis software module. The deep learning module receives a plurality of orthorectified geospatial images, pre-labeled to demarcate objects of interest, and optimized for the purpose of training the neural network of the image analysis software module. The module presents marked geospatial images and a second set of unmarked, optimized, training geospatial images to the convolutional neural network. This process may be repeated so that an image analysis software module can detect multiple object types or categories. The image analysis software module receives a plurality of orthorectified geospatial images from one or more geospatial image caches. Using multi-scale sliding window submodule, image analysis modules scan geospatial images, detect objects present and locate them on the geographical latitude-longitude system. The system reports the results in the requestor's preferred format.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
A system for broad area geospatial object recognition, identification, classification, location and quantification, comprising an image manipulation module to create synthetically-generated images to imitate and augment an existing quantity of orthorectified geospatial images; together with a deep learning module and a convolutional neural network serving as an image analysis module, to analyze a large corpus of orthorectified geospatial images, identify and demarcate a searched object of interest from within the corpus, locate and quantify the identified or classified objects from the corpus of geospatial imagery available to the system. The system reports results in a requestor's preferred format.
A system for incorporating geographical data into a map-related system, adding objects being tracked while moving within a specific geographic area and automatically analyzing their movement characteristics; wherein the tracking is done by visual means from a sky-borne platform.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06F 17/30 - Recherche documentaire; Structures de bases de données à cet effet
G06K 9/32 - Alignement ou centrage du capteur d'image ou de la zone image
66.
Choreographing automated and manual processes in support of mosaic generation
A database for creating orthomosaics, where the database is tailored to retrieve the most recent imagery for each pixel, in an efficient manner that partitions the orthomosaic into sufficiently small chunks that can each be quickly processed.
G06F 17/30 - Recherche documentaire; Structures de bases de données à cet effet
H04N 5/232 - Dispositifs pour la commande des caméras de télévision, p.ex. commande à distance
G06T 3/40 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement
H04N 1/387 - Composition, repositionnement ou autre modification des originaux
H04N 5/77 - Circuits d'interface entre un appareil d'enregistrement et un autre appareil entre un appareil d'enregistrement et une caméra de télévision
A system for large-scale crowd sourcing of map data cleanup and correction, comprising an application server that generates image data, sends image data to a user device, receives tagging data provided by the device user, and provides tags to a crowdsourced search and locate server based on tagging data from a user device, a crowdsourced search and locate server that receives tags from an application server, computes agreement and disagreement values and performs expectation-maximization analysis, and a map data server that stores and provides map data, and a method for estimating location and quality of a set of geolocation data.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail
G06F 16/9537 - Recherche à dépendance spatiale ou temporelle, p. ex. requêtes spatio-temporelles
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
G06F 16/29 - Bases de données d’informations géographiques
G06F 16/58 - Recherche caractérisée par l’utilisation de métadonnées, p. ex. de métadonnées ne provenant pas du contenu ou de métadonnées générées manuellement
68.
Object detection with textural to spectral domain adaptation
Techniques for object detection include training a first classifier to detect an object based on textural features and a second classifier to detect the object based on textural features and spectral features. A classifier relationship between the two classifiers is learned and used with the first classifier to detect other objects. If desired, the performance of the object detector can be improved by comparing the results of detecting objects with the first classifier and the classifier relationship versus detecting objects with the first and second classifier together, and modifying the classifier relationship based upon the comparison.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06K 9/66 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques utilisant des comparaisons ou corrélations simultanées de signaux images avec une pluralité de références, p.ex. matrice de résistances avec des références réglables par une méthode adaptative, p.ex. en s'instruisant
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Computer modeling services, namely, evaluating algorithms for object detection from remotely sensed imagery of earth; providing a website featuring a database containing remotely sensed earth imagery with known training data provided in relation to the imagery for evaluation and testing of algorithms for object detection from the remotely sensed earth imagery; providing a website featuring a database of scientific information in the nature of remotely sensed earth imagery
70.
Automated geospatial image mosaic generation with multiple zoom level support
Generation of a plurality of orthomosaic image layers for a geographical area, wherein the layers comprise different spatial resolutions and are radiometrically normalized to facilitate improved radiometric consistency when zooming relative to the geographic area. In applications that facilitate zooming with respect to a geographic area imaged by the orthomosaic layers, radiometric normalization of the orthomosaic image layers may reduce radiometric discontinuities when zooming in, zooming out, and/or panning a displayed portion that is output to the user. The orthomosaic layers may be generated based on one or more orthomosaic image layer or may be developed independently using a source images. In any regard, the radiometric normalization may include generating a normalization function based on image metadata that is calculated and may be independent of the spatial resolution of the image.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
G06K 9/52 - Extraction d'éléments ou de caractéristiques de l'image en déduisant des propriétés mathématiques ou géométriques de l'image complète
G06T 3/40 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement
G09B 29/10 - Spots pour la lecture des cartes ou indicateurs de position par coordonnéesAides pour la lecture des cartes
A crowdsourced search and locate platform has been developed and put into practice. A plurality of geospatial images of a geographical region of interest are presented to a plurality of participants in an internet mediated crowdsourcing campaign for the purpose of identifying and tagging specific features of interest to the campaign administrator. An algorithm of the invention, CrowdRank, monitors the identification accuracy of the participants both absolute and compared to other participants calculating a score used to weight each identification made by that user. CrowdRank also chooses images to maximize campaign efficiency and calculates the confidence level of each feature identification.
Techniques for improving overhead image bathymetry include obtaining depth information from image data based on one or more of the spectral domain, the angular domain (e.g., stereo or photogrammetry), the temporal domain (e.g., monitoring the movement of waves in a body of water), or any other suitable domain, together with a priori information about the area of interest. These different pieces of depth information from the various different domains are combined together using any combination of Optimal Estimation and Continuity Constraints to improve the accuracy of the results.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G01C 13/00 - Géodésie spécialement adaptée à l'eau libre, p. ex. à la mer, aux lacs, aux rivières ou aux canaux
G01S 11/12 - Systèmes pour déterminer la distance ou la vitesse sans utiliser la réflexion ou la reradiation utilisant des ondes électromagnétiques autres que les ondes radio
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
G01F 23/00 - Indication ou mesure du niveau des liquides ou des matériaux solides fluents, p. ex. indication en fonction du volume ou indication au moyen d'un signal d'alarme
G06T 7/50 - Récupération de la profondeur ou de la forme
Techniques for atmospheric compensation in satellite imagery that include converting an image including an array of radiance values to an array of surface reflectance values. The conversion is performed in an automated fashion by identifying one or more portions of the image for which the surface reflectance can be estimated and determining the Aerosol Optical Depth (AOD) by iteratively comparing the radiance value captured by the image sensor to a calculated radiance value (based on the known surface reflectance, historical values for other atmospheric parameters, and the AOD) and adjusting the AOD until the calculated radiance value is substantially the same as the captured radiance value.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06T 5/20 - Amélioration ou restauration d'image utilisant des opérateurs locaux
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Providing a website featuring technology that allows access and manipulation of geospatial imagery and related data including viewing and saving data related to the geospatial imagery; Providing temporary use of online non-downloadable software tools for searching, filtering, annotating, bookmarking, alerting, and monitoring geospatial imagery
75.
Automated sliver removal in orthomosaic generation
Techniques for automatically removing slivers from orthomosaics. First, slivers may be identified, which may be based upon user-configurable characteristics such as width and length. Second, slivers may be replaced with portions of another image, such as an older image. Third, boundary slivers may optionally be removed. Fourth, remaining boundary or interior slivers can be inflated by expanding the sliver until it is no longer a sliver, which may include replacing a portion of an adjacent image portion with older imagery.
A system for automated vector updating, comprising a database that stores raster and vector information, and a vector processing server that algorithmically processes vectors for updates, and methods for algorithm-based vector updating.
G06F 3/0481 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] fondées sur des propriétés spécifiques de l’objet d’interaction affiché ou sur un environnement basé sur les métaphores, p. ex. interaction avec des éléments du bureau telles les fenêtres ou les icônes, ou avec l’aide d’un curseur changeant de comportement ou d’aspect
A Metric Information Network (MIN) with a plurality of Ground Control Points (GCPs) that are selected in an automated fashion. The GCP selection includes clustering algorithms as compared to prior art pair-wise matching algorithms. Further, the image processing that takes place in identifying interest points, clustering, and selecting tie points to be GCPs is all performed before the MIN is updated. By arranging for the processing to happen in this manner, the processing that is embarrassingly parallel (identifying interest points, clustering, and selecting tie points) can be performed in a distributed fashion across many computers and then the MIN can be updated.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G01C 11/06 - Restitution des photos par comparaison de plusieurs photos de la même zone
78.
Global-scale damage detection using satellite imagery
A system for performing global-scale damage detection using satellite imagery, comprising a damage detection server that receives and analyzes image data to identify objects within an image via a curated computational method, and a curation interface that enables a user to curate image information for use in object identification, and a method for a curated computational method for performing global scale damage detection.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
A system for performing global scale object detection using satellite imagery, comprising an object detection server that receives and analyzes image data to identify objects within an image via a curated computational method, and a curation interface that enables a user to curate image information for use in object identification, and a method for a curated computational method for performing global scale object detection.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
80.
Integrated architecture for near-real-time satellite imaging applications
A system for operating an integrated architecture for near real-time satellite communications, comprising a plurality of collector satellites augment by aerial and terrestrial sensing systems that capture a plurality of EO, SAR and/or Signals sensor data of a portion of a region of the surface of the Earth, and a plurality of connector satellites that communicate with at least a collector satellite using a communication interface, and a method for using advanced environmental monitoring to adaptively task a collector satellite.
H04B 10/118 - Dispositions spécifiques à la transmission en espace libre, c.-à-d. dans l’air ou le vide spécialement adaptées aux communications par satellite
81.
Advanced semi-automated vector editing in two and three dimensions
A system for advanced vector editing, comprising a vector analysis server that analyzes vector information, a routing calculation server that calculates routes from the vector information, and a rendering engine that produces visualizations from the routing information, and several methods for advanced vector editing in two- and three-dimensional visualizations.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
82.
Automated seamline construction for high-quality high-resolution orthomosaics
A system for semi-automated feature extraction comprising an image analysis server that receives and initializes a plurality of raster images, a feature extraction server that identifies and extracts image features, a mosaic server that assembles mosaics from multiple images, and a rendering engine that provides visual representations of images for review by a human user, and a method for generating a cost raster utilizing the system of the invention.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06T 3/40 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement
G06F 17/30 - Recherche documentaire; Structures de bases de données à cet effet
83.
Automated conversion of two-dimensional hydrology vector models into valid three-dimensional hydrology vector models
A system for automated conversion of two-dimensional hydrology vector models into valid three-dimensional hydrology vector models, comprising a vector extraction engine that retrieves vectors from, and sends vectors to, a vector storage, a DSM server that retrieves a DSM from a DSM storage and computes a DSM from stereo disparity measurements of a stereo pair retrieved from a raster storage, and a rendering engine that provides visual representations of images for review by a human user, and a method for automated hydrology vector model development utilizing the system of the invention.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
A system for large-scale crowd sourcing of map data cleanup and correction, comprising an application server that generates image data, sends image data to a user device, receives tagging data provided by the device user, and provides tags to a crowdsourced search and locate server based on tagging data from a user device, a crowdsourced search and locate server that receives tags from an application server, computes agreement and disagreement values and performs expectation-maximization analysis, and a map data server that stores and provides map data, and a method for estimating location and quality of a set of geolocation data.
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
G06Q 99/00 - Matière non couverte par les autres groupes de la présente sous-classe
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06F 17/30 - Recherche documentaire; Structures de bases de données à cet effet
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
85.
Spectral data analytic cube classifier (SPADACC) for continuous wide area geospatial modeling using normalized and highly dimensional multispectal raster data
A system for enhanced geospatial modeling using a spectral data analytic cube classifier and normalized multispectral raster data, comprising a geospatial modeling server that receives and analyzes input imagery, a data import/export server that provides data for review or interaction and receives data to provide to the analysis server, and a database that stores data, and a method for enhanced geospatial modeling using raster data according to the system of the invention.
Land classification based on analysis of image data. Feature extraction techniques may be used to generate a feature stack corresponding to the image data to be classified. A user may identify training data from the image data from which a classification model may be generated using one or more machine learning techniques applied to one or more features of the image. In this regard, the classification module may in turn be used to classify pixels from the image data other than the training data. Additionally, quantifiable metrics regarding the accuracy and/or precision of the models may be provided for model evaluation and/or comparison. Additionally, the generation of models may be performed in a distributed system such that model creation and/or application may be distributed in a multi-user environment for collaborative and/or iterative approaches.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06F 17/30 - Recherche documentaire; Structures de bases de données à cet effet
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
87.
Automated geospatial image mosaic generation with multiple zoom level support
Generation of a plurality of orthomosaic image layers for a geographical area, wherein the layers comprise different spatial resolutions and are radiometrically normalized to facilitate improved radiometric consistency when zooming relative to the geographic area. In applications that facilitate zooming with respect to a geographic area imaged by the orthomosaic layers, radiometric normalization of the orthomosaic image layers may reduce radiometric discontinuities when zooming in, zooming out, and/or panning a displayed portion that is output to the user. The orthomosaic layers may be generated based on one or more orthomosaic image layer or may be developed independently using a source images. In any regard, the radiometric normalization may include generating a normalization function based on image metadata that is calculated and may be independent of the spatial resolution of the image.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
G06K 9/52 - Extraction d'éléments ou de caractéristiques de l'image en déduisant des propriétés mathématiques ou géométriques de l'image complète
G06T 3/40 - Changement d'échelle d’images complètes ou de parties d’image, p. ex. agrandissement ou rétrécissement
88.
Automated and scalable object and feature extraction from imagery
Feature extraction of image data using feature extraction modules. The feature extraction modules may be provided in an architecture that allows for modular, decoupled generation and/or operation of the feature extraction modules to generate feature data corresponding to image data. In this regard, the feature extraction modules may communicate with a file system storing image data and feature data by way of a common interface format. Accordingly, regardless of the nature of the execution of the feature extraction module, each feature extraction module may be communicative by way of the common interface format, thereby providing a modular approach that is highly scalable, flexible, and adaptive.
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
G06K 9/66 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques utilisant des comparaisons ou corrélations simultanées de signaux images avec une pluralité de références, p.ex. matrice de résistances avec des références réglables par une méthode adaptative, p.ex. en s'instruisant
H04N 1/407 - Commande ou modification de la gradation des tons ou des niveaux extrêmes, p. ex. du niveau de fond
G06T 5/40 - Amélioration ou restauration d'image utilisant des techniques d'histogrammes
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
89.
Some automated and semi-automated tools for linear feature extraction in two and three dimensions
A system for vector extraction comprising a vector extraction engine stored and operating on a network-connected computing device that loads raster images from a database stored and operating on a network-connected computing device, identifies features in the raster images, and computes a vector based on the features, and methods for feature and vector extraction.
G06K 9/48 - Extraction d'éléments ou de caractéristiques de l'image en codant le contour de la forme
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
A crowdsourced search and locate platform, comprising an application server that receives input from a plurality of user devices and navigates to a particular location, sends images of the location to a user device, and receives tagging data provided by the device user, and a client interface application that displays a plurality of interactive elements to a user, receive input from the user, and provide the results of the input to the application server, and methods for operating and administering a crowdsourced search and locate platform.
G06Q 10/06 - Ressources, gestion de tâches, des ressources humaines ou de projetsPlanification d’entreprise ou d’organisationModélisation d’entreprise ou d’organisation
G06Q 30/02 - MarketingEstimation ou détermination des prixCollecte de fonds
G06Q 30/06 - Transactions d’achat, de vente ou de crédit-bail
G06Q 99/00 - Matière non couverte par les autres groupes de la présente sous-classe
G06F 17/30 - Recherche documentaire; Structures de bases de données à cet effet
G06F 3/0484 - Techniques d’interaction fondées sur les interfaces utilisateur graphiques [GUI] pour la commande de fonctions ou d’opérations spécifiques, p. ex. sélection ou transformation d’un objet, d’une image ou d’un élément de texte affiché, détermination d’une valeur de paramètre ou sélection d’une plage de valeurs
G06F 3/0482 - Interaction avec des listes d’éléments sélectionnables, p. ex. des menus
G06Q 50/00 - Technologies de l’information et de la communication [TIC] spécialement adaptées à la mise en œuvre des procédés d’affaires d’un secteur particulier d’activité économique, p. ex. aux services d’utilité publique ou au tourisme
42 - Services scientifiques, technologiques et industriels, recherche et conception
Produits et services
Management of an online community of registered users, namely, hosting and maintaining a website for an online community of registered users where users utilize the website to analyze and provide information regarding satellite images of the Earth; Creating an online community of registered users for providing incentives for users to participate in the online community in order to utilize the website to analyze and provide information regarding satellite images of the Earth
A system for automated tonal balancing, comprising a rectification server that groups and processes images for use in tone-matching and provides them to a tone-matching server, that then performs tone-matching operations on the images and provides them as output for review or storage, and methods for tonal balancing using the system of the invention.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
Techniques for automatically removing slivers from orthomosaics. First, slivers may be identified, which may be based upon user-configurable characteristics such as width and length. Second, slivers may be replaced with portions of another image, such as an older image. Third, boundary slivers may optionally be removed. Fourth, remaining boundary or interior slivers can be inflated by expanding the sliver until it is no longer a sliver, which may include replacing a portion of an adjacent image portion with older imagery.
Automatic generation of a mosaic comprising a plurality of geospatial images. An embodiment of the automatic mosaic generation may include automated source image selection that includes comparison of source images to base layer image to determine radiometric similar source images. Additionally, an embodiment of an automatic cutline generator may be provided to automatically determine a cutline when merging two images such that radiometric differences between the images along the cutline are reduced. In this regard, less perceivable outlines may be provided. Further still, an embodiment of a radiometric normalization module may be provided that may determine radiometric adjustments to source images to match certain properties of the base layer image. In some embodiments, when processing source images, the source images may be downsampled during a portion of the processing to reduce computational overhead. Additionally, some highly parallel computations may be performed by a GPU to further enhance performance.
Techniques for improving overhead image bathymetry include obtaining depth information from image data based on one or more of the spectral domain, the angular domain (e.g., stereo or photogrammetry), the temporal domain (e.g., monitoring the movement of waves in a body of water), or any other suitable domain, together with a priori information about the area of interest. These different pieces of depth information from the various different domains are combined together using any combination of Optimal Estimation and Continuity Constraints to improve the accuracy of the results.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G01C 13/00 - Géodésie spécialement adaptée à l'eau libre, p. ex. à la mer, aux lacs, aux rivières ou aux canaux
G01S 11/12 - Systèmes pour déterminer la distance ou la vitesse sans utiliser la réflexion ou la reradiation utilisant des ondes électromagnétiques autres que les ondes radio
G06T 11/60 - Édition de figures et de texteCombinaison de figures ou de texte
G01F 23/00 - Indication ou mesure du niveau des liquides ou des matériaux solides fluents, p. ex. indication en fonction du volume ou indication au moyen d'un signal d'alarme
96.
Automated and scalable object and feature extraction from imagery
Feature extraction of image data using feature extraction modules. The feature extraction modules may be provided in an architecture that allows for modular, decoupled generation and/or operation of the feature extraction modules to generate feature data corresponding to image data. In this regard, the feature extraction modules may communicate with a file system storing image data and feature data by way of a common interface format. Accordingly, regardless of the nature of the execution of the feature extraction module, each feature extraction module may be communicative by way of the common interface format, thereby providing a modular approach that is highly scalable, flexible, and adaptive.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
H04N 1/407 - Commande ou modification de la gradation des tons ou des niveaux extrêmes, p. ex. du niveau de fond
G06T 5/40 - Amélioration ou restauration d'image utilisant des techniques d'histogrammes
97.
Classification of land based on analysis of remotely-sensed earth images
Land classification based on analysis of image data. Feature extraction techniques may be used to generate a feature stack corresponding to the image data to be classified. A user may identify training data from the image data from which a classification model may be generated using one or more machine learning techniques applied to one or more features of the image. In this regard, the classification module may in turn be used to classify pixels from the image data other than the training data. Additionally, quantifiable metrics regarding the accuracy and/or precision of the models may be provided for model evaluation and/or comparison. Additionally, the generation of models may be performed in a distributed system such that model creation and/or application may be distributed in a multi-user environment for collaborative and/or iterative approaches.
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G06K 9/62 - Méthodes ou dispositions pour la reconnaissance utilisant des moyens électroniques
G06F 17/30 - Recherche documentaire; Structures de bases de données à cet effet
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
A system for oil storage tank monitoring, comprising an extraction module and an analysis module, wherein an extraction module is utilized to determine information from an oil storage tank image and an analysis module is utilized to perform operations on extracted information, such as for determining measurements or values of oil storage tanks.
G06K 9/46 - Extraction d'éléments ou de caractéristiques de l'image
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
Automatic generation of a mosaic comprising a plurality of geospatial images. An embodiment of the automatic mosaic generation may include automated source image selection that includes comparison of source images to base layer image to determine radiometric similar source images. Additionally, an embodiment of an automatic cutline generator may be provided to automatically determine a cutline when merging two images such that radiometric differences between the images along the cutline are reduced. In this regard, less perceivable outlines may be provided. Further still, an embodiment of a radiometric normalization module may be provided that may determine radiometric adjustments to source images to match certain properties of the base layer image. In some embodiments, when processing source images, the source images may be downsampled during a portion of the processing to reduce computational overhead. Additionally, some highly parallel computations may be performed by a GPU to further enhance performance.
G06K 9/36 - Prétraitement de l'image, c. à d. traitement de l'information image sans se préoccuper de l'identité de l'image
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G09G 5/377 - Détails concernant le traitement de dessins graphiques pour mélanger ou superposer plusieurs dessins graphiques
Automatic generation of a mosaic comprising a plurality of geospatial images. An embodiment of the automatic mosaic generation may include automated source image selection that includes comparison of source images to base layer image to determine radiometric similar source images. Additionally, an embodiment of an automatic cutline generator may be provided to automatically determine a cutline when merging two images such that radiometric differences between the images along the cutline are reduced. In this regard, less perceivable outlines may be provided. Further still, an embodiment of a radiometric normalization module may be provided that may determine radiometric adjustments to source images to match certain properties of the base layer image. In some embodiments, when processing source images, the source images may be downsampled during a portion of the processing to reduce computational overhead. Additionally, some highly parallel computations may be performed by a GPU to further enhance performance.
G06K 9/32 - Alignement ou centrage du capteur d'image ou de la zone image
G06K 9/00 - Méthodes ou dispositions pour la lecture ou la reconnaissance de caractères imprimés ou écrits ou pour la reconnaissance de formes, p.ex. d'empreintes digitales
G09G 5/377 - Détails concernant le traitement de dessins graphiques pour mélanger ou superposer plusieurs dessins graphiques
