Abstract: A crowdsourced search and locate platform, comprising an application server and a crowdrank server. The application server: receives connections from crowdsourcing participants; navigates a first crowdsourcing participant to a specific geospatial location; sends an image corresponding to the geospatial location to the first crowdsourcing participant; receives tagging data from the first crowdsourcing participant, the tagging data corresponding to a plurality of objects and locations identified by the first crowdsourcing participant.

Abstract: A system for automated car counting comprises a satellite-based image collection subsystem; a data storage subsystem; and an analysis software module stored and operating on a computer coupled to the data storage subsystem. The satellite-based image collection subsystem collects images corresponding to a plurality of areas of interest and stores them in the data storage subsystem. The analysis module: (a) retrieves images corresponding to an area of interest from the data storage subsystem; (b) identifies a parking space in an image; (c) determines if there is a car located in the parking space; (d) determines a location, size, and angular direction of a car in a parking space; (e) determines an amount of overlap of a car with an adjacent parking space; (f) iterates steps (b)-(e) until no unprocessed parking spaces remain; and (g) iterates steps (a)-(f) until no unprocessed images corresponding to areas of interest remain.

Abstract: A system for automatically characterizing areas of interest (e.g., urban areas, forests, and/or other compound structures) in high resolution overhead imagery through manipulation of a dictionary of visual words. The pixels of an input overhead image are initially clustered into a plurality of hierarchically-arranged connected components of a first hierarchical data structure. Image descriptors (e.g., shape, spectral, etc.) of the connected components are then clustered into a plurality of hierarchically-arranged nodes of a second hierarchical data structure. The nodes at a particular level in the second hierarchical data structure become a dictionary of visual words. Subsets of the visual words may be used to label the cells of a grid over the geographic area as falling into one of a number of areas of interest. Categorization information from the grid may be mapped into a resultant image whereby pixels depict their respective type of area of interest.

Abstract: 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.

Abstract: A system for automatically extracting or isolating structures or areas of interest (e.g., built-up structures such as buildings, houses, shelters, tents; agricultural areas; etc.) from HR/VHR overhead imagery data by way of making as little as a single pass through a hierarchical data structure of input image components (where pixels are grouped into components based on any appropriate definition or measure of dissimilarity between adjacent pixels of the input image) to identify candidate components (e.g., possible structures of interest) free of necessarily having to re-iterate the same operator configured with different threshold parameters for a plurality of values.

Abstract: Processing and analyzing at least one remotely-sensed image to automatically detect and identify parking lots within a region of the remotely-sensed image. This may include: (1) receiving a remotely-sensed image having zero or more parking lots; (2) identifying a set of pixels related to parking lot features within the remotely-sensed image; (3) identifying at least one parking row within the remotely-sensed image; and (3) identifying a parking lot based on the identified parking rows. Identifying at least one parking lot may further include using first, second, third, and fourth level modules, such as a concrete/asphalt detection module, a road marking detection module, a vehicle detection module, and a Hough aggregation for parking row detection module.

Abstract: Techniques for using small parallax angles in remote sensing to determine cloud feature height include exploiting two identical medium-resolution SWIR bands with parallax to estimate cloud edge feature heights well enough to enable assessments of the impacts of shadows and proximate cloud scattering on ground illumination, and hence, on reflectance calculations. The bands are intentionally designed to have a suitable parallax angle, in one embodiment approximately 1.5 degrees. With this parallax, one band will see more ground pixels than the other band as they encounter a leading edge of a cloud and the other band will see more ground pixels than the one band as they encounter the lagging edge of the cloud. From these numbers of pixels, the height of the leading and lagging edges of the cloud can be determined.

Abstract: 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.

Abstract: 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.

Abstract: Utilities (e.g., systems, methods, etc.) for automatically generating high resolution population density estimation data sets through manipulation of low resolution population density estimation data sets with high resolution overhead imagery data (e.g., such as overhead imagery data acquired by satellites, aircrafts, etc. of celestial bodies). Stated differently, the present utilities make use of high resolution overhead imagery data to determine how to distribute the population density of a large, low resolution cell (e.g., 1000 m) among a plurality of smaller, high resolution cells (e.g., 100 m) within the larger cell.

Abstract: A system for automatically extracting interesting structures or areas (e.g., built-up structures such as buildings, tents, etc.) from HR/VHR satellite imagery data using corresponding LR satellite imagery data. The system breaks down HR/VHR input satellite images into a plurality of components (e.g., groups of pixels), organizes the components into a first hierarchical data structure (e.g., a Max-Tree), generates a second hierarchical data structure (e.g., a KD-Tree) from feature elements (e.g., spectral and shape characteristics) of the components, uses LR satellite imagery data to categorize components as being of interest or not, uses the feature elements of the categorized components to train the second data structure to be able to classify all components of the first data structure as being of interest or not, classifies the components of the first data structure with the trained second data structure, and then maps components classified as being of interest into a resultant image.

Abstract: A system for automatically extracting or isolating structures or areas of interest (e.g., built-up structures such as buildings, houses, shelters, tents; agricultural areas; etc.) from HR/VHR overhead imagery data by way of making as little as a single pass through a hierarchical data structure of input image components (where pixels are grouped into components based on any appropriate definition or measure of dissimilarity between adjacent pixels of the input image) to identify candidate components (e.g., possible structures of interest) free of necessarily having to re-iterate the same operator configured with different threshold parameters for a plurality of values.

Abstract: A system for automated car counting comprises a satellite-based image collection subsystem; a data storage subsystem; and an analysis software module stored and operating on a computer coupled to the data storage subsystem. The satellite-based image collection subsystem collects images corresponding to a plurality of areas of interest and stores them in the data storage subsystem. The analysis module: (a) retrieves images corresponding to an area of interest from the data storage subsystem; (b) identifies a parking space in an image; (c) determines if there is a car located in the parking space; (d) determines a location, size, and angular direction of a car in a parking space; (e) determines an amount of overlap of a car with an adjacent parking space; (f) iterates steps (b)-(e) until no unprocessed parking spaces remain; and (g) iterates steps (a)-(f) until no unprocessed images corresponding to areas of interest remain.

Abstract: Cloud cover assessment system and method provides for automatically determining whether a target digital image acquired from remote sensing platforms is substantially cloud-free. The target image is acquired and compared to a corresponding known cloud-free image from a cloud-free database, using an optimized feature matching process. A feature matching statistic is computed between pixels in the target image and pixels in the cloud-free image and each value is converted to a feature matching probability. Features in the target image that match features in the cloud-free image exhibit a high value of feature matching probability, and are considered unlikely to be obscured by clouds, and may be designated for inclusion in the cloud-free database.

Abstract: 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.

Abstract: 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.

Abstract: Techniques for making, using and updating a Modeled Atmospheric Correction Object (MACO) cluster and the MACO sites that are selected from within a given MACO cluster. The MACO construct is a novel application of a Model of Reality (MOR) that provides synthetic ground truth essential to converting imagery from top-of-the-atmosphere radiance to surface reflectance given a variety of spatial, spectral and radiance effects involving non-uniform distributions of opaque clouds, cirrus clouds, aerosols, water vapor, surface ice, surface snow, shadows and bidirectional reflectance distribution function (BRDF) effects.

Abstract: Techniques for using small parallax angles in remote sensing to determine cloud feature height include exploiting two identical medium-resolution SWIR bands with parallax to estimate cloud edge feature heights well enough to enable assessments of the impacts of shadows and proximate cloud scattering on ground illumination, and hence, on reflectance calculations. The bands are intentionally designed to have a suitable parallax angle, in one embodiment approximately 1.5 degrees. With this parallax, one band will see more ground pixels than the other band as they encounter a leading edge of a cloud and the other band will see more ground pixels than the one band as they encounter the lagging edge of the cloud. From these numbers of pixels, the height of the leading and lagging edges of the cloud can be determined.

Abstract: Techniques for synthesizing finer resolution image pixels and estimations of most likely endmembers and their abundances within relatively coarse resolution image pixels by using relatively fine resolution nearly collinear and nearly coincident panchromatic and multispectral data in the estimation of mixing endmembers, their abundances and their locations within native-scale coarse resolution imagery.

Abstract: 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.