Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.