- Longmont CO, US Christopher Burd - Washington DC, US Andrew Jenkins - Waterford VA, US Joseph Newbrough - Springfield VA, US Scott Szoko - Odenton MD, US Melanie Vinton - Fairfax Station VA, US
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.
Synthesizing Training Data For Broad Area Geospatial Object Detection
- Longmont CO, US Christopher Burd - Washington DC, US Andrew Jenkins - Waterford VA, US Joseph Newbrough - Springfield VA, US Scott Szoko - Odenton MD, US Melanie Vinton - Fairfax Station VA, US
International Classification:
G06T 7/00 G06N 3/04 G06N 3/08 G06T 7/40 G06K 9/62
Abstract:
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.
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