Invention Grant
- Patent Title: Broad area geospatial object detection using autogenerated deep learning models
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Application No.: US15452076Application Date: 2017-03-07
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Publication No.: US10013774B2Publication Date: 2018-07-03
- Inventor: Adam Estrada , Andrew Jenkins , Benjamin Brock , Chris Mangold
- Applicant: DigitalGlobe, Inc.
- Applicant Address: US CO Longmont
- Assignee: DigitalGlobe, Inc.
- Current Assignee: DigitalGlobe, Inc.
- Current Assignee Address: US CO Longmont
- Agency: Galvin Patent Law LLC
- Agent Brian R. Galvin
- Main IPC: G06K9/62
- IPC: G06K9/62 ; G06T7/73 ; G06T5/00

Abstract:
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.
Public/Granted literature
- US20170301108A1 BROAD AREA GEOSPATIAL OBJECT DETECTION USING AUTOGENERATED DEEP LEARNING MODELS Public/Granted day:2017-10-19
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