Patents by Inventor Chris Mangold

Chris Mangold has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Broad area geospatial object detection using autogenerated deep learning models
    Patent number: 10733759
    Abstract: 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.
    Type: Grant
    Filed: August 27, 2019
    Date of Patent: August 4, 2020
    Assignee: DIGITALGLOBE, INC.
    Inventors: Adam Estrada, Andrew Jenkins, Benjamin Brock, Chris Mangold
  • BROAD AREA GEOSPATIAL OBJECT DETECTION USING AUTOGENERATED DEEP LEARNING MODELS
    Publication number: 20200118292
    Abstract: 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.
    Type: Application
    Filed: August 27, 2019
    Publication date: April 16, 2020
    Inventors: Adam Estrada, Andrew Jenkins, Benjamin Brock, Chris Mangold
  • Broad area geospatial object detection using autogenerated deep learning models
    Patent number: 10395388
    Abstract: 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.
    Type: Grant
    Filed: July 3, 2018
    Date of Patent: August 27, 2019
    Assignee: DigitalGlobe, Inc.
    Inventors: Adam Estrada, Andrew Jenkins, Benjamin Brock, Chris Mangold
  • BROAD AREA GEOSPATIAL OBJECT DETECTION USING AUTOGENERATED DEEP LEARNING MODELS
    Publication number: 20190043217
    Abstract: 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.
    Type: Application
    Filed: July 3, 2018
    Publication date: February 7, 2019
    Inventors: Adam Estrada, Andrew Jenkins, Benjamin Brock, Chris Mangold
  • Broad area geospatial object detection using autogenerated deep learning models
    Patent number: 10013774
    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.
    Type: Grant
    Filed: March 7, 2017
    Date of Patent: July 3, 2018
    Assignee: DigitalGlobe, Inc.
    Inventors: Adam Estrada, Andrew Jenkins, Benjamin Brock, Chris Mangold
  • BROAD AREA GEOSPATIAL OBJECT DETECTION USING AUTOGENERATED DEEP LEARNING MODELS
    Publication number: 20170301108
    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.
    Type: Application
    Filed: March 7, 2017
    Publication date: October 19, 2017
    Inventors: Adam Estrada, Andrew Jenkins, Benjamin Brock, Chris Mangold
  • Broad area geospatial object detection using autogenerated deep learning models
    Patent number: 9589210
    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.
    Type: Grant
    Filed: August 26, 2015
    Date of Patent: March 7, 2017
    Assignee: DigitalGlobe, Inc.
    Inventors: Adam Estrada, Andrew Jenkins, Benjamin Brock, Chris Mangold
  • BROAD AREA GEOSPATIAL OBJECT DETECTION USING AUTOGENERATED DEEP LEARNING MODELS
    Publication number: 20170061249
    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.
    Type: Application
    Filed: August 26, 2015
    Publication date: March 2, 2017
    Inventors: Adam Estrada, Andrew Jenkins, Benjamin Brock, Chris Mangold