Broad area geospatial object detection using autogenerated deep learning models

1. A system for broad area geospatial object detection using auto-generated deep learning models, comprising:

a deep learning model training software module stored in a memory of and operating on a processor of a computing device; and

an image analysis software module stored in the memory of and operating on the processor of the computing device;

wherein the deep learning model training software module:

receives training data comprising a plurality of orthorectified geospatial images with a plurality of objects present therein, at least a first subset of the plurality of objects being labeled and a second subset of objects being unlabeled;

classifies the training data into a plurality of categories;

applies one or more image modification steps to the training data; and

generates an object classification model from the training data using a deep learning method comprising separate processing of the first and second subsets of the training data through a convolutional neural network system; and

wherein the image analysis software module:

receives orthorectified geospatial imagery;

applies a plurality of image modifications to the unanalyzed orthorectified geospatial imagery;

discards images unsuitable for analysis;

uses the object classification model to automatically identify and label all objects of interest in the received, unanalyzed orthorectified geospatial imagery; and

outputs the geospatial locations of the identified objects of interest.

2. A method for conducting automated identification of feature items from orthorectified geospatial imagery data comprising the following steps:

receiving, at a deep learning model training software module stored in a memory of and operating on a processor of a computing device, a plurality of orthorectified geospatial images, divided into discrete segments where a plurality of objects of interest are present therein, at least a first subset of the plurality of objects of interest being labeled and a second subset of objects of interest being unlabeled;

placing pre-labeled geospatial imagery segments into categories based upon the objects of interest they contain;

applying one or more image modification steps;

generating, using a convolutional neural network system operating as part of an image analysis software module stored in a memory of and operating on a processor of a computing device, an object classification model from the training data using a deep learning method comprising separate processing of the first and second subsets of the training data;

using the trained image analysis software module to identify feature items of interest within unanalyzed geospatial imagery data; and

reporting the identities and geospatial locations of a plurality of features of interest.

3. The system of claim 1 , wherein the plurality of categories are pre-defined.

4. The system of claim 1 , wherein the plurality of image modification steps are drawn from a set comprising artifact removal, color standardization, and resolution data determination.

5. The system of claim 1 , wherein the objects of interest are automatically identified and labeled regardless of the orientation of that feature item within the section.

6. The system of claim 5 , further wherein the objects of interest are automatically identified and labeled accounting for differences in item scale by using a multi-scale sliding window algorithm.

7. The method of claim 2 , wherein the plurality of categories are pre-defined.

8. The method of claim 2 , wherein the plurality of image modification steps are drawn from a set comprising artifact removal, color standardization, and resolution data determination.

9. The method of claim 2 , wherein the feature items of interest are identified regardless of the orientation of that feature item within the section.

10. The method of claim 9 , further wherein the feature items of interest are identified accounting for differences in item scale by using a multi-scale sliding window algorithm.