Abstract: An image processing system may include a processor and an associated memory configured to store training data that includes training geospatial images. Each training geospatial image may include pixels. The processor may be configured to operate a training model to identify a given feature from each of the training geospatial images, and to iteratively generate a probability distribution function based upon a number of pixels corresponding to the given feature and also based upon a bias factor being reduced with each iteration.

Abstract: Methods and systems in accordance with the present invention automatically subdivide an area having an arbitrary shape into multiple sub-regions that have approximately equal area under a threshold, with compact shapes having minimal perimeter length. These systems input an arbitrarily shaped zone and recursively bisect it until all of the new sub-zones are smaller than a particular threshold. A data processing system subdivides a two-dimensional region, such as a digital image of a landmass. The data processing system loads the region into memory, determines a minor axis of the region, and splits the region along the minor axis into a first sub-region and a second sub-region. The sub-regions are evaluated to determine if they are under the threshold area. The steps of the process are repeated until all resulting sub-regions are under the threshold area. Consistently compact sub-regions with minimal perimeter are achieved by splitting along the minor axis.

Abstract: An image processing system may include a processor and an associated memory configured to store training data that includes training geospatial images. The processor may be configured to operate a training model to identify a given feature from each of the training geospatial images, and to iteratively apply a bias factor to a loss function based upon a number of incorrectly identified pixels for the given feature. The bias factor may be reduced with each iteration.

Abstract: An image processing system may include a processor and an associated memory configured to store training data that includes training geospatial images. The processor may be configured to blur each of the training geospatial images. The processor may also be configured to iteratively operate a training model to identify a given feature from each of the blurred training geospatial images so that the blurring is reduced with each iteration.