Application of satellite image segmentation for urban planning optimization

This article presents research results of a convolution neural network for building detection on high-resolution aerial images of Planet database. Jaccard index was used for analysis of the quality of machine learning algorithm. This index of similarity compares results of algorithms with real masks. The masks were sliced on smaller parts together with images before training of developed model. The convolution neural network was launched on NVIDIA DGX-1 supercomputer, which was provided by AI-center of P.G Demidov Yaroslavl State University. The problem of building detection on satellite images can be put into practice for urban planning, building control, search of the best locations for outlets etc. © WCSE 2019. All rights reserved.