Building footprint extraction from very high-resolution satellite images using deep learning

Building footprint datasets are valuable for a variety of uses in urban settings. For a number of urban applications, polygonal building outlines with regularised bounds are required and are extremely challenging to prepare. We propose a deep learning strategy based on convolutional neural networks for retrieving building footprints. The model was trained using images from a variety of places across the metropolis, highlighting differences in land use patterns and the built environment. The evaluation measures indicate how the accuracy characteristics of distinct built-up settings differ. The results of the model are equivalent to cutting-edge building extraction methods.

Automated summary

This research proposes a deep learning strategy based on convolutional neural networks for retrieving building footprints in urban areas. The model, trained using images from various places, is able to accurately extract building outlines in different built-up settings.