  Building footprint segmentations for high resolution images are increasingly
demanded for many remote sensing applications. By the emerging deep learning
approaches, segmentation networks have made significant advances in the
semantic segmentation of objects. However, these advances and the increased
access to satellite images require the generation of accurate object boundaries
in satellite images. In the current paper, we propose a novel network-based on
Pix2Pix methodology to solve the problem of inaccurate boundaries obtained by
converting satellite images into maps using segmentation networks in order to
segment building footprints. To define the new network named G2G, our framework
includes two generators where the first generator extracts localization
features in order to merge them with the boundary features extracted from the
second generator to segment all detailed building edges. Moreover, different
strategies are implemented to enhance the quality of the proposed networks'
results, implying that the proposed network outperforms state-of-the-art
networks in segmentation accuracy with a large margin for all evaluation
metrics. The implementation is available at
https://github.com/A2Amir/A-Novel-Adaptive-Deep-Network-for-Building-Footprint-Segmentation.
