Comparison of Deep Learning Architectures for the Semantic Segmentation of Slum Areas From Satellite Images
Y. A. Lumban-Gaol,
A. Rizaldy,
A. Murtiyoso
Abstract:Abstract. The mapping of slum areas is an important task when considering the necessity for an inclusive, safe and resilient cities. While many methods exist in this regard, the use of machine learning and more specifically deep learning has gained traction in recent years. In this paper, we present a systematic comparison of existing deep learning architectures and backbones. The experiments in the paper investigate the question of which architecture and backbone combination and which configuration of dataset… Show more
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