DEEP BUILDING FOOTPRINT EXTRACTION FOR URBAN RISK ASSESSMENT – REMOTE SENSING AND DEEP LEARNING BASED APPROACH

Abstract

Mapping building footprints can play a crucial role in urban dynamics moni-toring, risk assessment and disaster management. Available free building footprints, like OpenStreetMap, provide manually annotated building foot-print information for some urban areas; however, frequently it does not en-tirely cover urban areas in many parts of the world and is not always availa-ble. The huge potential for meaningful ground information extraction from high-resolution Remote Sensing imagery can be considered as an alternative and a reliable source of data for building footprint generation. Therefore, the aim of the study is to explore the use of satellite imagery data and some of the state-of-the art deep learning tools to fully automate building footprint extraction. To better understand the usability and generalization ability of those approaches, this study proposes a comparative analysis of the perfor-mances and characteristics of two of the most recent deep learning models such as Unet and Attention-Unet for building footprint generation.