LAUSR

This paper is devoted to detect and classify intra-urban changes by jointly exploiting Sentinel-1 (S-1) SAR data and nighttime light data. By extracting urban extents and urban density maps from SAR data, changes in nighttime lights can be used to detect changes related to the level of activity in a specific portion of each urban areas. At the same time, changes in radar backscattering are prone to reveal changes in the two- and three-dimensional structures of the built-up. The combination of these multimodal datasets has already proved to be useful to discriminate urban change patterns at the city level. In this paper, instead, SAR datasets from S-1 are exploited, allowing the recognition of different intra-urban changes. Experimental results focus on fast growing (mega) cities in East Asia, allowing us to understand in a more detailed way how they are changing and evolving in all three dimensions. Examples for Nanjing, Shanghai, and Guangzhou (China), Saigon (Vietnam), and Vientiane (Laos) are discussed to prove this statement.