LAUSR

Abstract Brownfield assessment is a crucial precondition for sustainable urban renewal. In particular, brownfield assessment usually involve a large amount of data and information relevant to urban or land dynamic. However, obtaining these data and information in fine scale has been especially challenging via traditional datasets (like surveyed or statistics datasets). This paper proposes a method for integrating crowdsourced datasets and traditional datasets to measure dynamic information of land parcels and applies a presence and background data machine learning (PBL) model to assess the redevelopment suitability in mass. The work focuses on the study area of Shenzhen, a high-density urban context with an adequate amount of urban renewal cases and sufficient crowdsourced data. Results indicated that assessments based on the combination of crowdsourced datasets and traditional datasets were accurate and reliable for the residential and commercial land parcels. Furthermore, mass assessments at a fine spatial-temporal scale are viable if introduce crowdsourced datasets due to the cost of obtaining the crowdsourced data is much lower, and the spatial-temporal scale is much finer than investigated or surveyed data.