Abstract
Data detailed on artificial intelligence have been presented. According to news reporting originating from Hangzhou, People’s Republic of China, by NewsRx correspondents, research stated, “Building height, as an essential measure of urban vertical structure, is key to understanding how urbanization is reshaping inner-city characteristics, particularly in developing countries.” Financial supporters for this research include National Natural Science Foundation of China; Hong Kong Polytechnic University. Our news correspondents obtained a quote from the research from Zhejiang University: “However, estimating building height in urban environments remains challenging. Building height estimation with physical model-based feature approaches and machine learning approaches are limited by a constrained large-scale application capability and the lack of physical significance, respectively. In this study, we proposed a twostep method to estimate building height in spatially heterogeneous urban areas by integrating the merits of machine learning approaches and physical model-based features, together with spatial contextual information. First, we trained a block-level machine learning model on Hangzhou block units to estimate average block-level building height as spatial contextual information. Second, we trained a building-level machine learning model to estimate the final building height of Hangzhou with the estimated spatial contextual information and additional physical model-based features, including radar look angle, building wall orientation, the length of the building, and dielectric constants of the building wall. Our results showed that the proposed method can largely improve the performance of building height estimation, with an overall R2 and RMSE of 0.76 and 6.64 m, respectively.”
NETL
NSTL
Elsevier