A flexible framework for built-up height mapping using ICESat-2 photons and multisource satellite observations

Built-up heights serve as a nexus in understanding the complex relationship between urban forms and socioeconomic activities. With the advent of remote sensing technology, built-up height mapping from satellite observations has become available over the past years. However, the absence of high-precision sample data poses a significant limitation to built-up height mapping at large (regional or global) scales, particularly in developing regions. To address this issue, we proposed a flexible mapping framework to derive precise building height samples using the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data for built-up height estimation. First, we calculated building heights from ICESat-2 photons using advanced algorithms such as Random Sample Consensus (RANSAC) linear fitting and cloth simulation filtering. Then, we constructed large-scale built-up height samples by aggregating the height information into grid cells with optimal size. Finally, aided by these grids with height information from ICEsat-2 and other satellite observations from Sentinel data as well as the digital surface model (DSM), we mapped built-up heights in two mega-cities (i.e., New York and Shenzhen) using the random forest regression model. Our results demonstrate building height estimation using ICESat-2 data generally exhibits in relation to other studies high accuracy, showing great potential to support large-scale built-up height mapping using satellite observations. We found the optimal grid size for built-up height mapping is around 300 m, after a comprehensive sensitivity analysis regarding the building fraction within the grid and its size. Overall, the mapped built-up heights are reliable, with relatively low mean absolute errors (MAE) of 2.69 m in New York and 3.87 m in Shenzhen, similar to or better than previous studies. By leveraging high-precision elevation data provided by the ICESat-2 data, our proposed approach can effectively collect samples in regions with limited information on building heights, showing great potential for large-scale built-up height monitoring and supporting future urban studies.