Building height serves as an essential feature of urban morphology that provides valuable insights into human socio-cultural behaviors and their impact on the environment in an urban milieu. However, openly accessible building height information at the individual building level is still lacking and remains sorely limited. Previous studies have shown that the ICESat-2′s ATL03/08 products are of good accuracy for urban building heights retrieval, however, these studies are limited to areas with available data coverage. To this end, we propose a method for extracting urban building height by using ICESat-2 ATL03 photons and high-resolution remote sensing images. We first extracted the information of building roof to footprint offsets and building shadows from high resolution imagery using multitasking CNN frameworks. Using the building height samples calculated from ICESat-2 ATL03 photons, we developed a building height estimation method that combines building offset and shadow length information. We assessed the efficacy of the proposed method in the Wujiaochang area of Shanghai city, China. The results indicated that the proposed method is able to extract building height with a MAE of 4.7 m, and outperforms the traditional shadow-based and offset-based method. We believe that the proposed method is a good candidate for accurately retrieving building heights on a city-wide scale.

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has been collecting elevation measurements of the Earth’s surface since its launch in September 2018. Although ICESat-2 was not designed for urban applications, its excellent altimetry capabilities over the globe make it possible to obtain urban height information. However, few studies have been conducted to validate ICESat-2 height measurements in urban areas. In this letter, we evaluate the heights retrieved from 20 months of ATL03 and ATL08 data using airborne LiDAR data collected in New York City (NYC). The results indicated that the heights from ATL03 product have a moderate agreement with airborne LiDAR data with vertical errors around −1.49 m (root mean squared error [RMSE] = 2.89 m, mean absolute error [MAE] = 2.11 m, $R^{2} = 0.98$ , and observations = 910 497). The ATL08 product also performed a fine accuracy in terrain heights estimating (mean error [ME] = −0.01 m, RMSE = 3.63 m, MAE = 2.04 m, and $R^{2} = 0.94$ ). In the three categories of urban environments, ATL03 performs best in urban high-rise dense area, with an average residual error of −1.44 m, followed by urban non-high-rise dense area with an average residual error of −1.49 m. In urban forest area, ATL08 shows its performance in measuring terrain height with an RMSE of 1.78 m. We demonstrated that ICESat-2 can provide a useful source of urban heights and holds great potential to light up more urban applications related to urban change monitoring and 3-D morphology.

Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS) are two emerging mobile mapping technologies for capturing detailed spatial information near ground in urban built environments. BLS has flexible mobility and usually provides point clouds in a local coordinate system. Therefore, a mismatch between VLS and BLS point clouds data is quite common. Fusing VLS and BLS data in different coordinate systems could provide a comprehensive survey of urban built environments. Because of the complexity of urban road environments and the difference in data acquisition methods, traditional registration approaches based on point-level correspondences are likely to fail and sometimes involve substantial manual efforts. In this paper, we propose a novel registration approach that finds the optimal transformation between the respective point clouds based on a unique tree distribution pattern defined by tree trunk centers. The proposed method consists of three key steps, i.e., trunk center extraction, stepwise minimum spanning tree (MST) matching, and transformation estimation. Stepwise MST matching is an essential step in finding the one-to-one correspondences using a topological similarity between the two LiDAR datasets. We evaluated our method with five real-world datasets collected in the Shanghai city, China. The results showed that the proposed method performed well in all five experiment sites with an average rotation error of less than 0.06° and an average translation error of less than 0.05 m. Moreover, the reported mean position deviation in the five sites are 0.112 m, 0.144 m, 0.176 m, 0.148 m, and 0.184 m, respectively. Our proposed method has a great potential for registering multiplatform LiDAR data that could provide comprehensive and essential 3D information for numerous urban applications.