Estimating building height from ICESat-2 data: the case of the Netherlands

Building height information has been more used in a variety of industries in recent years. This information can be used in sustainable urban planning, urban climate research, population estimation, and three-dimensional (3D) building reconstruction, etc. Nowadays, building height information is obtained mostly by photogrammetry, high-resolution photos, and aerial Light Detection and Ranging (LiDAR) data. However, the existing technique is constrained by issues of scale, cost, and quality. The Ice, Cloud and land Elevation Satellite-2 (ICESat-2) was launched in 2018, using photoncounting LiDAR technology to gather Earth’s surface elevation data globally. It represents the highest level in space-borne laser altimeters and has been proved can estimate building height. In this thesis, a method is proposed to estimate the height of all buildings in the Netherlands. To estimate the building height, elevation data from ICESat-2 and footprints from Basisregistratie Adressen en Gebouwen (BAG) are two used datasets. Spatial interpolation methods and percentile methods are used to get ground and roof elevation for each footprint, respectively. Random Forest Regression (RFR) method is used to deal with the low coverage of ICESat-2. The result shows that less 3% of buildings could obtain their height from ICESat-2 data. Among these buildings, 90% of them are lower than 10 meters and half of them are between 5 - 10 meters. This caused a low performance of the prediction model with Mean Absolute Error (MAE) of 2.1m. The building between 5 - 10 meters has the smallest MAE of 1.1267m. Small amount of available of ICESat-2 data is the key reason, which leads to the training data of building over 10 meters is not enough. The results reveal it is impossible to get the height of all buildings in the Netherlands with ICESat-2 data. But the proposed method is a feasible option for buildings between 5 and 10 meters in height.Geomatic