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.

Storing accurate models of complex geometries in a compact way has become an increasingly challenging issue, especially when dealing with large datasets. One of such datasets is Cobra-Groeninzicht's database of all trees in the Netherlands. In the gaming industry, a new technique is being used to generate tree models: the L-system. An L-system stores a string representation of the structural model of a tree, with the added possibility for recursive modelling using growing rules. This format proves a promising alternative to more traditional methods of storing complex geometries. However, it remains unclear whether it can be an accurate enough representation for modelling and analysing real-life trees.

In this research project, the AdTree algorithm is used to reconstruct a skeleton from a point cloud of a single tree. This skeleton is then transformed to an L-System string format, as well as a CityJSON format (both in JSON structure). The L-system format comes with the advantage that it allows for several methods of increasing its compactness further (growing, generalisation). The overall size of these files also indicates fewer storage space is needed to store the tree geometry. The quality of the L-System skeleton is nearly equal to the input, the skeleton generated by. Assuming it can be read and drawn using a Turtle program, the L-system thus allows for storing the same geometric information more compactly than traditional storage formats, with sufficient accuracy, and the added possibilities of growing or generalising the model.