Digital Surface Models (DSMs) are commonly employed to investigate topographical characteristics and processes; however, the presence of canopy and infrastructure in urban and forested areas can lead to height biases and inaccuracies. In this study, I aim to correct such biases by applying a deep learning approach known as Residual U-Net to remove the selected pixels and generate Digital Terrain Models (DTMs) that accurately represent the Earth's surface without canopy and infrastructure influence.

The Residual U-Net model was trained and tested on a dataset of DSM and DTM pairs, which were acquired from resampled AHN4. The model was evaluated on its ability to predict DTMs from DSMs, and its performance was compared with other existing methods. Additionally, the model was tested on different resolutions and the Copernicus DEM to assess its adaptability and generalization capabilities.

The results indicate that the Residual U-Net model outperforms conventional techniques, effectively reducing the influence of canopy and infrastructure, and resulting in DTMs with enhanced precision. The study also explores the errors in detail and identifies the model's error causes, highlighting its limitations and areas for potential improvement.

This study concludes by demonstrating the efficacy of applying deep learning techniques, such as Residual U-Net, to correct global elevation models for canopy and infrastructure. The results indicate that the model is a promising tool for topographical investigation in both urban and woodland situations, offering a versatile solution for generating accurate DTMs from DSMs.

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.