The accuracy and comprehensiveness of 3D city models have become increasingly important for applications like monitoring, sustainability evaluation, disaster management, and urban planning. However, creating accurate and complete 3D models is challenging. Traditional methods, such as photogrammetry and Light Detection and Ranging (LiDAR), often face issues like time-consuming processes and data gaps caused by occlusion. Additionally, these methods typically produce discrete models that fail to capture all the information from the original objects, limiting the ability to reconstruct small structures.

In this thesis, we explore the potential and characteristics of enhancing 3D models for urban areas using implicit neural representation. This method offers generalizability, ensures no void areas, and provides more detailed information when using multi-modal inputs. The datasets used for model training include Actueel Hoogtebestand Nederland 3 (AHN3), 2019 Luchtfoto Beeldmateriaal, 3D Basisregistratie Adressen en Gebouwen (BAG), and Basisregistratie Grootschalige Topografie (BGT). The city center of Eindhoven is used for training and testing, and the city center of Rotterdam serves as a test dataset.

The method involves learning location-dependent latent codes from raw point cloud and orthophoto, and adjusting the probability of space occupancy based on point clouds sampled from the 3D city model. A decoder is used to calculate the probability of existence for any point in the 3D space from the continuous field. Proper sampling allows us to derive a continuous Digital Surface Model (DSM) with unlimited resolution from the neural network.

The results show a high degree of accuracy: the generated DSM for the training area in Eindhoven demonstrated a median absolute error of 0.484 m overall. Accuracy for building areas was recorded at 0.798 m, and for terrain, it was 0.302 m. The model also displayed robust generalization capabilities, with an accuracy of 0.336 m in the Eindhoven test area and 0.23 meters in Rotterdam. Additionally, the model's ability to fill voids was confirmed through both visual inspection and quantitative evaluations. This research underscores the potential of implicit neural representation for generating detailed DSMs and effectively filling no-data voids.

In a rapidly evolving digital landscape, 3D city models have become more accurate and complex. Despite their widespread availability of open-source 3D city model datasets, these invaluable resources remain underutilized. Our primary goal centers on the classification of building and roof types. For our client, Spotr, our work directly impacts on their current project of house value estimations for insurance companies. Beyond insurance, it can be used in energy level rating, sustainability assessments, and so on.
In classification schema design, we designed 15 building types and 7 roof types that are compiled with cities in Europe. We labeled 6599 buildings and 2551 roofs in total using CityJSON files of Delft, Rotterdam, Den Haag, Munich, and Berlin. The accuracy for building classification is 70% and for roof classification is 74%.
For future work, we can increase the labeling amount and include more data from different regions. What's more, it can be extended to 2D roof classification: With the help of 3D roof surfaces, we can easily have the bounding box of the roof and additional 3D features can also be used.