Web mapping is essential in many fields, including navigation, real estate, and tourism. With advances in mapping technology, 3D web mapping has been introduced by multiple geospatial platforms. Compared to 2D web mapping, 3D web mapping provides more details, better visual effects, and is crucial for applications like digital twins. In navigation, terrain is a fundamental element in 3D mapping. However, the vast amount of data from 3D models requires the use of multiple Levels of Detail (LoDs) to reduce data consumption and request time on the client side. Without further processing, different LoD models will have gaps at their boundaries. Such gaps can cause visual inconsistency and artifacts. Locking boundaries is a commonly used method to solve the gaps in different LoDs problems, that is to maintain the vertices on the boundaries during simplification. While this the method causes inefficiency in simplification. Nanite (Nan) proposed a method using triangle clustering to address issues with Level-of-detail (LoD) model gaps, inefficiencies in cluster simplifications, and visual inconsistencies caused by locked boundaries. This method can be applied to large-scale models, such as terrain data. However, this method was only applied to local data use cases and not to web mapping. Therefore, this thesis investigates adapting the triangle clustering method for web mapping. Considering the fact that the Nanite triangle clustering method performs well on multi-LoD models (Nan), we believe this method could also be migrated to the area of large-scale terrain. We proposed a method to construct multi-level LoD terrain for navigation based on Nanite method. In our approach, large-scale terrain data is distributed using web tiles for navigation purposes.
Keywords— WebGIS; Web mapping; 3D map; Multi-LoD ... Read more

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.




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