CR
C. Rao
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Road network models are essential for modern urban planning, transportation analysis, and digital map reconstruction. This thesis addresses the development of a comprehensive digital road network model that advances from linear representation to a lane-level and areal polygonal representation, based on OpenStreetMap (OSM) data. While OSM provides rich road network data, challenges remain in reconstructing high-resolution, lane-level networks that are both geometrically precise and topologically consistent.
The objective of this research is to create a reffned digital road model that enhances the geometric accuracy and topological integrity of road networks by transitioning from road-level centerlines to lane-level polygons and full areal representations. This model is designed to capture essential aspects and semantic information such as traffic modes (motorized and cycling), intersection complexity, and road connectivity.
To achieve these objectives, the lane network generation methodology integrates OSM data with graph theory and civil engineering principles, focusing on lane topology and connectivity. The approach enhances linear geometry into areal geometry by combining a buffer-based method for road ribbons with a node-based method for intersection geometry. This process generates multiple intermediate results, including a road-level data model, a traffic-level data model, and grouped strokes representing global and local adjacency as graph-like models. Additionally, lane-level networks, lane geometry, and areal representations of roads and intersections are developed. Key innovations include a generalized method for lane centerline and polygon generation, ensuring road-lane correspondence and consistency between low-LoD linear models and high-LoD areal models.
The final product is a digital road network model that accommodates variations in road shapes, realistic intersection geometry, and detailed trafffc lane information, enabling highly detailed urban simulations and transportation analyses. While challenges remain regarding lane alignment, complex intersections, and the lack of detailed trafffc signal data, this research successfully bridges the gap between OSM’s raw linear road centerline data and the lane-level areal representations.
...
The objective of this research is to create a reffned digital road model that enhances the geometric accuracy and topological integrity of road networks by transitioning from road-level centerlines to lane-level polygons and full areal representations. This model is designed to capture essential aspects and semantic information such as traffic modes (motorized and cycling), intersection complexity, and road connectivity.
To achieve these objectives, the lane network generation methodology integrates OSM data with graph theory and civil engineering principles, focusing on lane topology and connectivity. The approach enhances linear geometry into areal geometry by combining a buffer-based method for road ribbons with a node-based method for intersection geometry. This process generates multiple intermediate results, including a road-level data model, a traffic-level data model, and grouped strokes representing global and local adjacency as graph-like models. Additionally, lane-level networks, lane geometry, and areal representations of roads and intersections are developed. Key innovations include a generalized method for lane centerline and polygon generation, ensuring road-lane correspondence and consistency between low-LoD linear models and high-LoD areal models.
The final product is a digital road network model that accommodates variations in road shapes, realistic intersection geometry, and detailed trafffc lane information, enabling highly detailed urban simulations and transportation analyses. While challenges remain regarding lane alignment, complex intersections, and the lack of detailed trafffc signal data, this research successfully bridges the gap between OSM’s raw linear road centerline data and the lane-level areal representations.
...
Road network models are essential for modern urban planning, transportation analysis, and digital map reconstruction. This thesis addresses the development of a comprehensive digital road network model that advances from linear representation to a lane-level and areal polygonal representation, based on OpenStreetMap (OSM) data. While OSM provides rich road network data, challenges remain in reconstructing high-resolution, lane-level networks that are both geometrically precise and topologically consistent.
The objective of this research is to create a reffned digital road model that enhances the geometric accuracy and topological integrity of road networks by transitioning from road-level centerlines to lane-level polygons and full areal representations. This model is designed to capture essential aspects and semantic information such as traffic modes (motorized and cycling), intersection complexity, and road connectivity.
To achieve these objectives, the lane network generation methodology integrates OSM data with graph theory and civil engineering principles, focusing on lane topology and connectivity. The approach enhances linear geometry into areal geometry by combining a buffer-based method for road ribbons with a node-based method for intersection geometry. This process generates multiple intermediate results, including a road-level data model, a traffic-level data model, and grouped strokes representing global and local adjacency as graph-like models. Additionally, lane-level networks, lane geometry, and areal representations of roads and intersections are developed. Key innovations include a generalized method for lane centerline and polygon generation, ensuring road-lane correspondence and consistency between low-LoD linear models and high-LoD areal models.
The final product is a digital road network model that accommodates variations in road shapes, realistic intersection geometry, and detailed trafffc lane information, enabling highly detailed urban simulations and transportation analyses. While challenges remain regarding lane alignment, complex intersections, and the lack of detailed trafffc signal data, this research successfully bridges the gap between OSM’s raw linear road centerline data and the lane-level areal representations.
The objective of this research is to create a reffned digital road model that enhances the geometric accuracy and topological integrity of road networks by transitioning from road-level centerlines to lane-level polygons and full areal representations. This model is designed to capture essential aspects and semantic information such as traffic modes (motorized and cycling), intersection complexity, and road connectivity.
To achieve these objectives, the lane network generation methodology integrates OSM data with graph theory and civil engineering principles, focusing on lane topology and connectivity. The approach enhances linear geometry into areal geometry by combining a buffer-based method for road ribbons with a node-based method for intersection geometry. This process generates multiple intermediate results, including a road-level data model, a traffic-level data model, and grouped strokes representing global and local adjacency as graph-like models. Additionally, lane-level networks, lane geometry, and areal representations of roads and intersections are developed. Key innovations include a generalized method for lane centerline and polygon generation, ensuring road-lane correspondence and consistency between low-LoD linear models and high-LoD areal models.
The final product is a digital road network model that accommodates variations in road shapes, realistic intersection geometry, and detailed trafffc lane information, enabling highly detailed urban simulations and transportation analyses. While challenges remain regarding lane alignment, complex intersections, and the lack of detailed trafffc signal data, this research successfully bridges the gap between OSM’s raw linear road centerline data and the lane-level areal representations.
Student report
(2023)
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S. Li, C. Rao, C. Zhang, W. Qiuxian, C.A. León Sánchez, G. Stavropoulou, J.E. Stoter
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.
...
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.
...
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.
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.