KK
K. Kenesei
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This research concerned the design and implementation of a system to perform the 3D conversion of a national road network using airborne Lidar data and land-based road elevation measurements, preserving its topology and quantifying output elevation accuracy. While the system was designed with generality in mind, specific Dutch open data datasets were used as input which were also used in a commercial project with the same goals. In addition to designing a scientifically sound equivalent of the commercial implementation, this research also explored purely academic goals, such as selecting road surface laser reflections accurately and using them for 2.5D road surface modelling purposes. Our system was based on decomposing the 3D conversion task into individually solvable 2.5D sub-problems to enable the use of efficient terrain modelling methods, and to prove that scaling via multiprocessing would be possible. At the 10-30 points per m2 sampling density we worked with, the road surfaces are oversampled. We found that because our elevation estimates are based on road reflections only, and because road surfaces are smooth and relatively flat, the output quality and accuracy depends only on the specific technique used to extract the elevations from the Lidar data. Using our methods, the output elevation accuracy represents a 30% increase relative to the Lidar input data, amounting to 10.6 cm at 95% certainty with our specific datasets. This is assumed to be violated only where local density drops below 3 points per m2, which occurs where roads are occluded by opaque objects, unless the land-based road surface measurements can provide enough additional data. Elsewhere, more than 98% of the road network conforms with the minimum point density. The 2.5D-based decomposition was found to be an effective means of simplifying the problem and terrain modelling methods were successfully used in the procedure. More than 90% of the traffic-occupied road surfaces are included in the resulting surface models, and about 75% of the total paved surfaces. We extracted the output elevations from them, using linear interpolation where they were unavailable. Our 3D conversion quality and accuracy represents an improvement relative to the commercial results, mostly due to the better prioritising of the input elevation sources, in addition to our solution's capacity to handle complex small to medium-scale occlusion better, including complex relationships between roads such as multi-level motorway junctions.
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This research concerned the design and implementation of a system to perform the 3D conversion of a national road network using airborne Lidar data and land-based road elevation measurements, preserving its topology and quantifying output elevation accuracy. While the system was designed with generality in mind, specific Dutch open data datasets were used as input which were also used in a commercial project with the same goals. In addition to designing a scientifically sound equivalent of the commercial implementation, this research also explored purely academic goals, such as selecting road surface laser reflections accurately and using them for 2.5D road surface modelling purposes. Our system was based on decomposing the 3D conversion task into individually solvable 2.5D sub-problems to enable the use of efficient terrain modelling methods, and to prove that scaling via multiprocessing would be possible. At the 10-30 points per m2 sampling density we worked with, the road surfaces are oversampled. We found that because our elevation estimates are based on road reflections only, and because road surfaces are smooth and relatively flat, the output quality and accuracy depends only on the specific technique used to extract the elevations from the Lidar data. Using our methods, the output elevation accuracy represents a 30% increase relative to the Lidar input data, amounting to 10.6 cm at 95% certainty with our specific datasets. This is assumed to be violated only where local density drops below 3 points per m2, which occurs where roads are occluded by opaque objects, unless the land-based road surface measurements can provide enough additional data. Elsewhere, more than 98% of the road network conforms with the minimum point density. The 2.5D-based decomposition was found to be an effective means of simplifying the problem and terrain modelling methods were successfully used in the procedure. More than 90% of the traffic-occupied road surfaces are included in the resulting surface models, and about 75% of the total paved surfaces. We extracted the output elevations from them, using linear interpolation where they were unavailable. Our 3D conversion quality and accuracy represents an improvement relative to the commercial results, mostly due to the better prioritising of the input elevation sources, in addition to our solution's capacity to handle complex small to medium-scale occlusion better, including complex relationships between roads such as multi-level motorway junctions.
Student report
(2020)
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K. Alhoz, K. Kenesei, M. Papageorgiou, E.E.M. Keurentjes, M. de Jong, H. Ledoux, R.Y. Peters
The outcome of the project has been successful from the interpolation perspective, where the final results have shown major improvements over the original rasters, and a multitude of possible other interpolation methods have shown potential. In the end, the algorithm used to create the gridded DTM was based on Startin’s Laplace interpolation and for DSM a Python implementation of quadrant-based IDW was used. The Startin Laplace method gives statistically good results while running quickly on it’s Rust base, though using quite some memory. The quadrant-based IDW has proven to be the best way to interpolate results which include the buildings, creating crisp edges without too many artefacts. Fur-thermore, the introduction of a polygon water flattening step was essential to prevent no-data values where water bodies prevented for accurate interpolation. The long-shot goal of being able to process all the tiles for the Netherlands was missed by a lot, eventually choosing to interpolate a series of tiles neighboring Delft to create a contiguous result set. Zuid-Holland has therefore only been partially completed, whereas the expectation was to be able to complete this area with ease in three days. An initial overview of the comparison for a single tile in Rotterdam (37HN1), can be seen in Figure 1.3 for the DTM results and Figure 1.4 for the DSM results.
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The outcome of the project has been successful from the interpolation perspective, where the final results have shown major improvements over the original rasters, and a multitude of possible other interpolation methods have shown potential. In the end, the algorithm used to create the gridded DTM was based on Startin’s Laplace interpolation and for DSM a Python implementation of quadrant-based IDW was used. The Startin Laplace method gives statistically good results while running quickly on it’s Rust base, though using quite some memory. The quadrant-based IDW has proven to be the best way to interpolate results which include the buildings, creating crisp edges without too many artefacts. Fur-thermore, the introduction of a polygon water flattening step was essential to prevent no-data values where water bodies prevented for accurate interpolation. The long-shot goal of being able to process all the tiles for the Netherlands was missed by a lot, eventually choosing to interpolate a series of tiles neighboring Delft to create a contiguous result set. Zuid-Holland has therefore only been partially completed, whereas the expectation was to be able to complete this area with ease in three days. An initial overview of the comparison for a single tile in Rotterdam (37HN1), can be seen in Figure 1.3 for the DTM results and Figure 1.4 for the DSM results.