A large-scale Glasgow 3D city models derived from airborne LiDAR point clouds

Li, Q. , Zhao, Q. , Quintas Zon, M., Jablon, P. E., Wang, M., Hu, C., Ou, Y. and Sen, S. (2024) A large-scale Glasgow 3D city models derived from airborne LiDAR point clouds. [Data Collection]

Collection description

UBDC generates 3D city models via the airborne LiDAR point clouds acquired between 2020-2021 on behalf of Glasgow City Council.

We prepared a set of training and validation data to classify the whole LiDAR dataset for subsequent 3D model construction. The annotated point clouds were generated to train the weakly supervised semantic segmentation algorithm Semantic Query Network (SQN) to classify point clouds [1]-[2]. Four tiles of the 1×1 km2 sparse point clouds were annotated for training and four tiles of 0.5×0.5 km2 full point clouds were annotated for validation. Annotated data contains historical and modern architectures as well as residential and industrial buildings. The point clouds were manually labeled into four categories: ground, trees (including arbors and shrubs but excluding lawn), buildings, and others. The annotated point cloud data can be used to train a deep learning model for point cloud classification or help advance the manipulation within airborne LiDAR.

The large-scale 3D city model containing 3D information on terrain, trees, and buildings in Glasgow City. This dataset comprises terrain, tree canopy, and building products derived from high-density airborne LiDAR data.

The terrain products include Digital Terrain Model (DTM), Digital Surface Model (DSM), and normalized Digital Surface Model (nDSM) in 0.5 m spatial resolution. The DTM and DSM rasters were provided by the vendor and nDSM rasters were obtained by subtracting DTM from DSM. Terrain products are provided in 5 km by 5 km GeoTIF format raster.

The tree canopy products are composed of canopy height models (CHM) and tree top locations. Classified tree point clouds were applied with pit-free algorithm to generate CHM in 0.5 m grid raster in GeoTIF format [3]-[4]. Treetop locations were identified by using Local Maximum Filter based on CHM and are recorded as points in Shapefile format. The tree canopy products are provided are provided in 5 km by 5 km tiles.

Building 3D model products include footprint polygons with building height attributes and 3D mesh of building models in LoD1 and LoD2 levels. A series of processes such as converting building point clouds to building height models (BHM), converting BHM to polygons, and polygon regularization were conducted to obtain the building footprint polygons. Building height attributes were calculated from BHM for each footprint. The building footprint data are provided in Shapefile format. LoD1 models were generated based on the footprint and average height of the building. LoD2 models were constructed based on footprint and building point cloud with City3D tool[5]. LoD1 and LoD2 models are provided in OBJ and shapefile format. Building 3D model products are provided in 5 km by 5 km tiles.

Reference:

[1] Hu, Q., Yang, B., Fang, G., Guo, Y., Leonardis, A., Trigoni, N., & Markham, A. (2022, October). Sqn: Weakly-supervised semantic segmentation of large-scale 3d point clouds. In European Conference on Computer Vision (pp. 600-619). Cham: Springer Nature Switzerland.

[2] Li, Q., & Zhao, Q. (2023, May). Weakly-Supervised Semantic Segmentation of Airborne LiDAR Point Clouds in Hong Kong Urban Areas. In 2023 Joint Urban Remote Sensing Event (JURSE) (pp. 1-4). IEEE.

[3] Khosravipour, A., Skidmore, A. K., Isenburg, M., Wang, T., & Hussin, Y. A. (2014). Generating pit-free canopy height models from airborne lidar. Photogrammetric Engineering & Remote Sensing, 80(9), 863-872.

[4] Roussel, J. R., Auty, D., Coops, N. C., Tompalski, P., Goodbody, T. R., Meador, A. S., ... & Achim, A. (2020). lidR: An R package for analysis of Airborne Laser Scanning (ALS) data. Remote Sensing of Environment, 251, 112061.

[5] Huang, J., Stoter, J., Peters, R., & Nan, L. (2022). City3D: Large-scale building reconstruction from airborne LiDAR point clouds. Remote Sensing, 14(9), 2254.

Other resources:

Airborne LiDAR point clouds, DTM and DSM are available at Scottish Remote Sensing Portal, https://remotesensingdata.gov.scot/

An old version of Glasgow 3D city models in the city centre are available at https://data.glasgow.gov.uk/pages/3d-urban-model.

College / School: College of Social Sciences > School of Social and Political Sciences > Urban Studies
Date Deposited: 29 Apr 2024 13:40
Related resources:
URI: https://researchdata.gla.ac.uk/id/eprint/1642

Repository Staff Only: Update this record

Li, Q. , Zhao, Q. , Quintas Zon, M., Jablon, P. E., Wang, M., Hu, C., Ou, Y. and Sen, S. (2024); A large-scale Glasgow 3D city models derived from airborne LiDAR point clouds

Urban Big Data Centre

https://researchdata.gla.ac.uk/1642

Retrieved: 2024-05-27