Glasgow 3D city models derived from airborne LiDAR point clouds licensed data

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.

Cite this as

Glasgow City Council/Urban Big Data Centre (2024). Glasgow 3D city models derived from airborne LiDAR point clouds licensed data [Data set]. University of Glasgow. https://doi.org/10.20394/vwyl2on6
Retrieved: 18:31 13 Jul 2024 (UTC)

Additional Info

Title Glasgow 3D city models derived from airborne LiDAR point clouds licensed data
Alternative title Glasgow annotated airborne LiDAR point clouds
URL glasgow-3d-city-models-derived-from-airborne-lidar-point-clouds-licensed-data
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.

Content

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.

References: * [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.

Subjects Urban Planning
Topics
Dataset Citation Glasgow City Council/Urban Big Data Centre. Economic and Social Research Council.Glasgow 3D city models derived from airborne LiDAR point clouds licensed data, 2024 [data collection]. University of Glasgow - Urban Big Data Centre.
Time Period Coverage LiDAR data were acquired from 2020-2021
Geographical Coverage Part of the areas in Glasgow City, UK
Spatial Units
Observation Units
Resource Type dataset
Data Format LiDAR Point clouds and text file
Weighting
Method of Collection

LiDAR data were acquired from 2020-2021

Collection Status
Dataset Aggregation
Data Owner Glasgow City Council/Urban Big Data Centre
Data Owner Url https://ubdc.ac.uk/
License other-closed
Licence Specifics
Provider b2ba2219-ff6c-461a-a746-e762ef7600fd
Version
Dataset Available
Dataset Closed
Dataset Valid
Dataset Updating Frequency
Dataset Next Version Due
Date Published 2024-04-04
Date of Fieldwork
Dataset File Type
Dataset File Size 11,177,334 KB
Dataset Creation Date
Dataset Access Restrictions Safeguarded Dataset
Metadata Created Date 2024-04-04
Metadata Created Institution Urban Big Data Centre
Dataset Fields (1)
Field Name:
Description:
Type: