PointER
A LiDAR-Derived Point Cloud Dataset of One Million English Buildings Linked to Energy Characteristics.
https://github.com/kdmayer/PointER
Category: Consumption
Sub Category: Buildings and Heating
Keywords
building-energy dataset deep-learning lidar point-cloud
Last synced: about 2 hours ago
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Repository metadata
A LiDAR-Derived Point Cloud Dataset of One Million English Buildings Linked to Energy Characteristics
- Host: GitHub
- URL: https://github.com/kdmayer/PointER
- Owner: kdmayer
- License: mit
- Created: 2022-08-02T22:22:37.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-10-06T14:32:00.000Z (over 1 year ago)
- Last Synced: 2025-04-10T04:38:31.649Z (17 days ago)
- Topics: building-energy, dataset, deep-learning, lidar, point-cloud
- Language: Python
- Homepage: https://www.nature.com/articles/s41597-023-02544-x
- Size: 11.9 MB
- Stars: 13
- Watchers: 2
- Forks: 2
- Open Issues: 0
- Releases: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
README.md
Points for Energy Renovation (PointER):
A LiDAR-Derived Point Cloud Dataset of One Million English Buildings Linked to Energy Characteristics
Getting Started
- Please see our setup documentation for a step by step description.
- Please check our related open access paper for information about the method and the resulting dataset.
- A dataset comprising one million building point clouds with half of the buildings linked to energy features is available here.
Prerequisites
- Required packages are documented in the environment.yml file.
- The environment_for_analysis.yml includes some more packages required for visualization and analysis.
Running the Code
- To run an example point cloud generation, please use the jupyter notebook.
- To run the point cloud generation for an entire area of interest, please see the point cloud generation documentation.
- The main program can be found here. Please note, that the point cloud generation process involves some upfront data preparation.
The process involves 6 steps:
Due to the size of the point cloud files, it is recommended to set up the container on a machine with a large working memory.
We ran the code without problems on a machine with 48 GB, but a machine with 16 GB or more should work.
Dataset
The dataset contains one million building point clouds for 16 Local Authority Districts in England.
These Local Authority Districts are representative for the English building stock and selected across the country (see image).
This is an example of a resulting point cloud:
Data Sources
- Point cloud data (.laz): UK National LiDAR Programme
- Open Government Licencse
- We use Verisk UKBuildings database (.gpkg format) as building footprints
- License for personal use only
- Alternatively, we can use OSM data
- Local Authority Distric Boundaries (.shp format)
- Open Government Licencse
- Unique Property Reference Numbers (UPRN) including coordinates (.gpkg format)
- Open Government Licencse
Versioning
V0.1 Initial version
Citation
@article{Krapf2023,
doi = {10.1038/s41597-023-02544-x},
url = {https://doi.org/10.1038/s41597-023-02544-x},
year = {2023},
publisher = {Springer Science and Business Media {LLC}},
volume = {10},
author = {Sebastian Krapf and Kevin Mayer and Martin Fischer},
title = {Points for energy renovation ({PointER}): A point cloud dataset of a million buildings linked to energy features},
journal = {Scientific Data}
}
License
This project is licensed under the MIT License.
Owner metadata
- Name: Kevin
- Login: kdmayer
- Email:
- Kind: user
- Description: PhD Candidate at Stanford.
- Website: https://kdmayer.github.io/
- Location: Stanford
- Twitter:
- Company: Stanford University
- Icon url: https://avatars.githubusercontent.com/u/49768291?u=301a5aeeb9e8c9b94bc4fc17d610b1e1e811df0f&v=4
- Repositories: 26
- Last ynced at: 2024-06-11T15:51:39.478Z
- Profile URL: https://github.com/kdmayer
GitHub Events
Total
- Watch event: 1
- Fork event: 1
Last Year
- Watch event: 1
- Fork event: 1
Committers metadata
Last synced: 5 days ago
Total Commits: 224
Total Committers: 5
Avg Commits per committer: 44.8
Development Distribution Score (DDS): 0.491
Commits in past year: 30
Committers in past year: 3
Avg Commits per committer in past year: 10.0
Development Distribution Score (DDS) in past year: 0.133
| Name | Commits | |
|---|---|---|
| ADS\ga73pag | s****f@t****e | 114 |
| Kevin | k****2@g****m | 72 |
| ADS\ga73pag | k****f@f****e | 33 |
| vagrant | v****t@v****m | 4 |
| SebastianKrapf | 6****f | 1 |
Committer domains:
- vagrant.vm: 1
- ftm.mw.tum.de: 1
- tum.de: 1
Issue and Pull Request metadata
Last synced: 1 day ago
Total issues: 2
Total pull requests: 4
Average time to close issues: 7 days
Average time to close pull requests: 19 days
Total issue authors: 1
Total pull request authors: 2
Average comments per issue: 2.5
Average comments per pull request: 0.75
Merged pull request: 4
Bot issues: 0
Bot pull requests: 0
Past year issues: 0
Past year pull requests: 0
Past year average time to close issues: N/A
Past year average time to close pull requests: N/A
Past year issue authors: 0
Past year pull request authors: 0
Past year average comments per issue: 0
Past year average comments per pull request: 0
Past year merged pull request: 0
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- kdmayer (2)
Top Pull Request Authors
- SebastianKrapf (3)
- kdmayer (1)
Top Issue Labels
- bug (2)
Top Pull Request Labels
Score: 4.174387269895637