{"id":13723702,"url":"https://github.com/kdmayer/PointER","last_synced_at":"2025-05-07T17:30:49.682Z","repository":{"id":175978562,"uuid":"520665553","full_name":"kdmayer/PointER","owner":"kdmayer","description":"A LiDAR-Derived Point Cloud Dataset of One Million English Buildings Linked to Energy Characteristics","archived":false,"fork":false,"pushed_at":"2023-10-06T14:32:00.000Z","size":12514,"stargazers_count":13,"open_issues_count":0,"forks_count":2,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-04-10T04:38:31.649Z","etag":null,"topics":["building-energy","dataset","deep-learning","lidar","point-cloud"],"latest_commit_sha":null,"homepage":"https://www.nature.com/articles/s41597-023-02544-x","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kdmayer.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2022-08-02T22:22:37.000Z","updated_at":"2024-11-04T19:14:22.000Z","dependencies_parsed_at":null,"dependency_job_id":"61cca017-4cfc-4b6e-ab69-16afd305e579","html_url":"https://github.com/kdmayer/PointER","commit_stats":{"total_commits":224,"total_committers":5,"mean_commits":44.8,"dds":0.4910714285714286,"last_synced_commit":"273d41cbba97d93c971bdcd1d1848b286a39961c"},"previous_names":["kdmayer/pointer"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kdmayer%2FPointER","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kdmayer%2FPointER/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kdmayer%2FPointER/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kdmayer%2FPointER/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kdmayer","download_url":"https://codeload.github.com/kdmayer/PointER/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248345273,"owners_count":21088244,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["building-energy","dataset","deep-learning","lidar","point-cloud"],"created_at":"2024-08-03T01:01:44.610Z","updated_at":"2025-05-07T17:30:49.642Z","avatar_url":"https://github.com/kdmayer.png","language":"Python","funding_links":[],"categories":["Consumption"],"sub_categories":["Buildings and Heating"],"readme":"# Points for Energy Renovation (PointER): \n## A LiDAR-Derived Point Cloud Dataset of One Million English Buildings Linked to Energy Characteristics\n\n## Getting Started\n- Please see our [setup documentation](documentation/DB_CONTAINER_SETUP.md) for a step by step description.\n- Please check our [related open access paper](https://www.nature.com/articles/s41597-023-02544-x) for information about the method and the resulting dataset. \n- A dataset comprising one million building point clouds with half of the buildings linked to energy features is available [here](https://mediatum.ub.tum.de/1713501).\n\n## Prerequisites\n- Required packages are documented in the [environment.yml](environment.yml) file. \n- The [environment_for_analysis.yml](environment_for_analysis.yml) includes some more packages required for visualization and analysis.\n\n## Running the Code\n- To run an example point cloud generation, please use the [jupyter notebook](experimentation/building_pointcloud_generation.ipynb).\n- To run the point cloud generation for an entire area of interest, please see the [point cloud generation documentation](documentation/RUN_POINTCLOUD_GENERATION.md).\n- The main program can be found [here](src/building_pointcloud_main.py). Please note, that the point cloud generation process involves some upfront data preparation.\n\nThe process involves 6 steps:\n\n![img](/assets/images/overview.png)\n\nDue to the size of the point cloud files, it is recommended to set up the container on a machine with a large working memory. \nWe ran the code without problems on a machine with 48 GB, but a machine with 16 GB or more should work.\n\n## Dataset\nThe [dataset](https://mediatum.ub.tum.de/1713501) contains one million building point clouds for 16 Local Authority Districts in England.\nThese Local Authority Districts are representative for the English building stock and selected across the country (see image).\n\n![img](/assets/images/LAD_selected.png)\n\nThis is an example of a resulting point cloud:\n![img](/assets/images/example.png)\n\n## Data Sources\n- Point cloud data (.laz): [UK National LiDAR Programme](https://www.data.gov.uk/dataset/f0db0249-f17b-4036-9e65-309148c97ce4/national-lidar-programme)\n  - Open Government Licencse\n- We use [Verisk UKBuildings database](https://www.verisk.com/en-gb/3d-visual-intelligence/products/ukbuildings/) (.gpkg format) as building footprints\n  - License for personal use only\n  - Alternatively, we can use OSM data\n- [Local Authority Distric Boundaries](https://geoportal.statistics.gov.uk/) (.shp format) \n  - Open Government Licencse\n- [Unique Property Reference Numbers](https://www.ordnancesurvey.co.uk/business-government/products/open-uprn) (UPRN) including coordinates (.gpkg format) \n  - Open Government Licencse\n\n\n## Versioning\nV0.1 Initial version\n\n## Citation\n\n    @article{Krapf2023,\n      doi = {10.1038/s41597-023-02544-x},\n      url = {https://doi.org/10.1038/s41597-023-02544-x},\n      year = {2023},\n      publisher = {Springer Science and Business Media {LLC}},\n      volume = {10},\n      author = {Sebastian Krapf and Kevin Mayer and Martin Fischer},\n      title = {Points for energy renovation ({PointER}): A point cloud dataset of a million buildings linked to energy features},\n      journal = {Scientific Data}\n    }\n\n## License\nThis project is licensed under the [MIT License](LICENSE).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkdmayer%2FPointER","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkdmayer%2FPointER","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkdmayer%2FPointER/lists"}