Data and code

Open resources from our group

We publish most of our code and data on our Github account.

Please see below for a list of resources and projects, especially lab-grown datasets that we released openly. On this page, we also include outputs led by collaborators, in which we were involved.

On a related note, you may be interested in our comprehensive survey on open-source software, published as a review paper in CEUS.

In general, all code and data are released under a liberal licence, enabling you unrestricted use as long as you attribute them. If you use the code and/or the data for presentations and publications, we kindly ask you to cite the related paper(s) and credit our work. We have provided guidelines to do so in each resource.

While a great deal of effort has been put into each project, they are not free of errors, and we cannot be held responsible for the use of code and/or data and any issues that may arise.

Feel free to contact us for more information, report bugs and errors, or simply to inform us what are you using the data for. We would be pleased to hear how others are using our research. In you are interested in collaborating with us, please get in touch with the lead developer of each resource.

Global Urban Road Network Patterns

Short description:Deep learning-based analysis of the urban morphology around the world
Lead developer:Chen Wangyang
Further reading:Readme on the Github repo. Paper in progress, stay tuned
Code: Github repo
Main data source(s):OpenStreetMap

SVIQC – Street View Imagery Quality Checker

Short description:A toolkit to examine the quality of street view imagery
Lead developer:Hou Yujun
Further reading:Readme on the Github repo. Paper in progress, stay tuned
Code: Github repo
Main data source(s):Mapillary

Visual soundscapes

Short description:Predicting the urban soundscape from street view imagery
Lead developer:Tianhong Zhao
Further reading:Readme on the Github repo. Paper in progress, stay tuned
Code: Github repo

GBMI – Global Building Morphology Indicators

GBMI London

Short description:Billions of built form metrics of selected urban areas around the world, together with a database solution to compute them
Lead developer:Yoong Shin Chow
FormatsCSV, Shapefile, Geopackage, GeoTIFF
Further reading:Please read more at our website or in the paper
Download:Project website
Code:The code used to generate the dataset is available in the Github repo
Main data source(s):OpenStreetMap, GADM
Coverage:Dozens of urban areas around the world
Citation:
Click to view the BibTeX entry

@article{2022_ceus_gbmi, author = {Biljecki, Filip and Chow, Yoong Shin}, doi = {10.1016/j.compenvurbsys.2022.101809}, journal = {Computers, Environment and Urban Systems}, pages = {101809}, title = {Global Building Morphology Indicators}, volume = {95}, year = {2022} }

Roofpedia – solar and green roofs around the world

Density of solar panels in Singapore

Short description:Locations of buildings that have installed photovoltaics or greenery on their rooftops
Lead developer:Abraham Noah Wu
FormatsGeoJSON
Further reading:Please read more at our website or in the paper
Download: Github repo
Code:The code used to generate the dataset is available in the same Github repo
Main data source(s):Various satellite imagery, OpenStreetMap. All open data
Coverage:17 cities around the world
Citation:
Click to view the BibTeX entry

@article{roofpedia, author = {Abraham Noah Wu and Filip Biljecki}, doi = {10.1016/j.landurbplan.2021.104167}, journal = {Landscape and Urban Planning}, pages = {104167}, title = {Roofpedia: Automatic mapping of green and solar roofs for an open roofscape registry and evaluation of urban sustainability}, url = {https://doi.org/10.1016/j.landurbplan.2021.104167}, volume = {214}, year = 2021 }

Semantic Riverscapes

A section of the Grand Canal in Tianjin

Short description:A semantically annotated UAV oblique image dataset covering an urban river landscape
Lead developer:Junjie Luo
Further reading:Paper in progress… Stay tuned
Download: Github repo
Main data source(s):Own data collection (UAV) and manual labelling
Coverage:Tianjin (China)

Classification of Urban Morphology with Deep Learning

Short description:Software to generate diagrams of the urban form at the city-scale and classify them using deep learning
Lead developer:Chen Wangyang
Further reading:Please read more in the paper
Code: Github repo
Citation:
Click to view the BibTeX entry

@article{2021_ceus_dl_morphology, author = {Wangyang Chen and Abraham Noah Wu and Filip Biljecki}, doi = {10.1016/j.compenvurbsys.2021.101706}, journal = {Computers, Environment and Urban Systems}, pages = {101706}, title = {Classification of Urban Morphology with Deep Learning: Application on Urban Vitality}, url = {https://doi.org/10.1016/j.compenvurbsys.2021.101706}, volume = {90}, year = 2021 }

GANmapper: geographical content translation

Short description:A building footprint generator using Generative Adversarial Networks from sparse data such as street networks
Lead developer:Abraham Noah Wu
Further reading:Please read more in the IJGIS paper
Code: Github repo
Citation:
Click to view the BibTeX entry

@article{2022_ijgis_ganmapper, year = {2022}, author = {Wu, Abraham Noah and Biljecki, Filip}, title = {{GANmapper: geographical data translation}}, journal = {International Journal of Geographical Information Science}, doi = {10.1080/13658816.2022.2041643} }

3D dataset of all public housing (HDB) buildings in Singapore

HDB 3D city model, Singapore

Short description:About 12k semantically rich 3D buildings in Singapore in CityJSON and OBJ
Lead developer:Filip Biljecki
FormatsCityJSON, OBJ
Further reading:Please read more here in our blog post or in the paper
Download: Github repo
Code:The code used to generate the dataset is available in a separate Github repo
Main data source(s):HDB, OpenStreetMap, OneMap. All open data
Coverage:Singapore
Citation:
Click to view the BibTeX entry

@article{2020_3dgeoinfo_3d_asean, author = {Biljecki, F.}, doi = {10.5194/isprs-annals-vi-4-w1-2020-37-2020}, journal = {ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences}, pages = {37–44}, title = {Exploration of open data in Southeast Asia to generate 3D building models}, volume = {VI-4/W1-2020}, year = {2020} }

With our friendly collaborators

EUropean BUilding stock Characteristics in a Common and Open database

Short description:EUBUCCO is a scientific database of individual building footprints for 206 million buildings across the 27 European Union countries and Switzerland, together with three main attributes – building type, height and construction year – included for respectively 45%, 74%, 24% of the buildings.
Lead developer:Nikola Milojevic-Dupont and Felix Wagner, 3D Geoinformation, Mercator Research Institute for Global Commons and Climate Change and TU Berlin
Code: Github repo
Data:Zenodo

3D building metrics for urban morphology

Short description:3D Building Metrics. Elevating geometric analysis for urban morphology, solar potential, CFD etc to the next level
Lead developer:Anna Labetski and Stelios Vitalis, 3D Geoinformation, TU Delft
Code: Github repo

3dfier: automatic reconstruction of 3D city models

Short description:Takes 2D GIS datasets (e.g. topographical datasets) and “3dfies” them (as in “making them three-dimensional”) by lifting every polygon to 3D
Lead developer:3D Geoinformation, TU Delft
Code: Github repo
Further reading:Please read more in the paper
Citation:
Click to view the BibTeX entry

@article{2021_joss_3dfier, author = {Ledoux, Hugo and Biljecki, Filip and Dukai, Balázs and Kumar, Kavisha and Peters, Ravi and Stoter, Jantien and Commandeur, Tom}, doi = {10.21105/joss.02866}, journal = {Journal of Open Source Software}, number = {57}, pages = {2866}, title = {3dfier: automatic reconstruction of 3D city models}, volume = {6}, year = {2021} }

ifc2indoorgml

Short description:A tool allowing to generate IndoorGML files from IFC input models
Lead developer:Abdoulaye Diakite, GRID, University of New South Wales
Code: Github repo
Further reading:Paper
Citation:
Click to view the BibTeX entry

@article{2022_isprs_ifc2indoorgml, author = {Diakite, AA and Díaz-Vilariño, L and Biljecki, F and Isikdag, Ü and Simmons, S and Li, K and Zlatanova, S}, doi = {10.5194/isprs-archives-xliii-b4-2022-295-2022}, journal = {Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.}, pages = {295–301}, title = {ifc2indoorgml: An open-source tool for generating IndoorGML from IFC}, volume = {XLIII-B4-2022}, year = {2022} }

AIDA - Annotated Image Database of Architecture

Short description:A repository of architectural photographs worldwide, labelled with a vast list of hierarchical categories and a series of auxiliary annotations
Lead developer:Chen Jielin
Further reading:Paper
Download:Harvard Dataverse
Main data source(s):ArchDaily
Citation:
Click to view the BibTeX entry

@inproceedings{2021_caadria_aida, author = {Chen, Jielin and Stouffs, Rudi and Biljecki, Filip}, booktitle = {Proceedings of the 26th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) 2021}, pages = {161–170}, title = {Hierarchical (Multi-Label) Architectural Image Recognition and Classification}, volume = {1}, year = {2021} }