Deep Conditional Census-Constrained Clustering (DeepC4) for Large-scale Multi-task Disaggregation of Urban Morphology
Creators
-
1.
University of Cambridge
- 2. UKRI Centre for Doctoral Training (CDT) in the Application of Artificial Intelligence to the study of Environmental Risks (AI4ER)
- 3. Cambridge University Centre for Risk in the Built Environment (CURBE)
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4.
Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)
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5.
University of Bonn
Description
This Zenodo record contains the datasets of our research (Spatial Disaggregation of Rwandan Building Exposure and Vulnerability via Weakly Supervised Conditional Census-Constrained Clustering (C4) using Earth Observation Data) submiited for American Geophysical Union Annual Meeting 2024 to be held in Washington, D.C. on 9th-13th of December 2024. The GitHub repository of MATLAB & Python codes can be accessed here: github.com/riskaudit/rwa. If you have any inquiries or would like to access any related materials, please feel free to visit my website (joshuadimasaka.com) or our project website (riskaudit.github.io), follow our project's GitHub repository (github.com/riskaudit), or send an email to jtd33@cam.ac.uk.
History of Versions:
- 1.0.0 - Initial upload (C4)
- 1.1.0 - Updated output_yMapsAndQGISStyles.zip (DeepC4)
Notes (English)
#AGU24 Presentation Details:
✅ NH23B - Advancing Building & Population Inventories to Support Equity & Inclusion
📍 Hall B-C (Poster Hall), Walter E. Washington Convention Center
🗓️ Tuesday, 10 Dec 2024
🕚 13:40 - 17:30 EST
🌐 https://agu.confex.com/agu/agu24/meetingapp.cgi/Paper/1610379
Files
graphicalabstract.pdf
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Additional details
Funding
Software
- Repository URL
- https://github.com/riskaudit/DeepC4
- Programming language
- MATLAB, Python
- Development Status
- Active
Biodiversity
- County
- Rwanda