Published 2024 | Version 1.1.0
Poster Open

Global Mapping of Exposure and Physical Vulnerability Dynamics in Least Developed Countries using Remote Sensing and Machine Learning

  • 1. ROR icon 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)
  • 4. German Aerospace Center
  • 5. ROR icon University of Bonn

Description

This Zenodo record contains the poster presented at 2nd Machine Learning for Remote Sensing Workshop12th International Conference on Learning Representations (ICLR) in Vienna, Austria, on 11th of May 2024. The GitHub repository of Python codes can be accessed here: github.com/riskaudit/OpenSendaiBench. 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.

Relevant download links:

Updates:

  • Minor Update for Version 1.1.0 (May 22, 2024): We added the 10-minute slide presented at the Cambridge Hazard & Risk Research Day on the 21st of May 2024 at the University of Cambridge.

Files

ICLR24_ML4RS_JDCGES_POSTER.pdf

Files (122.3 MB)

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Additional details

Related works

Cites
Dataset: 10.5281/zenodo.10840484 (DOI)
Is variant form of
Conference paper: arXiv:2404.01748 (arXiv)

Funding

UK Research and Innovation
UKRI Centre for Doctoral Training in Application of Artificial Intelligence to the study of Environmental Risks (AI4ER) EP/S022961/1

Software

Repository URL
https://github.com/riskaudit/OpenSendaiBench
Programming language
Python
Development Status
Wip