Isabelle Tingzon

I’m Issa, a data science consultant at UNICEF, a Disaster and Climate Risk Data Fellow at the World Bank/GFDRR, a core member of Climate Change AI, and a former volunteer at Women Who Code.

My work focuses mainly on the applications of machine learning, computer vision, and Earth observation for socioeconomic development, humanitarian assistance, climate resilience, and disaster response.

News

Dec 9, 2024	Our paper, “Large-scale School Mapping using Weakly Supervised Deep Learning for Universal School Connectivity” was accepted at AAAI 2025 Special Track on AI for Social Impact (AISI)! 🎉
Nov 18, 2024	Our work, “Mapping Buildings Using Deep Learning for Climate Resilience in the Caribbean”, at the World Bank/GFDRR was highlighted as a Case Study in the UNFCCC Technology Executive Committee Information Note on Artificial Intelligence for Climate Action in Developing Countries: Opportunities, Challenges and Risks launched at COP29!
Oct 15, 2024	Our work, “Advancing Universal School Connectivity through AI-enabled School Mapping”, at UNICEF Giga won the IEEE Connecting the Unconnected (CTU) Challenge for Best Innovative Technology Application at the CTU Summit 2024 in Dubai!🏆

Latest Tutorials

Aug 18, 2023	Rooftop Classification from Drone Imagery for DRR (Part 2/2)
Aug 8, 2023	Building Footprint Delineation for Disaster Risk Reduction and Response (Part 1/2)
Jul 8, 2023	Automating Crop Field Delineation using Python GIS (Part 2/2)

Featured Publications

Fusing VHR Post-Disaster Aerial Imagery and LiDAR Data for Roof Classification in the Caribbean

Isabelle Tingzon, Nuala Margaret Cowan, and Pierre Chrzanowski

In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, Oct 2023

Bib PDF Code

@inproceedings{Tingzon_2023_ICCV,
  author = {Tingzon, Isabelle and Cowan, Nuala Margaret and Chrzanowski, Pierre},
  title = {Fusing VHR Post-Disaster Aerial Imagery and LiDAR Data for Roof Classification in the Caribbean},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
  month = oct,
  year = {2023},
  pages = {3740-3747},
}

Interpretable poverty mapping using social media data, satellite images, and geospatial information

Chiara Ledesma, Oshean Lee Garonita, Lorenzo Jaime Flores, and 2 more authors

In Machine Learning for the Developing World (ML4D) NeurIPS Workshop, Oct 2020

arXiv Bib Slides

@inproceedings{ledesma2020interpretable,
  title = {Interpretable poverty mapping using social media data, satellite images, and geospatial information},
  author = {Ledesma, Chiara and Garonita, Oshean Lee and Flores, Lorenzo Jaime and Tingzon, Isabelle and Dalisay, Danielle},
  booktitle = {Machine Learning for the Developing World (ML4D) NeurIPS Workshop},
  year = {2020},
}