Isabelle Tingzon(She/Her)UNICEF
Isabelle Tingzon is a Data Science Consultant at UNICEF and a Disaster and Climate Risk Data Fellow at the World Bank under the Global Facility for Disaster Reduction and Recovery (GFDRR). Her work focuses on the applications of machine learning and Earth observation for socioeconomic development, humanitarian assistance, and disaster response. Her most recent work includes integrating multimodal Earth Observation data such as drone images, LiDAR data, and street-view photos to generate critical exposure datasets in Small Island Developing States (SIDS) under the Digital Earth for Resilient Housing and Infrastructure Project at the World Bank. She also serves as the Technical Lead for AI/ML at Women Who Code Manila and is a Core Member of Climate Change AI. |
 |
TalksMapping Housing Stock Characteristics from Drone Images for Climate Resilience in the CaribbeanAnnotations via NIDM-Terms Fosters Improved Search of OpenNeuro Datasets22-Apr-24Comprehensive housing stock information is crucial for informing the development of climate-resilience strategies aiming to reduce the adverse impacts of extreme climate hazards in high-risk regions like the Caribbean. In this study, we propose an end-to-end workflow for rapidly generating critical baseline exposure data using very high-resolution drone imagery and deep learning techniques. Specifically, our work leverages the Segment Anything Model (SAM) and convolutional neural networks (CNN) to automate the generation of building footprints and roof classification maps. We evaluate the cross-country generalizability of the CNN models to determine how well models trained in one geographical context can be adapted to another. Finally, we discuss our initiatives for training and upskilling government staff, community mappers, and disaster responders in the use of geospatial technologies. Our work emphasizes the importance of local capacity building in the adoption of AI and Earth Observation for climate resilience in the Caribbean. |