4 research outputs found
Fusing VHR Post-disaster Aerial Imagery and LiDAR Data for Roof Classification in the Caribbean
Accurate and up-to-date information on building characteristics is essential
for vulnerability assessment; however, the high costs and long timeframes
associated with conducting traditional field surveys can be an obstacle to
obtaining critical exposure datasets needed for disaster risk management. In
this work, we leverage deep learning techniques for the automated
classification of roof characteristics from very high-resolution orthophotos
and airborne LiDAR data obtained in Dominica following Hurricane Maria in 2017.
We demonstrate that the fusion of multimodal earth observation data performs
better than using any single data source alone. Using our proposed methods, we
achieve F1 scores of 0.93 and 0.92 for roof type and roof material
classification, respectively. This work is intended to help governments produce
more timely building information to improve resilience and disaster response in
the Caribbean.Comment: ICCV 2023 Workshop on Artificial Intelligence for Humanitarian
Assistance and Disaster Respons
Mapping New Informal Settlements using Machine Learning and Time Series Satellite Images: An Application in the Venezuelan Migration Crisis
Since 2014, nearly 2 million Venezuelans have fled to Colombia to escape an
economically devastated country during what is one of the largest humanitarian
crises in modern history. Non-government organizations and local government
units are faced with the challenge of identifying, assessing, and monitoring
rapidly growing migrant communities in order to provide urgent humanitarian
aid. However, with many of these displaced populations living in informal
settlements areas across the country, locating migrant settlements across large
territories can be a major challenge. To address this problem, we propose a
novel approach for rapidly and cost-effectively locating new and emerging
informal settlements using machine learning and publicly accessible Sentinel-2
time-series satellite imagery. We demonstrate the effectiveness of the approach
in identifying potential Venezuelan migrant settlements in Colombia that have
emerged between 2015 to 2020. Finally, we emphasize the importance of
post-classification verification and present a two-step validation approach
consisting of (1) remote validation using Google Earth and (2) on-the-ground
validation through the Premise App, a mobile crowdsourcing platform