Remote sensing for disaster response
Remote sensing data is an invaluable resource in providing critical insights before and after natural and human-made disasters. Prior to disasters, “primary” remote sensing data in the forms of optical imagery and SAR data can be used measure potential various hazards (e.g. floods, droughts) and “secondary” remote sensing data, such as gridded population datasets, can be used to measure exposure to such hazards. Post disaster situations, satellite data can be used to generate rapid insights that could help guide efforts of first responders. In this talk, in depth examples will be shared of how remote sensing data has been used both prior and post disaster situations. The focus will be on the Beirut 2020 Explosion and how a collaboration between local stakeholders and NASA JPL led to one of the most data rich disasters recorded in modern history. Key projects: SAR data was initially used to generate a damage proxy map, a social media campaign to collect geo-located photos created a rich database used for ground truthing to further improve the assessment models, and finally a drone mapping project using advanced photogrammetry resulted in a very high resolution 3D model and point cloud of the entire impacted area.
Ms. Melda SalhabCo-founder of Open Map, Lebanon
Melda is the co-founder of Open Map Lebanon and a doctoral researcher at the Centre for Advanced Spatial Analysis, UCL, London, UK. Her doctoral research is focused on extracting economic insights from remote sensing datasets, at the country and sub-country scale, but with global coverage. Through Open Map Lebanon, she supported disaster recovery post the Beirut 2020 explosion, by leveraging remote sensing and crowdsourced data. Previously, Melda was a consultant at the World Bank where she assessed global population exposed to multiple types of flood risk using satellite data. Previously, she was a management consultant at Monitor Deloitte Middle East. Melda has a BA in Economics, an MSc in Urban Economic Development, and an MRes in Spatial Data Science and Visualization.