MBZUAI focuses on AI role in extreme weather events
ABU DHABI: Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) is investigating how artificial intelligence (AI) can provide solutions for urban planning globally to help tackle the increase in extreme weather events due to climate change.
Following the recent record-breaking April 16 weather system experienced across the Gulf region, a team of MBZUAI researchers and students led by Associate Professor of Computer Vision, Dr Salman Khan, have combined AI and computer vision techniques to create an automated satellite data analysis prototype for flood assessment.
Using three localised case studies for the Palm in Dubai, the Musaffah area in Abu Dhabi, and Alburaimi region in Oman, the researchers have utilised available spatial satellite data to compare remotely sensed imagery before and after the storm. The analysis aims to provide a useful change detection tool for local municipalities and authorities in rapidly assessing the impact of heavy rains.
The analysis tool can precisely locate which roads are heavily impacted by water accumulation following extreme rain events (shown as yellow lines overlapping flooding areas), and pinpoint affected critical infrastructure such as hospitals, schools, restaurants and malls, industrial complexes, residential areas, and population clusters which may be most at danger.
Currently, the model is in its early stages and relies on AI models and free, publicly available data including Sentinel-2 imagery at 10-metre resolution and Openstreetmap, where volunteers worldwide contribute geographic information like streets, points of interest, and landmarks with community efforts to ensure data authenticity.
“From our analysis of the Dubai case study area, we found that 140 kilometers out of the 2963 kilometres was impacted by rain, including primary, residential, service, and highways,” Dr Khan said.
“In this imagery, from the color depiction, we can estimate the depth of the water. We noted a roughly six square kilometer built-up area in this study to be flooded after the April 16 rain, equating to 6.4% of the total built-up area.”
“Our hope is that such automated tools and analysis can help local authorities assess the critical locations that need more attention after extreme weather events and analyse the recovery process over time. For example, our analysis pinpoints 460 affected cafes, 75 pharmacies/ clinics, four malls, and 11 educational institutions: vital information for rescue, recovery, and future planning.”
“The case studies can be expanded to map recovery and study how long it takes for flooded areas to dry up as a part of temporal analysis.”