The absence of comprehensive situational awareness information poses a significant challenge for humanitarian organizations during their response efforts. We present Flood Insights, an end-to-end system that ingests data from multiple non-traditional data sources such as remote sensing, social sensing, and geospatial data. We employ state-of-the-art natural language processing and computer vision models to identify flood exposure, ground-level damage and flood reports, and most importantly, urgent needs of affected people. We deploy and test the system during a recent real-world catastrophe, the 2022 Pakistan floods, to surface critical situational and damage information at the district level. We validated the system's effectiveness through geographic regression analysis using official ground-truth data, showcasing its strong performance and explanatory power. Moreover, the system was commended by the United Nations Development Programme stationed in Pakistan, as well as local authorities, for pinpointing hard-hit districts and enhancing disaster response.

The devastating 2022 floods in Pakistan resulted in a catastrophe impacting millions of people and destroying thousands of homes. While disaster management efforts were taken, crisis responders struggled to understand the country-wide flood extent, population exposure, urgent needs of affected people, and various types of damage. To tackle this challenge, we leverage remote and social sensing with geospatial data using state-of-the-art machine learning techniques for text and image processing. Our satellite-based analysis over a one-month period (25 Aug–25 Sep) revealed that 11.48% of Pakistan was inundated. When combined with geospatial data, this meant 18.9 million people were at risk across 160 districts in Pakistan, with adults constituting 50% of the exposed population. Our social sensing data analysis surfaced 106.7k reports pertaining to deaths, injuries, and concerns of the affected people. To understand the urgent needs of the affected population, we analyzed tweet texts and found that South Karachi, Chitral and North Waziristan required the most basic necessities like food and shelter. Further analysis of tweet images revealed that Lasbela, Rajanpur, and Jhal Magsi had the highest damage reports normalized by their population. These damage reports were found to correlate strongly with affected people reports and need reports, achieving an R-Square of 0.96 and 0.94, respectively. Our extensive study shows that combining remote sensing, social sensing, and geospatial data can provide accurate and timely information during a disaster event, which is crucial in prioritizing areas for immediate and gradual response.