Flood Event Extraction from News Media to Support Satellite-Based Flood
  Insurance
Tejit Pabari Beth Tellman Giannis Karamanolakis Mitchell Thomas Max Mauerman Eugene Wu Upmanu Lall Marco Tedesco Michael S Steckler Paolo Colosio Daniel E Osgood Melody Braun Jens de Bruijn Shammun Islam
Floods cause large losses to property, life, and livelihoods across the world
every year, hindering sustainable development. Safety nets to help absorb
financial shocks in disasters, such as insurance, are often unavailable in
regions of the world most vulnerable to floods, like Bangladesh. Index-based
insurance has emerged as an affordable solution, which considers weather data
or information from satellites to create a "flood index" that should correlate
with the damage insured. However, existing flood event databases are often
incomplete, and satellite sensors are not reliable under extreme weather
conditions (e.g., because of clouds), which limits the spatial and temporal
resolution of current approaches for index-based insurance.
  In this work, we explore a novel approach for supporting satellite-based
flood index insurance by extracting high-resolution spatio-temporal information
from news media. First, we publish a dataset consisting of 40,000 news articles
covering flood events in Bangladesh by 10 prominent news sources, and inundated
area estimates for each division in Bangladesh collected from a satellite radar
sensor. Second, we show that keyword-based models are not adequate for this
novel application, while context-based classifiers cover complex and implicit
flood related patterns. Third, we show that time series extracted from news
media have substantial correlation Spearman's rho$=0.70 with satellite
estimates of inundated area. Our work demonstrates that news media is a
promising source for improving the temporal resolution and expanding the
spatial coverage of the available flood damage data.