# Flood Event Extraction from News Media to Support Satellite-Based Flood Insurance

arXiv: https://arxiv.org/abs/2312.14943 · HTML: https://mlsanity.com/paper/2312.14943/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 41 references that are in the map.
- First posted on arXiv 5 Dec 2023.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Survey Of Cross-lingual Word Embedding Models](https://mlsanity.com/paper/1706.04902/) (2017)
- [Processing Social Media Messages in Mass Emergency: A Survey](https://mlsanity.com/paper/1407.7071/) (2014)
- [Twitter as a Lifeline: Human-annotated Twitter Corpora for NLP of Crisis-related Messages](https://mlsanity.com/paper/1605.05894/) (2016)
- [Domain Adaptation with Adversarial Training and Graph Embeddings](https://mlsanity.com/paper/1805.05151/) (2018)
- [Detecting Perceived Emotions in Hurricane Disasters](https://mlsanity.com/paper/2004.14299/) (2020)
- [Automated Extraction of Socio-political Events from News (AESPEN): Workshop and Shared Task Report](https://mlsanity.com/paper/2005.06070/) (2020)
- [Social Sensing of Floods in the UK](https://mlsanity.com/paper/1711.04695/) (2017)
- [Intelligent Disaster Response via Social Media Analysis - A Survey](https://mlsanity.com/paper/1709.02426/) (2017)
- [NARMADA: Need and Available Resource Managing Assistant for Disasters and Adversities](https://mlsanity.com/paper/2005.13524/) (2020)

## Built on by (most cited first, 1 of 1)

- [Harnessing Large Language Models for Disaster Management: A Survey](https://mlsanity.com/paper/2501.06932/) (2025)

## Abstract (arXiv)

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.