Natural Disaster Analysis using Satellite Imagery and Social-Media Data for Emergency Response Situations

TL;DR

This paper presents a novel approach combining satellite imagery and Twitter data analysis to enhance real-time disaster assessment and relief efforts, demonstrated through case studies of floods in Kerala and Mississippi.

Contribution

It introduces an integrated framework utilizing segmented satellite images and Twitter data mapping for comprehensive disaster analysis and response.

Findings

Effective identification of affected regions through satellite image segmentation.

Real-time Twitter data integration provides situational awareness.

Validated approach on flood case studies in Kerala and Mississippi.

Abstract

Disaster Management is one of the most promising research areas because of its significant economic, environmental and social repercussions. This research focuses on analyzing different types of data (pre and post satellite images and twitter data) related to disaster management for in-depth analysis of location-wise emergency requirements. This research has been divided into two stages, namely, satellite image analysis and twitter data analysis followed by integration using location. The first stage involves pre and post disaster satellite image analysis of the location using multi-class land cover segmentation technique based on U-Net architecture. The second stage focuses on mapping the region with essential information about the disaster situation and immediate requirements for relief operations. The severely affected regions are demarcated and twitter data is extracted using keywords respective to that location. The extraction of situational information from a large corpus of raw tweets adopts Content Word based Tweet Summarization (COWTS) technique. An integration of these modules using real-time location-based mapping and frequency analysis technique gathers multi-dimensional information in the advent of disaster occurrence such as the Kerala and Mississippi floods that were analyzed and validated as test cases. The novelty of this research lies in the application of segmented satellite images for disaster relief using highlighted land cover changes and integration of twitter data by mapping these region-specific filters for obtaining a complete overview of the disaster.