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.

翻译：灾害管理因其显著的经济、环境和社会影响，已成为最具前景的研究领域之一。本研究聚焦于分析与灾害管理相关的多类数据（灾前/灾后卫星图像及推特数据），以深入探究不同地点的应急需求。研究分为两个阶段：卫星图像分析与推特数据分析，随后通过地理位置进行整合。第一阶段采用基于U-Net架构的多类土地覆盖分割技术，对目标地点进行灾前/灾后卫星图像分析；第二阶段通过灾害情境关键信息和救援行动即时需求对区域进行映射。严重受灾区域被标注后，利用与该位置相关的关键词提取推特数据。从大量原始推文语料库中提取情境信息时，采用基于内容词的推文摘要（COWTS）技术。通过实时位置映射与频率分析技术整合上述模块，可在灾害发生时获取多维信息——以喀拉拉邦和密西西比河洪水作为测试案例进行的分析与验证证实了该方法的有效性。本研究的创新性在于：利用土地覆盖变化显著区域的分割卫星图像指导灾害救援，并通过映射区域特定过滤器整合推特数据，从而获取灾害的全貌分析。