  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.
