Krishnaprasad Thirunarayan (Advisor), Amit Sheth (Committee Member), Valerie Shalin (Committee Member)
Master of Science (MS)
With the surge in digital information systems, there is a data deluge from various sources that can be analyzed and integrated to produce relevant, reliable and actionable information, for better decision making. We employ multi-modal data (i.e., unstructured text, gazetteers, and imagery) for an aggregate level analysis and location-centric demand/request matching in the context of disaster relief. After classifying the Need expressed in a tweet (the WHAT), we leverage OpenStreetMap to geolocate that Need on a computationally accessible map of the local terrain (the WHERE) populated with location features such as hospitals and housing. Further, our novel use of flood mapping based on satellite images of the affected area supports the elimination of candidate resources that are not accessible by road transportation. The resulting map-based visualization of the tool DisasterRecord: Disaster response and relief coordination, serves two levels of users. A community level user (first-responders) can visualize aggregated summary of a selected geographical area and an individual level user can identify current needs and available resources in their geographic proximity. Additionally, our pluggable, modularized pipeline (DisasterRecord) is extensible so that additional functionality can be layered on top of the map. The integration of disaster-related tweets, imagery and pre-existing knowledge-base resources (gazetteers) reduce decision-making latency and enhance resiliency by assisting decision-makers and first responders involved with relief effort coordination.
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
Copyright 2018, some rights reserved. My ETD may be copied and distributed only for non-commercial purposes and may not be modified. All use must give me credit as the original author.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.