The Use of Social Media in Guiding Satellite Image Collection During Disaster Events

Authors: Michael Hodgson*, University of South Carolina, Zhenlong Li, University of South Carolina, Silvia E Piovan, University of Padova, Bruce A Davis, NASA
Topics: Geographic Information Science and Systems, Hazards, Risks, and Disasters, Remote Sensing
Keywords: social media, satellite, image collection, disaster
Session Type: Paper
Day: 4/10/2018
Start / End Time: 10:00 AM / 11:40 AM
Room: Napoleon D1, Sheraton 3rd Floor
Presentation File: No File Uploaded

The collection of satellite remotely during the disaster response phase of the hazard cycle requires rapid decisions with limited opportunities. High resolution satellite-sensor combinations have one opportunity each two to three-day cycle for collecting an image at a location. Understanding where the impact area and how bad the impact is of highest priority in the response phase. The use of ancillary spatial data in guiding the collection mission is extremely important. However, the availability of ancillary spatial data providing information on the impact area is largely dependent on governmental sources operating with few staff. However, a large body of informed sources is available through real-time social media. Social media enables wide-scale interaction where primary data becomes collectively resourceful, self-policing, and provides disaster areas information in a useful timeframe. For example, micro-level disaster information (e.g. site specific damage and flood water height) can be captured in real-time, enabling rapid assessment of disaster impact. In this research we examined the utility of Twitter data in guiding satellite image collection during disaster events. By examining the spatiotemporal distributions of relevant tweets and analyzing the content of Twitter messages, we are able to delineate the disaster impact area(s) and assess the magnitude of the impact in near-real time. The identified impact areas and the impact magnitude are then fed into an optimization model for satellite image acquisition planning. The method is evaluated with several nature disasters including the 2017 California wildfires, 2017 Harvey floods, and 2015 South Carolina floods.

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