Abstract
Abstract. Social sensing and satellite imagery are named as the top emerging data sources for disaster management. There is a wealth of data, both in quantity and quality that can be extracted from social media platforms such as Twitter, given that the content published by users is generally in real-time and includes a geotag or toponym. To reduce costs, risks, and time, performing reconnaissance using remote sources of information is highly suggested. This study explores how social media data can be used to supplement satellite imagery in post-disaster remote reconnaissance using the January 2020 Taal Volcano Eruption in the Philippines. Tweets about the volcanic eruption were scraped, and ashfall-affected locations mentioned in tweet content were extracted using Named Entity Recognition (NER). To visualize the progression of the tweeted locations, dot density maps and hotspot maps were generated. Additionally, a potential ashfall extent map was generated from processed DIWATA-2 satellite imagery using Support Vector Machine (SVM) classification. An intersection of both dot density map and ashfall extent map was performed for comparative analysis of both data. Validation was carried out by matching the ashfall-affected locations with ground reports from local government offices and news reports. The use of social media data complements satellite image classification in the detection of disaster damage for a quick and cost-efficient remote reconnaissance. This information can be utilized by rescue teams for faster emergency response and relief operations during and after a disaster.
Highlights
The Philippines is hit by a slew of natural disasters every year
Social sensing as a source will most likely address the need for remote reconnaissance by reducing the time delays, risks, logistics, and costs associated with dispatching survey teams to the site
The DIWATA-2 satellite imagery was taken on January 27, 2020, two weeks after the volcanic eruption
Summary
The Philippines is hit by a slew of natural disasters every year. The most difficult task in the aftermath of such events is determining the extent of the damage and destruction on the ground. Fieldwork and sending survey teams to disaster-affected areas to assess disaster damage are typically costly, risky, and time-consuming (Dashti et al, 2014). While remote sensing has become the standard for Earth observation, satellite data can have quality limitations, time delays, and information gaps (Havas et al, 2017). Social media data is leading the way in emerging data acquisition trends for disaster resilience (Yu et al, 2018). With real-time broadcasting of an event by social sensors, social media data can be used to track and monitor the progression of disasters in affected areas in an efficient and costeffective manner. Social sensing as a source will most likely address the need for remote reconnaissance by reducing the time delays, risks, logistics, and costs associated with dispatching survey teams to the site
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