Critical Image Identification via Incident-Type Definition Using Smartphone Data during an Emergency: A Case Study of the 2020 Heavy Rainfall Event in Korea

Abstract

In unpredictable disaster scenarios, it is important to recognize the situation promptly and take appropriate response actions. This study proposes a cloud computing-based data collection, processing, and analysis process that employs a crowd-sensing application. Clustering algorithms are used to define the major damage types, and hotspot analysis is applied to effectively filter critical data from crowdsourced data. To verify the utility of the proposed process, it is applied to Icheon-si and Anseong-si, both in Gyeonggi-do, which were affected by heavy rainfall in 2020. The results show that the types of incident at the damaged site were effectively detected, and images reflecting the damage situation could be classified using the application of the geospatial analysis technique. For 5 August 2020, which was close to the date of the event, the images were classified with a precision of 100% at a threshold of 0.4. For 24–25 August 2020, the image classification precision exceeded 95% at a threshold of 0.5, except for the mudslide mudflow in the Yul area. The location distribution of the classified images showed a distribution similar to that of damaged regions in unmanned aerial vehicle images.

Use of Mobile Crowdsensing in Disaster Management: A Systematic Review, Challenges, and Open Issues

“…Di Felice and Iessi [ 46 ] propose a software service to reduce the processing times of Tweets during emergencies, Ludwig et al [ 23 ] introduce a web application using mobile crowdsensing that combines physical and digital activities to respond to rescue authorities’ information requests, Choi et al [ 47 ] presents a cloud-based data process that employs a mobile crowdsensing application to detect images as per damage type, Tripathi and Singh [ 48 ] present a data fusion model of human virtual sensors and actual traditional sensors for disaster response.…”

“…More information on the environment should be collected through smartphone sensors to ensure that there is a building collapse. Choi et al [ 47 ] discuss identifying major damage locations and types of the incident at the damaged site through crowdsensed image data using clustering algorithms. The study presents a cloud-based data collection, processing, and analysis process that employs a mobile crowdsensing application.…”