Crowdsourcing (CS) and Remote Sensing (RS) data in Detecting Street Blockages in the Aftermath of an Earthquake: Bam Earthquake, 2003, Iran

this paper explores the effectiveness of Crowdsourcing (CS) and Remote Sensing (RS) data in detecting street blockages in the aftermath of an earthquake. Building destruction, bridge collapse, and hazardous zones can block a street and congest the traffic flow. Consequently, deployment of disaster response operations to the disaster zone could be delayed. This research was conducted through a designed experiment in the study area of Bam City that experienced a massive earthquake in 2003 and 396 people were interviewed regarding street’s blockages in the aftermath of the earthquake. This data as Crowd Sourced (CS) data and Remote Sensing (RS) data were considered to extract streets’ blockages in the study area. The number of blockages based on CS data and RS data were 289 and 412, respectively. Comparison of these results with the actual street blockage locations showed 76.3% and 53.9% accuracy using CS and RS data, respectively. Combination of CS and RS data demonstrated 87.4% accuracy in detecting street blockages in compare to actual street blockage locations showing CS data could be applied as a complementary source of information in detecting street blockages and increase the effectiveness of RS data in routing disaster response operations

کلیدواژه ها:

Crowdsourcing (CS) ، Remote Sensing (RS) ، Street Blockage ، and Bam Earthquake