Utilizing crowdsourced data for timely investigation of catastrophic landslide accidents: a case study of the coal mine collapse in inner Mongolia, China

Catastrophic landslide accidents are a significant global issue, resulting in considerable loss of life and property damage. However, traditional landslide survey methods are typically time-consuming and require expensive equipment, which hinders timely responses to the need for landslide rescue and accident investigation. This study proposes a method for utilizing timely crowdsourced data in the preliminary investigation of catastrophic landslide accidents. Specifically, we examine the case of the Xinjing Landslide in Inner Mongolia, China, which occurred on February 23, 2023. We demonstrate the ability of crowdsourced data to provide real-time information about landslide occurrence, size, movement direction, and speed. Moreover, we analyze the possible triggers of the landslide based on the gathered data. Our findings suggest that prompt crowdsourced data can provide valuable information about landslides and potentially save lives through timely responses. This study emphasizes the potential of timely crowdsourced data in enhancing landslide investigation and calls for further research into the integration of crowdsourced data with traditional monitoring methods.