# A New Method for Large-Scale Landslide Classification from Satellite Radar
## 1 Introduction  
The majority of continental earthquakes occur in mountainous regions, where they can trigger thousands of landslides over areas of several tens of thousands of km\({}^{2}\)[1; 2]. These landslides are responsible for more deaths globally than any other secondary earthquake hazard [3]. Earthquake-triggered landslides cause damage to power, transportation and communication infrastructure, isolating remote communities and disrupting emergency response efforts, and may cause further hazards such as dam-outburst floods [4; 5]. Information on where landslides have occurred is therefore essential for emergency response coordination and for directing site-specific investigations on the ground, e.g., [6; 7]. This information must be rapidly generated and communicated in order to limit delays to resource allocation and therefore be of practical value [8; 9]. The information may take several forms, from detailed maps oflandslide locations, with individual events recorded either as points, polylines or polygons, to landslide density maps that identify regions that have experienced high numbers or large areas of landslides [9]. Following past earthquakes that triggered extensive landsl, landslide information products have been generated too slowly for use in emergency response. The most common method, which is to identify triggered landslides through comparison of pre-event and post-event optical satellite imagery, is labour-intensive and reliant on the acquisition of cloud-free imagery. In some cases, automation can alleviate the labour-intensive nature of this process, e.g., [10], but cloud-cover often presents an insurmountable barrier to rapid production of landslide maps using optical imagery. This delays the supply of information to emergency response coordinators, as was the case in the aftermath of the 2015 Nepal and 2016 Ecuador earthquakes [9; 11]. When cloud-free optical satellite imagery is unavailable, emergency response coordinators must rely on ground-based observations, which may not have wide or homogeneous areal coverage, and on the outputs from predictive models. Such models estimate where landslides are likely to have occurred based on factors such as peak ground acceleration, topographic slope and proximity to rivers or active faults, e.g., [12; 13; 14]. However, these models are generally static in time, empirical in nature, and are strongly dependent on input data quality. Peak ground acceleration, for example, may be poorly constrained immediately following the earthquake [15]. Additionally, the models may fail to capture differences in susceptibility for different regions, as illustrated by the significant differences in triggered landsl between the 2008 M\({}_{\text{w}}\) 7.9 Wenchuan earthquake and the 2015 Mw 7.8 Gorkha earthquake [1]. The inclusion of observed landslide data in these models improves their predictive skill but the improvement is limited if these data are clustered, as they necessarily must be if mapped using optical satellite imagery through small gaps in cloud-cover [14]. Synthetic Aperture Radar (SAR) satellite imagery, which uses active emission and sensing of electromagnetic radiation in the microwave rather than the visible light spectrum, can acquire useable imagery in cloudy conditions as radar is able to penetrate cloud cover. SAR may therefore provide a solution to the problem of mapping landslides when cloud obstructs optical imagery. In recent years the number of satellite-based SAR systems has vastly increased, leading to a corresponding increase in the frequency and regularity of image acquisition everywhere on Earth [16]. For example, the European Space Agency's (ESA's) Sentinel-1 satellite constellation (comprising the Sentinel-1a and Sentinel-1b satellites), imagery from which is used in this study, comprises two satellites and acquires imagery on ascending and descending tracks every 12 days for tectonic regions globally and every 6 days in Europe [17]. These data are freely available to download.