## 3 Data and Method
### Data Source  
#### 3.1.1 Landslide Inventory  
A detailed and accurate earthquake-triggered landslide inventory is an important basis for landslide susceptibility assessment [31; 32]. With the advance of sensor and space technology, remote sensing is able to acquire detailed temporal and spatial information on landslides on the Earth's surface. This study used the satellite images of post-earthquake with high-resolution (3 m) (Planet satellite), which were acquired within 5 days after the earthquake [33]. These images cover the entire earthquake affected area, and have low cloud coverage (Figure 5b). The series of pre-earthquake ortho-images also come from the high-resolution (3 m) optical satellite images (Planet satellite) to ensure that the landslides existing before the earthquake are not recognized as co-seismic landslides (Figure 5a). The processed images are overlaid on the Google Earth Platform. Thus, the images can provide a stereo-like perspective view, and are very convenient to compare them before and after the seismic event [34].  
Figure 4: Procedural flow chart of this study.  
Figure 5: High-resolution remote sensing images. (**a**) Before the 2018 Tomakomai earthquake (3 August 2018). (**b**) After the 2018 Tomakomai earthquake.  
We followed the principles of image interpretation: (1) About the landslide scale, we interpret it as long as it is a landslide that can be distinguished from the image. (2) About the landslides before the earthquake, we think that they were caused by this earthquake if their shapes are not consistent before and after the earthquake. (3) In general, the areas with flow-textures, light tones, and non-or destroyed-vegetable coverage are identified as co-seismic landslides. Due to the thick vegetation coverage in the quake-affected area, the tone of non-seismic landslides is often dimmer than co-seismic landslides. Therefore, we mostly judge the co-seismic landslides based on whether the sliding surface is fresh or not. (Figure 6). (4) In addition, some of the detailed features can also be used, including the direction of the landslide, damaged plants on a landslide, the destroyed house and roads. These features allow us to build a co-seismic landslide inventory based on the satellite images. Figure 6b shows the distribution of co-seismic landslides near the epicenter (acquired on 11 September 2018) and images before the earthquake on 3 August 2018 (Figure 6a) in the same area. From the comparison of pre- and post-earthquake images, we can observe that a large number of co-seismic landslides were triggered by the 2018 Tomakomai earthquake (Figure 6b).  
In order to keep the objectivity of the landslide number, congregate and patched landslides were divided into individual landslides expressed as multi-polygons, rather than an individual landslide as a single polygon. Finally, we established a landslide inventory of the Tomakomai earthquake, which contains at least 9295 landslides, of which 6682 pieces are larger than 1000 m\({}^{2}\) (Figure 7). These landslides are mostly distributed in mountainous areas in Atsuma, Abira-cho, and Mukawa of Hokkaido. These landslides are mostly contiguous and shallow, several meters deep-seated [3]. The study area was defined as an irregular elliptical area according to the distribution of these landslides  
Figure 6: Interpretations of remote sensing images showing landslides near the epicenter (**a**): before the 2018 Tomakomai earthquake (3 August 2018); (**b**): after the 2018 Tomakomai earthquake (11 September 2018). Yellow polygons are boundaries of individual landslides triggered by the earthquake.  
(Figure 7), which covers 588.06 km\({}^{2}\). The total area of 9295 landslides is 30.96 km\({}^{2}\), the proportion of landslide area in the whole study area is 5.26%, and the density of landslide points is 15.80/km\({}^{2}\).