## 1 Introduction  
The 5 September 2018 Mw6.6 earthquake of Tomakomai, Japan has triggered about 10,000 landslides with high density, causing serious economic losses and casualties. In some local areas, the damage caused by this event even exceeded that by greater earthquakes such as the 2008 Wenchuan, China Mw7.9 [1] and 2015 Nepal Mw7.8 [2]. According to reports, this earthquake killed at least 41 persons, including 36 dead by landslides [3]. Hundreds of landslides have broken the ridges and changed the landform there. However, so far no comprehensive landslide inventory and landslide susceptibility assessment related to this event have been reported. To fill this gap, this work attempted to establish a detailed landslide inventory of the Tomakomai earthquake and use effective methods to assess landslide susceptibility in the entire affected area.  
Currently, the methods for landslide susceptibility mapping can be classified into two types: Qualitative and quantitative approaches. In the qualitative approach, expert opinions are very vital to estimate landslide potential from intrinsic factors, assigning weights of variables and rating susceptibility assessment are subjective [4]. Thus, the resulting susceptibility assessment is not ideal. In contrast, the quantitative approach can be considered as more objective due to their data-dependent characteristics.  
The quantitative approaches include statistical and advanced machine learning approaches. The statistical approaches use a landslide inventory to establish a relationship between landslides and their controlling factors for a sampled area, which is then extended to the total research area to attain the susceptibility assessment [5]. Many scholars have employed these methods to carry out landslide susceptibility assessment with the weight of evidence [6], discriminant analysis [7], binary statistical analysis [8; 9; 10], and the multivariate statistical model [11; 12]. Among them, the LR method has been proved to be more applicable and have achieved good assessment results in several earthquakes cases [13; 14; 15; 16; 17]. Meanwhile, with the development of machine learning, the landslide susceptibility assessment based on machine learning is gradually favored by scholars [18], such as neural network [13; 19] and SVM [20; 21; 22]. Among them, the SVM is more powerful to handle high-dimensional and nonlinear problems, and its prediction accuracy is superior to the statistical models in most cases [23; 24; 25].  
At present, most statistical models and advanced machine learning models have been used in non-seismic landslide areas with a small number of landslide samples. While there are few applications in the specific earthquake events with a large number of seismic landslides. And these studies mostly rely on incomplete landslide database to conduct the landslides susceptibility assessment in local quake-affected area, which cannot fully reflect the overall distribution of landslides triggered by a single earthquake [26; 27; 25].  
The aim of this study is to establish a detailed landslide inventory for the 2018 Mw6.6 Tomakomai, Japan earthquake based on high-resolution Planet images (3 m) before and after the earthquake and test the applicability of the LR and SVM to landslide susceptibility mapping in the affected area. Then the application effects of the two models were compared. This study provides successful experience and scientific reference for the selection of remote sensing images, landslide interpretation, selection of landslide influencing factors, and selection of evaluation model in similar studies.