# Landslide Recognition Based on DeepLabv3+ Framework Fusing ResNet101 and ECA Attention Mechanism
## 2 Study Area and Dataset
### The Bijie Dataset  
Bijie, located in the northwest of China's Guizhou Province, is one of the areas with the worst landslide disasters in China. The dataset comes from [36], where researchers initially located landslides in satellite images based on historical inventory data of landslides collected from field surveys. Secondly, geologists marked each landslide boundary, corrected the local bias of a few landslides in the inventory data, deleted some landslides in the inventory data that could not be recognized from the image, and marked some areas with obvious geological landslide morphology characteristics as new potential landslides. Finally, the potential landslide samples and the samples with large location deviations in the inventory data were further confirmed by careful field investigation.  
The dataset was compiled from May to August 2018 by the TripleSat satellite. It contains 770 landslide instances, including rockfalls, rocksslides, and certain debris flow landslides, as well as 2003 negative samples that are not landslides, and its resolution is 0.8 m. The dataset consists of remote sensing landslide images, landslide boundary contour files (i.e., masks), and digital elevation models (DEMs). The red dots in Figure 1 indicate the actual landslide sites.  
The geo-tagging at the time of the landslides was conducted on the basis of field investigations, first accurately recording the actual location of each landslide event and then collecting related geological and geometric information. These data were then used to create landslide boundary files and register them with satellite images. Strict quality control measures were implemented throughout the labeling process. All the labeled data were reviewed and corrected several times to eliminate any possible human errors or inconsistencies.  
The pixels of the image range from 128 \(\times\) 128 to 1024 \(\times\) 1024. The landslide image contains the four channels of red, green, blue spectrum and digital elevation model (DEM), and the spatial resolution of the DEM is 0.8 m. In order to reduce the impact of the number of landslide and non-landslide images on the performance of the model, data augmentation  
Figure 1: Image data of the study areas. The red points are the Bijie landslide locations, and the yellow points are the Luding landslide locations [35].  
techniques such as cropping, rotation and flipping are used. Figure 2 shows the data image. In this experiment, the ratio of the test set, training set and validation set was set to 8:1:1.  
In our study, we used the 2018 under the background of the image data ([[http://study.rsgis.whu.edu.cn/pages/download/](http://study.rsgis.whu.edu.cn/pages/download/)]([http://study.rsgis.whu.edu.cn/pages/download/](http://study.rsgis.whu.edu.cn/pages/download/)) accessed on 26 August 2024), which has the characteristics of high resolution and wide coverage, with the RGB and four DEM channels, in accordance with the input conditions of the model. This dataset went through rigorous quality control and preprocessing steps, ensuring a suitable training process for the deep learning models. There may be differences between different data sources, such as differences in features such as the resolution, spectral range, and acquisition time, which may lead to certain deviations in the model output, and the resulting deviations are generally small for data with similar feature channels.