# A Lightweight and Partitioned CNN Algorithm for Multi-Landslide Detection in Remote Sensing Images
## 1 Introduction  
Landslides are severe geological hazards that widely occur in mountainous environments with slopes and frequently lead to chain reactions such as mountain collapses and debris flows, which can pose serious risks to human life and property. Therefore, enhancing the detection and early warning systems for landslide-related geological catastrophes holds considerable implications in the context of China's endeavors towards disaster mitigation and risk reduction [1; 2].  
Traditional landslide detection methods primarily rely on geologists, which often entails significant manpower and financial investments. However, the effectiveness of these methods may not always meet expectations. In light of the advancements in satellite imaging accuracy, researchers have increasingly proposed landslide detection approaches that leverage optical image data. Concurrently, machine learning has become increasingly popular in the field of landslide detection. Besides, the emergence of convolutional neural networks (CNNs) has led to the successful application of deep-learning-based object recognition algorithms in landslide detection, and they have gradually become mainstream. In contrast to machine learning approaches, deep learning techniques abandon the complicated artificially designed features, which adopt deeper convolutional neural networks toautomatically acquire distinguishing characteristics. Furthermore, the data sample capacity of deep learning for landslide detection can be extensive, rendering it more appropriate for large-scale landslide identification and endowing it with a more-robust generalization capability. Although deep-learning-based algorithms have shown success in detecting landslides, they still face some challenges: These models necessitate substantial computational resources and numerous parameters, leading to diminished inference efficiency. As a result, employing them in power-limited contexts or embedded platforms with the objective of achieving real-time detection becomes challenging. Moreover, the scarcity of open-source repositories containing high-spatial-resolution images of landslides hampers the effective training and validation of these models.  
To tackle these challenges, we constructed a landslide dataset utilizing the Google Earth platform in this study. This paper presents a lightweight framework named LP-YOLO, which achieves real-time landslide detection. Our contributions are as follows:  
(1) We propose the Partition module and form a new feature extraction network named PartitionNet to replace the backbone of YOLOv5, which brings better performance, while reducing drastically the redundant parameters and computational complexity.  
(2) A new feature-exploiting module named the VH block is constructed and added to the backbone to retain the information after down-sampling and to explore long-range information with a small computational cost.  
(3) We designed a new feature fusion structure and propose a CSPCrossStage module instead of C3 in the neck of the model to boost information flow and enhance the location accuracy of multi-scale landslides with less computing resource.  
(4) The SIoU loss function and the attention mechanisms were introduced to expedite the convergence speed during training and enhance the detection accuracy of the model.