# A Lightweight and Partitioned CNN Algorithm for Multi-Landslide Detection in Remote Sensing Images
## 2 Related Work  
Landslide detection can generally be categorized into two approaches: traditional methods of landslide identification and automatic identification methods based on machine learning algorithms. Traditional methods of landslide detection often rely on field surveys conducted by experienced geologists, complemented by instrumental imaging techniques for analysis. These methods involve on-site inspections, geological mapping, and the collection of ground truth data, for example using interferometry synthetic aperture radar (InSAR) technology to obtain multi-temporal data to observe whether the slope is deformed, which can be used as a basis to infer potential landslides [3]. While traditional methods have been widely practiced and have proven effective, they have the limitations of being time-consuming and resource-intensive. The second category predominantly utilizes pre-existing datasets of landslides and facilitates automatic identification through the construction of algorithmic models. Generally, automatic landslide detection techniques can be categorized into machine learning approaches and deep learning approaches. Machine learning algorithms encompass methods such as Bayesian, logistic regression, support vector machines (SVMs), random forests, and artificial neural networks [4; 5; 6; 7], which can utilize various features related to landslide occurrence, such as texture and terrain information for classification and prediction. For instance, [PERSON] [8] applied random forest to evaluate the sensitivity of landslides, and [PERSON] [9] utilized SVM and kernel logistic regression for landslide recognition. Artificial neural networks, including pulse-coupled neural networks (PCNNs) and spiking neural networks, have been shown to possess outstanding capabilities in image fusion and computer vision applications [10; 11]. Owing to the swift advancements in hardware equipment and artificial intelligence, deep learning techniques have emerged as an additional potent data-driven approach for detection. Consequently, a multitude of sophisticated object-detection algorithms have surfaced, including two-stage object-detection algorithms represented by region-based convolutional neural networks (RCNNs), Fast R-CNN, and Faster R-CNN [12; 13] and single-stage object detection networks represented by the SSD algorithm [14] and the YOLO algorithm series [15; 16; 17; 18]. For instance, [PERSON] [19] selected the YOLOv3 and Mask RCNN algorithms to achieve automatic recognition of loess landslides, with an optimal average precision of 18.9%. [PERSON] [20] proposed an enhanced convolutional neural network for landslide detection of Bijie City, which demonstrated the effectiveness of a new landslide prediction based on the dataset. [PERSON] [21] incorporated a coordinate attention mechanism [22] to enhance the YOLOX [23] object-detection model, effectively tackling the problem of the poor detection of complex mixed landslides. [PERSON] [24] proposed SegFormer, a model based on the Transformer architecture, which is capable of automatically detecting landslides. In recent years, various lightweight neural architectures such as MobileNetV1-3 [25; 26; 27], ShuffleNet [28], GhostNet [29], and FasterNet [30] have followed, aiming to achieve fewer parameters, a fast inference speed, and high performance. In brief, MobileNet incorporates depthwise separable convolution and an inverted residual structure to decrease the computational expense while simultaneously improving the detection performance. ShuffleNet substitutes the \(1\times 1\) convolution with group convolution and incorporates a shuffle operation to facilitate information flow among various groups. In GhostNet, to avoid redundant features maps, the spatial features are only captured by inexpensive operations for half of the features. FasterNet proposes a partial convolution to extract features with an efficient and parameter-friendly manner.