Deep learning-based landslide detection in optical remote sensing images has been extensively studied. However, several challenges remain. Over time, factors such as vegetation cover and surface weathering can weaken the distinct characteristics of landslides, leading to blurred boundaries and diminished texture features. Furthermore, obtaining landslide samples is challenging in regions with low landslide frequency. Expanding the acquisition range introduces greater variability in the optical characteristics of the samples. As a result, deep learning models often struggle to achieve accurate landslide identification in these regions. To address these challenges, we propose a multi-scale target attention YOLO model (MSTA-YOLO). First, we introduced a receptive field attention (RFA) module, which initially applies channel attention to emphasize the primary features and then simulates the human visual receptive field using convolutions of varying sizes. This design enhances the model’s feature extraction capability, particularly for complex and multi-scale features. Next, we incorporated the normalized Wasserstein distance (NWD) to refine the loss function, thereby enhancing the model’s learning capacity for detecting small-scale landslides. Finally, we streamlined the model by removing redundant structures, achieving a more efficient architecture compared to state-of-the-art YOLO models. Experimental results demonstrated that our proposed MSTA-YOLO outperformed other compared methods in landslide detection and is particularly suitable for wide-area landslide monitoring.

The large-scale detection of greenhouses and rural buildings is important for natural resource surveys and farmland protection. However, in rural and mountainous areas, the resolution and accessibility of remote sensing satellite images from a single source are poor, making it difficult to detect greenhouses and rural buildings effectively and automatically. In this paper, a wide-area greenhouse and rural building (GH-RB) detection dataset is constructed as a benchmark by using high-resolution remote sensing images of Hebei Province, China, collected from the image platform. Then, Efficient Weighted YOLOv8 (EW-YOLOv8) is proposed by using the dataset with unbalanced and small samples of greenhouse and rural buildings, in which the improvement measures are introduced. These include the following: (1) replacing the traditional up-sampler with DySample in the up-sampling part of the neck of the model to recover the lost details after multiple down-sampling operations; (2) replacing the calculation loss function with NWD loss to compensate for the sensitivity of the IoU to the position deviation of small objects; and (3) introducing a weight function named Slide to resolve the data imbalance between easy and difficult samples. The experimental results show that the proposed method can achieve excellent object detection performance on the RSOD dataset compared with state-of-the-art methods, proving the effectiveness of the proposed EW-YOLOv8. The results on the constructed GH-RB dataset show that the proposed method can detect greenhouse and rural buildings quickly and accurately, which could help improve the efficiency of investigating farmland usage and performing natural resource surveys.

Loess landslides in northwestern China are highly unstable and difficult to distinguish due to sparse vegetation and their spectral and morphological similarity to the surrounding terrain. These landslides demonstrate considerable diversity in manifestation, encompassing shallow translational slides, small-scale features, partially obscured formations, and instances with irregular or poorly defined boundaries. To address the above issues, we propose MPES-YOLO, a multi-scale lightweight YOLO-based framework with selective edge enhancement to detect loess landslides. This model is based on the YOLOv8 architecture and incorporates a multi-scale partial convolution and exponential moving average (MPCE) module to improve multi-scale feature representation while reducing computational cost and enhancing small-target sensitivity. Additionally, to address ambiguous boundaries, a selective edge enhancement (SEE) module is introduced to extract authentic object edges from original images and inject them into key training layers, improving boundary perception. Finally, SIoU is adopted to improve geometric consistency for irregular landslide boundary localization. This paper first verified the basic detection performance of MPES-YOLO on the publicly available Bijie landslide dataset. Then, an experimental study was conducted in the loess landslides of Yan’an City, Shaanxi Province. The mAP@0.5 was 91.9%, and the parameter quantity was reduced by 23.3% compared with the baseline model. A generalization experiment was also carried out on the landslides in the Ningxia region, with the mAP@0.5 being 97.4%. The results show that MPES-YOLO achieves a strong balance between detection accuracy and computational efficiency, providing an effective and scalable solution for automated loess landslide detection and geological disaster early warning.