Diverse architectures of CNNs have been deployed for the detection of landslide-related information [3,4,24,26–28], with notable examples including U-Net [29–33], ResNet [34,35], and Mask-RCNN [36,37]. These models use image patches of multiple sizes for binary segmentation, i.e., landslide and background, yet they face notable challenges. The model by Bragagnolo et al. (2021) struggles to accurately detect landslides in complex terrains where visual features resemble surrounding landscapes, making it difficult to distinguish landslides from other elements [29]. Similarly, Ghorbanzadeh et al. (2021) find it challenging to differentiate between landslides and visually similar bare land in optical imagery, suggesting that SAR data could enhance accuracy by providing additional insights [30]. Devara et al. (2024) highlight the dependence on high-quality training data and the need for specific threshold settings, adding complexity to the detection process [33]. Other studies [2,36,37] have evaluated models using UAV-acquired images, Liu et al. (2024) emphasize the importance of empirical settings for better results when dealing with images of varying sizes [37].

YOLO models [38] specifically, YOLOv3 [39,40], YOLOv4 [41], YOLOv5 [42], YOLOv6 [43], YOLOv7 [44], YOLOv8 [45], and YOLOX [46] have been applied by researchers with significant success. To further refine landslide detection accuracy, attention models have been combined with CNNs [5]. These include 3D-SCAM [47], U-Net + CBAM [48], LA-YOLO-LLL [49], LD-YOLO: based on ECA [50], YOLO-SA [51], SW-MSA [52], YOLOv7-SE [53], LS-YOLO [54], and LA-YOLO (an improved YOLOv8) [55].

Although previous studies have laid a significant foundation, several research gaps remain unsolved. Firstly, YOLOv5 has been extensively applied in other object detection tasks, however, there is limited documentation on its application for landslide detection, particularly in complex terrains using remote sensing data. Existing YOLOv5 studies may lack a thorough evaluation of its performance in detecting landslides under challenging conditions such as varying land cover and cloud cover. Secondly, the efficacy of attention mechanisms within single-stage detection algorithms warrants comprehensive evaluation. Previous efforts integrating attention mechanisms with YOLOv5 often involved altering backbone architectures, making it difficult to isolate and evaluate the direct impact of attention mechanisms on performance. This indicates that the true potential of attention models was not fully explored. Thirdly, identifying an appropriate deep-learning model for accurate landslide detection is imperative. Our study addresses these gaps by adding attention mechanisms within the YOLOv5 architecture without altering other components. This allows us to isolate and fully assess how these mechanisms enhance detection accuracy, particularly in challenging landslide detection tasks where distinguishing landslides from complex backgrounds is crucial. By focusing on relevant image features, attention mechanisms improve the model’s precision in detecting landslides through remote sensing imagery, and our work demonstrates these advances through comprehensive experimentation.

Landslides often occur suddenly and have severe impacts, necessitating timely and precise information for effective emergency actions. Hence, our work contributes to the ongoing development of algorithms that can support real-time emergency response efforts. While other models have shown potential for landslide detection, our research focuses on improving the precision and efficiency of detecting landslides within image patches, utilizing the YOLOv5 + attention model. This specificity in improving detection in challenging scenarios is the unique contribution of our work. By addressing these more detailed aspects of algorithm improvement, our study contributes to the broader goal of rapid and accurate landslide detection, a crucial component of the emergency response framework.

The novel contributions of our work are threefold:

The development and optimization of improved YOLOv5 tailored for automated landslide event detection. We enhance YOLOv5 by refining its architecture to improve feature extraction and classification accuracy in landslide-prone regions.

The input datasets for our study consist primarily of high-resolution satellite images described in Section 2. The initial annotations of the dataset are represented in the form of polygons (file_type = xml). Therefore, we transformed the data into the required format, i.e., bounding boxes, comprising five parameters illustrated in Algorithm 2.

Additionally, a YAML file (a standard YOLO format), containing necessary information such as the file paths for training, validation, and testing datasets, the number of classes, and the class names (landslide and background), has been created. To facilitate experimentation, the annotated data has been divided into three subsets: 70% for training, 20% for validation, and 10% for testing. In our study, we used the Roboflow-guided approach for this data distribution. Roboflow helps manage and prepare datasets for machine learning by automatically partitioning them into balanced training, validation, and testing subsets. This ensures diverse and representative data distribution, reducing bias and improving the model’s accuracy and generalization for landslide detection.

This section explains the baseline model (YOLOv5), the implemented attention modules (CBAM and ECA), and the proposed network in detail.

During training, an image size of 640 × 640 pixels is served as an input to the proposed network. The choice of a 640 × 640 pixel image size for our landslide detection model is strategically aligned with the spatial resolution of the imagery and the scale of landslide features. This image size is generally sufficient to capture a variety of landslide sizes, ensuring that the model can detect features effectively while maintaining a balance between detail and computational efficiency. Additionally, the YOLOv5 architecture is designed to efficiently process input images of size 640 × 640 pixels. Moreover, the batch size is 8, epochs 500. We used a stochastic gradient descent optimizer with an initial learning rate of 0.01, an initial momentum factor of 0.937, and an initial weight decay of 0.0005. Besides, the considered hyperparameters are based on previous studies [44,46] and directly adopted for the current experiment. Furthermore, the machine configuration includes CPU: Intel® Xeon-CPU E3-1231 (v3@3.40 GHz), GPU (graphics processing unit): NVIDIA-GeForce-RTX3080Ti, deep learning framework: PyTorch 1.7, operating system: Ubuntu 18.04, and CUDA11.4.

The performance of the YOLOv5 + attention model is evaluated using both quantitative and qualitative criteria. The four primary metrics used for quantitative assessment are precision, recall, F-score, and mAP (Eqs (7)–(10)). The use of these is well-established and has been extensively used in previous studies [5,48,49,67,68]. Below is a detailed explanation of each metric: (7)Precision=(Truepositive)(Truepositive)+(Falsepositive) (8)Recall=(Truepositive)(Truepositive)+(Falsenegative) (9)F−Score=2×Precision×RecallPrecision+Recall (10)mAP=∑i=0n1n∫01Precision(Recall)d(Recall)

On the other hand, qualitative evaluation is conducted through visual analysis of the computed results.

The results of quantitative indicators, i.e., mAP, precision, recall, and f-score of the attention-embedded YOLOv5 are shown in Table 1. The mAP@0.5 of the YOLOv5 variants (baseline) particularly, YOLOv5n, YOLOv5s, and YOLOv5m is 73.1%, 75.1%, and 72.5%, respectively.

The mAP is a comprehensive metric that summarizes the precision-recall trade-off across different confidence thresholds. An increase in mAP indicates an overall improvement in the model’s ability, providing a more accurate and reliable detection performance across various conditions. A higher mAP reflects that the model is consistently detecting landslides with both high precision and high recall, making it effective in diverse and challenging environments. Considering the YOLOv5n + CBAM model, the mAP@0.5 computed is 77.8%, which showed an improvement of +4.7%. Further, an enhancement of +1.0% is noticed in mAP@0.5 of YOLOv5s + CBAM (76.1%). Moreover, in the case of YOLOv5m + CBAM, the estimated progress is +4.3% (mAP@0.5). Besides, ECA assimilated YOLOv5 also demonstrated the increased accuracy, mainly YOLOv5n + ECA (mAP@0.5 = 77.0%) and YOLOv5s + ECA (mAP@0.5 = 76.8%) unveil the progress of +3.9%, and +1.7%, respectively. Here, mAP evaluates how well the YOLOv5 model, enhanced with attention mechanisms (CBAM and ECA), detects landslides across a range of confidence thresholds. Unlike precision and recall, which are evaluated at a single threshold, mAP considers the performance at multiple thresholds, providing a holistic view of the model’s accuracy. In landslide detection, variations in confidence levels can affect the reliability of detections. mAP provides a comprehensive summary of how well the model performs across these different levels, ensuring that the evaluation is not biased by a single confidence threshold.

In addition, a difference of 1.60% is observed in the f-score of YOLOv5n + CBAM and YOLOv5n + ECA; however, the improvement reached +4.3% and +2.7%, respectively. Significant progress of +1.9% and +5.1% is seen in the f-score of YOLOv5s + CBAM and YOLOv5s + ECA, respectively. The highest improvement of +6.2% in the F-score is obtained in the case of YOLOv5s + CBAM. The F-score is a critical metric for evaluating landslide detection models as it balances precision and recall, handles imbalanced datasets, provides a single performance metric for easy comparison, helps interpret model trade-offs, and reflects real-world applicability. For landslide detection using models like YOLOv5 enhanced with attention mechanisms, the F-score ensures that both false positives and false negatives are minimized, leading to more accurate and reliable detection and mapping of landslides. The precision and recall ranged from 70.4% to 84.4% and 64.4% to 75.3%. Precision measures the proportion of correctly identified landslides out of all detected landslides. However, recall estimates the proportion of actual landslides that the model successfully detects. Considering the evaluation metrics, the YOLOv5n + CBAM exhibited the best performance.

In landslide detection tasks, calculating GLOPs (Giga Floating Point Operations), the number of parameters, the GPU used, and computational time are essential for estimating the model’s performance. GLOPs and parameters provide insights into the model’s complexity and computational load, which directly affect its efficiency and feasibility for real-time applications. The computed GLOPs (highest = 49.0 (YOLOv5m + CBAM), and lowest = 4.1 (YOLOv5n)) and the number of parameters (maximum = 22.21 (YOLOv5m + CBAM), and minimum = 1.76 (YOLOv5n)) in millions (m) by the baseline and attention integrated models are also shown in Table 1.

The computational time is an important parameter for practical deployment, especially in disaster response scenarios where timely detection and mapping of landslides are imperative. Together, these metrics help in evaluating the model’s performance, optimizing resource allocation, and ensuring that the system can deliver rapid and accurate results, thereby enhancing its applicability and reliability in real-world landslide detection tasks. The execution time of the YOLOv5s + ECA network is maximum (1.53 h) while the YOLOv5n + CBAM takes the minimum time (0.88 h). The execution time of two baseline models (YOLOv5n = 0.54 h and YOLOv5s = 0.53 h) is almost similar (difference = 0.01 h), whereas YOLOv5m is executed in 0.91 h.

Our study is limited to analyzing landslides within fixed-size image patches, and as such, the total coverage area is not explicitly defined in our current analysis. While this patch-based approach simplifies detailed analysis, it doesn’t assess performance over larger, contiguous regions, which would require processing multiple patches for broader applications.

Furthermore, the GPU used is crucial as it determines the model’s processing capability and speed; different GPUs offer varying levels of performance that can significantly impact the model’s ability to handle large datasets quickly. The GPU or memory utilization (GBs) by each model is also given in Table 1. It is observed that GPU consumption is directly related to the size of the model (YOLOv5n = 1.10 GBs, YOLOv5s = 1.88 GBs, and YOLOv5m = 3.73 GBs). Moreover, estimating the loss functions is crucial for the proposed landslide detection. The bounding box regression loss computes how accurately the predicted bounding boxes align with the actual landslide locations, ensuring that the model precisely identifies the spatial extent of landslides. Confidence loss, on the other hand, evaluates the model’s certainty in its predictions, distinguishing between true landslide detections and false positives.

The assessed loss functions (training and validation) of the five proposed networks (YOLOv5n + CBAM, YOLOv5s + CBAM, YOLOv5m + CBAM, YOLOv5n + ECA, and YOLOv5s + ECA) are presented in Fig. 4a,b, respectively. When the bounding box regression loss is low, it indicates that the model is accurately predicting the locations of the landslides with minimal error, i.e., the predicted boxes are close to the actual ground truth boxes. However, a high bounding box regression loss suggests that the predicted bounding boxes are not aligned well with the true locations of the landslides. This could indicate that the model struggles to localize the landslide accurately, resulting in poor detection performance. Further, confidence loss estimates how assured the model is in its prediction that a detected object is indeed a landslide. A low confidence loss means the model is effectively distinguishing between landslides and non-landslide regions. The model is confident when it makes correct detections and assigns high confidence scores to these predictions. Higher confidence loss shows that the model is either missing some landslides (false negatives) or incorrectly identifying non-landslide regions as landslides (false positives). This reflects a lack of confidence in making accurate predictions. Hence, the lower function represents that the model is performing well (YOLOv5n + CBAM). It can accurately locate and identify landslides with high confidence, meaning that both the bounding box and object classification tasks are being handled correctly. On the other hand, a higher loss function signifies that the model needs further training or optimization. Either it struggles with localizing landslides, identifying them, or both, leading to errors.

The visual outcomes of the extracted landslides by YOLO plus attention models are represented by bounding boxes shown in Fig. 5. The bounding box is a standard in object detection frameworks, especially those based on the YOLO architecture. Bounding boxes provide a simplified and efficient means of detecting and localizing objects within an image. The primary goal of the YOLOv5 + attention model is to quickly identify potential landslide areas from satellite imagery. Bounding boxes allow for rapid detection, which is crucial for time-sensitive applications such as post-disaster response. Moreover, training a model to detect and delineate exact polygonal outlines is significantly more complex and computationally intensive compared to detecting bounding boxes. Bounding boxes serve as a practical compromise that enables efficient training and inference while still providing valuable spatial information about the landslides. The use of bounding boxes allows the model to scale more effectively when processing large geographical areas. Given the extensive size of satellite images and the potential number of landslide events to be detected, bounding boxes enable the model to process images more quickly and with fewer computational resources. While bounding boxes provide a useful starting point for landslide detection, we acknowledge that detailed polygonal outlines are more desirable for many applications, such as accurate mapping and geospatial analysis. Irrespective of the spatial location, the landslides of small (A1), as well as moderate (B1) sizes in an image, are successfully extracted by the YOLOv5n + CBAM model. The model can differentiate the landslides of different extents. The dataset comprises images with complex backgrounds, which makes it challenging for landslide detection. For example, the presence of clouds, and landscapes with homogenous characteristics (landslides, and bare sand). In Fig. 5 C1, some of the landslides are difficult to interpret manually, however model predicts them effectively. The images in column 4 (D1) indicate the examples where the models struggle to detect landslide events. Nevertheless, YOLOv5n + CBAM showcased high detection precision and robustness. Hence, automated landslide recognition models are anticipated for better prediction results.

For the considered dataset, the competency of the advanced YOLO models specifically, YOLOv6 (n, s, and m), YOLOv7 tiny, and YOLOv8 (n, and s) is evaluated. Moreover, YOLO-NAS (s, and m), a recent object detection model, is also executed to examine its capability in the studies of landslide hazards. In total, we compared the obtained results with eight state-of-the-art YOLO models. The obtained mAP@0.5 of all the models (Fig. 6). The results indicate that the maximum accuracy is achieved by YOLOv8s (74.8%) followed by YOLOv8n (73.4%). The performance of YOLO-NASm (60.4%) is the least; however, a notable improvement is seen in YOLO-NASs (65.4%). A difference of 1.2% is observed in the case of YOLOv6s (67.3%), and YOLOv6m (68.5%), whereas almost similar accuracy is estimated for YOLOv6n (69.7%), and YOLOv7 tiny (69.0%) models. The significant improvement of +3.0% to +17.4%, and +2.2% to +16.6% in the mAP@0.50 of CBAM, and ECA-integrated YOLOv5 networks, respectively demonstrates the superior performance of our approach compared to other state-of-the-art YOLO models. Our model not only outperforms earlier YOLO versions but even more advanced models such as YOLOv8 and YOLO-NAS. This comparison and wide range of improvement prove the robustness of our attention-enhanced architecture across challenging environments.

Further, the lack of direct comparison with previous studies in our work is primarily due to the unique nature of our dataset and the novel combination of models we employed. The dataset used in our study has not been extensively utilized in previous studies. This uniqueness makes direct comparison challenging. Earlier studies on landslide detection have used different datasets, making direct performance comparison unfeasible. A justified comparison can only be made with studies using the same data and models. Our study aims to establish a baseline for future research using this specific dataset and methodology. However, we compared our results with the existing YOLOv5 + attention models applied to other datasets with dissimilar characteristics. The comparison between the suggested YOLOv5n + CBAM, YOLOv5n + ECA models and earlier studies, particularly LP-YOLO [44] and YOLOv5 + ASSF + CBAM [69], highlights the important advancements made in terms of performance metrics, specifically mAP@0.5, precision, and recall. Compared to LP-YOLO, our YOLOv5n + CBAM model shows a significant improvement in mAP@0.5 (+28.8%), precision (+30.7%), and recall (+21.3%). This shows that our model is enhanced at correctly identifying landslides, minimizing both false positives and false negatives, which is critical in applications requiring high accuracy. Moreover, YOLOv5n + ECA also shows a noteworthy improvement over LP-YOLO, with increases in mAP@0.5 (+28.0%), precision (+22.3%), and recall (+25.4%). These metrics demonstrate that the ECA-based model also yields challenging results.

In comparison with the YOLOv5 + ASSF + CBAM model [64], our YOLOv5n + CBAM shows a +3.8% increase in mAP@0.5 and an improvement of +6.0% in precision, although no progress is observed in recall. This illustrates that our model not only matches existing attention-based approaches but even exceeds them in terms of accuracy and precision, making it a stronger alternative. In addition, YOLOv5n + ECA also demonstrates an improvement of +3.0% in mAP@0.5, further showcasing how incorporating different attention mechanisms can result in improvements over previously existing models.

The proposed model is a trained YOLOv5 model enhanced with attention mechanisms such as CBAM and ECA. This advanced model is specifically designed for the detection and mapping of landslides using remote sensing imagery. The model’s training process involves several critical phases, which include the acquisition and annotation of training data, the integration of attention mechanisms during model training, and the fine-tuning and validation of the model to ensure optimal performance. These steps are essential for developing a model that can accurately identify landslide features in input images and generate detailed, reliable outputs. The timescales for data generation are largely dependent on the size and complexity of the dataset; however, once trained, the model is capable of processing and generating maps for small to medium-sized areas efficiently. The minimum mappable feature size is a critical factor in the effectiveness of our YOLOv5 + attention model for landslide detection. The model’s ability to identify and map small landslide features is constrained by the resolution of the input images and the model’s detection capabilities. Regarding input requirements, the model needs high-resolution satellite images to effectively detect landslides, with multi-spectral images being preferred due to their ability to capture detailed features. Additionally, high-resolution UAV images can be utilized for detailed analyses of smaller areas. The output from the model is presented as bounding boxes that accurately indicate the presence of landslides, aiming for high spatial accuracy with minimal misalignment. This ensures that the detected landslides closely match their actual locations.

Moreover, a relatively lower recall of the YOLOv5n + CBAM model is noted, despite its high precision and F-score. This indicates that while the model effectively minimizes false positives, it may overlook some actual landslide occurrences. To address this, we analyzed potential reasons for the lower recall. Filtering of subtle features: The CBAM module enhances feature selection but might suppress small-scale landslides. Detection threshold: A high confidence threshold may lead to fewer false positives but could also exclude some true positives. Dataset characteristics: The presence of complex terrain and vegetation may introduce challenges in detecting all landslides. Spatial resolution constraints: Smaller landslides may not be well represented due to the image resolution limitations. To improve recall while maintaining high precision, we plan to explore several approaches. Fine-tuning the detection confidence threshold could help retain more potential detections without significantly increasing false positives. Data augmentation techniques, such as generating synthetic samples and applying transformations, can enhance the representation of underrepresented landslide features. Additionally, incorporating hybrid attention mechanisms or multi-scale feature extraction strategies may allow the model to capture more complex landslide patterns effectively. Besides, post-processing techniques such as morphological operations or region-growing algorithms could be utilized to refine detections and improve recall.

Additionally, from Table 1, we observe that models integrated with attention mechanisms generally enhance detection performance in terms of precision, recall, and F-score, but at the cost of increased GFLOPs, parameter size, and computational time. For instance, YOLOv5n + CBAM improves the F-score and mAP@0.5 compared to the baseline YOLOv5n while maintaining a relatively small increase in computational complexity. This suggests that CBAM enhances feature extraction efficiency with only a small computational overhead. However, a different trend is seen in YOLOv5s + ECA, which achieves a higher precision (80.7%) but at a significant computational cost and a GPU memory demand. This highlights that while ECA-based models improve accuracy, they also require substantially more computational resources. Based on the results, we noted that the YOLOv5n + CBAM model presents a strong balance between performance and efficiency, making it an ideal choice for applications where both accuracy and computational constraints need to be considered. On the other hand, if higher accuracy is the priority and computational resources are not a constraint, models like YOLOv5s + ECA can be beneficial. Furthermore, we discuss the performance of an additional attention mechanism integrated within YOLOv5 to infer its capability. Next, we evaluate the potential of the state-of-the-art YOLO-based models for extracting landslides. Afterward, the implication of the patience parameter in the YOLO model is highlighted.