An Iterative Classification and Semantic Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images

Lu, Zili; Peng, Yuexing; Li, Wei; Yu, Junchuan; Ge, Daqing; Han, Lingyi; Xiang, Wei

doi:10.1109/tgrs.2023.3313586

Cited by 11 publications

(5 citation statements)

References 48 publications

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“…For example, mapping ancient landslides from high-resolution remote sensing images (HRSIs) poses significant challenges due to their transformed morphology over time and resemblance to surroundings. Addressing these challenges, a novel Iterative Classification and Semantic Segmentation Network (ICSSN) [116] was proposed. This network improves classification at both the object and pixel levels by iteratively enhancing the shared feature extraction module.…”

Section: Landslide Mapping (Pixel-level)mentioning

confidence: 99%

“…These findings suggest the importance of selecting the appropriate deep learning model based on the specific characteristics of the landslide. Considering the dynamic, nonlinear, and unstable nature of landslides, Li et al (2023) [145] proposed a dynamic model based on CNN-LSTM for landslide displacement prediction. This model decomposes displacements into trend, periodic, and random components, with a least square quintic polynomial function modeling the trend and CNN-LSTM predicting the periodic and random displacements.…”

Section: Landslide Displacement Predictionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for Exploring Landslides with Remote Sensing and Geo-Environmental Data: Frameworks, Progress, Challenges, and Opportunities

Zhang,

Wang

2024

Remote Sensing

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This article offers a comprehensive AI-centric review of deep learning in exploring landslides with remote-sensing techniques, breaking new ground beyond traditional methodologies. We categorize deep learning tasks into five key frameworks—classification, detection, segmentation, sequence, and the hybrid framework—and analyze their specific applications in landslide-related tasks. Following the presented frameworks, we review state-or-art studies and provide clear insights into the powerful capability of deep learning models for landslide detection, mapping, susceptibility mapping, and displacement prediction. We then discuss current challenges and future research directions, emphasizing areas like model generalizability and advanced network architectures. Aimed at serving both newcomers and experts on remote sensing and engineering geology, this review highlights the potential of deep learning in advancing landslide risk management and preservation.

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Section: Landslide Mapping (Pixel-level)mentioning

confidence: 99%

Section: Landslide Displacement Predictionmentioning

confidence: 99%

Deep Learning for Exploring Landslides with Remote Sensing and Geo-Environmental Data: Frameworks, Progress, Challenges, and Opportunities

Zhang,

Wang

2024

Remote Sensing

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“…However, the detection of old landslides still faces technical challenges that need to be solved in relevant research areas. The presence of old landslides is characterized by their long history and considerable time interval, leading to various degrees of transformation over time [20]. Subsequent to the occurrence of a landslide, vegetation often reestablishes itself over several years, blending with the surrounding environment.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, differentiating old landslides from recent ones primarily relies on scrutinizing the inherent morphological characteristics of the landslide itself and identifying certain traces of human-induced alteration [21]. Zili et al [20] designed an iterative classification and semantic segmentation network to classify and segment old landslides on the Loess Plateau, and the results show that the designed network is extremely effective for old landslides that are difficult to identify there. For the semantic segmentation task, the F1 score increased from 0.5054 to 0.5448 and the detection accuracy of the old landslide improved to 0.9 compared to the basic network.…”

Section: Introductionmentioning

confidence: 99%

Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8

Li,

Ding,

Zhang

et al. 2024

Applied Sciences

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The reactivation of old landslides can be triggered by heavy destructive earthquakes, heavy rainfall, and ongoing human activities, thereby resulting in the occurrence of secondary landslides. However, most existing models are designed for detecting nascent landslides and there are few algorithms for old landslide detection. In this paper, we introduce a novel landslide detection model known as YOLOv8-CW, built upon the YOLOv8 (You Only Look Once) architecture, to tackle the formidable challenge of identifying old landslides. We replace the Complete-IoU loss function in the original model with the Wise-IoU loss function to mitigate the impact of low-quality samples on model training and improve detection recall rate. We integrate a CBAM (Convolutional Block Attention Module) attention mechanism into our model to enhance detection accuracy. By focusing on the southwest river basin of the Sichuan–Tibet area, we collect 558 optical remote sensing images of old landslides in three channels from Google Earth and establish a dataset specifically for old landslide detection. Compared to the original model, our proposed YOLOv8-CW model achieves an increase in detection accuracy of 10.9%, recall rate of 6%, and F1 score from 0.66 to 0.74, respectively. These results demonstrate that our improved model exhibits excellent performance in detecting old landslides within the Sichuan–Tibet area.

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“…Although accurate, these methods are inefficient and pose safety risks [ 6 ]. Recent advancements in remote sensing technology provide new perspectives and means for landslide detection, such as high-resolution imagery [ 7 , 8 , 9 ], Synthetic Aperture Radar (SAR) interferometry [ 10 , 11 ] and LiDAR [ 12 , 13 , 14 ]. These data have led scholars to propose various automatic landslide detection methods, with optical remote sensing imagery offering rich spectral information and high-resolution surface images, aiding in distinguishing landslides from other land cover types [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

EMR-HRNet: A Multi-Scale Feature Fusion Network for Landslide Segmentation from Remote Sensing Images

Jin,

Liu,

Huang

2024

Sensors

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Landslides constitute a significant hazard to human life, safety and natural resources. Traditional landslide investigation methods demand considerable human effort and expertise. To address this issue, this study introduces an innovative landslide segmentation framework, EMR-HRNet, aimed at enhancing accuracy. Initially, a novel data augmentation technique, CenterRep, is proposed, not only augmenting the training dataset but also enabling the model to more effectively capture the intricate features of landslides. Furthermore, this paper integrates a RefConv and Multi-Dconv Head Transposed Attention (RMA) feature pyramid structure into the HRNet model, augmenting the model’s capacity for semantic recognition and expression at various levels. Last, the incorporation of the Dilated Efficient Multi-Scale Attention (DEMA) block substantially widens the model’s receptive field, bolstering its capability to discern local features. Rigorous evaluations on the Bijie dataset and the Sichuan and surrounding area dataset demonstrate that EMR-HRNet outperforms other advanced semantic segmentation models, achieving mIoU scores of 81.70% and 71.68%, respectively. Additionally, ablation studies conducted across the comprehensive dataset further corroborate the enhancements’ efficacy. The results indicate that EMR-HRNet excels in processing satellite and UAV remote sensing imagery, showcasing its significant potential in multi-source optical remote sensing for landslide segmentation.

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An Iterative Classification and Semantic Segmentation Network for Old Landslide Detection Using High-Resolution Remote Sensing Images

Cited by 11 publications

References 48 publications

Deep Learning for Exploring Landslides with Remote Sensing and Geo-Environmental Data: Frameworks, Progress, Challenges, and Opportunities

Deep Learning for Exploring Landslides with Remote Sensing and Geo-Environmental Data: Frameworks, Progress, Challenges, and Opportunities

Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8

EMR-HRNet: A Multi-Scale Feature Fusion Network for Landslide Segmentation from Remote Sensing Images

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