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Journal articles on the topic 'Semantic landslides'

Author: Grafiati

Published: 28 May 2022

Last updated: 31 July 2025

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1

Li, Zhi-Hai, An-Chi Shi, Huai-Xian Xiao, et al. "Robust Landslide Recognition Using UAV Datasets: A Case Study in Baihetan Reservoir." Remote Sensing 16, no. 14 (2024): 2558. http://dx.doi.org/10.3390/rs16142558.

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The task of landslide recognition focuses on extracting the location and extent of landslides over large areas, providing ample data support for subsequent landslide research. This study explores the use of UAV and deep learning technologies to achieve robust landslide recognition in a more rational, simpler, and faster manner. Specifically, the widely successful DeepLabV3+ model was used as a blueprint and a dual-encoder design was introduced to reconstruct a novel semantic segmentation model consisting of Encoder1, Encoder2, Mixer and Decoder modules. This model, named DeepLab for Landslide

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Yang, Shuang, Yuzhu Wang, Panzhe Wang, et al. "Automatic Identification of Landslides Based on Deep Learning." Applied Sciences 12, no. 16 (2022): 8153. http://dx.doi.org/10.3390/app12168153.

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A landslide is a kind of geological disaster with high frequency, great destructiveness, and wide distribution today. The occurrence of landslide disasters bring huge losses of life and property. In disaster relief operations, timely and reliable intervention measures are very important to prevent the recurrence of landslides or secondary disasters. However, traditional landslide identification methods are mainly based on visual interpretation and on-site investigation, which are time-consuming and inefficient. They cannot meet the time requirements in disaster relief operations. Therefore, to

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Tang, Xiaochuan, Zihan Tu, Yu Wang, Mingzhe Liu, Dongfen Li, and Xuanmei Fan. "Automatic Detection of Coseismic Landslides Using a New Transformer Method." Remote Sensing 14, no. 12 (2022): 2884. http://dx.doi.org/10.3390/rs14122884.

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Earthquake-triggered landslides frequently occur in active mountain areas, which poses great threats to the safety of human lives and public infrastructures. Fast and accurate mapping of coseismic landslides is important for earthquake disaster emergency rescue and landslide risk analysis. Machine learning methods provide automatic solutions for landslide detection, which are more efficient than manual landslide mapping. Deep learning technologies are attracting increasing interest in automatic landslide detection. CNN is one of the most widely used deep learning frameworks for landslide detec

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Sun, Hui, Shuguang Yang, Rui Wang, and Kaixin Yang. "Study on a Landslide Segmentation Algorithm Based on Improved High-Resolution Networks." Applied Sciences 14, no. 15 (2024): 6459. http://dx.doi.org/10.3390/app14156459.

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Landslides are a kind of geological hazard with great destructive potential. When a landslide event occurs, a reliable landslide segmentation method is important for assessing the extent of the disaster and preventing secondary disasters. Although deep learning methods have been applied to improve the efficiency of landslide segmentation, there are still some problems that need to be solved, such as the poor segmentation due to the similarity between old landslide areas and the background features and missed detections of small-scale landslides. To tackle these challenges, a proposed high-reso

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Guan, Yong, Lili Yu, Shengyou Hao, Linsen Li, Xiaotong Zhang, and Ming Hao. "Slope Failure and Landslide Detection in Huangdao District of Qingdao City Based on an Improved Faster R-CNN Model." GeoHazards 4, no. 3 (2023): 302–15. http://dx.doi.org/10.3390/geohazards4030017.

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To reduce the significant losses caused by slope failures and landslides, it is of great significance to detect and predict these disasters scientifically. This study focused on Huangdao District of Qingdao City in Shandong Province, using the improved Faster R-CNN network to detect slope failures and landslides. This study introduced a multi-scale feature enhancement module into the Faster R-CNN model. The module enhances the network’s perception of different scales of slope failures and landslides by deeply fusing high-resolution weak semantic features with low-resolution strong semantic fea

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Ju, Yuanzhen, Qiang Xu, Shichao Jin, et al. "Loess Landslide Detection Using Object Detection Algorithms in Northwest China." Remote Sensing 14, no. 5 (2022): 1182. http://dx.doi.org/10.3390/rs14051182.

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Regional landslide identification is important for the risk management of landslide hazards. The traditional methods of regional landslide identification were mainly conducted by a human being. In previous studies, automatic landslide recognition mainly focused on new landslides distinct from the environment induced by rainfall or earthquake, using the image classification method and semantic segmentation method of deep learning. However, there is a lack of research on the automatic recognition of old loess landslides, which are difficult to distinguish from the environment. Therefore, this st

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Zheng, Xiangxiang, Lingyi Han, Guojin He, Ning Wang, Guizhou Wang, and Lei Feng. "Semantic Segmentation Model for Wide-Area Coseismic Landslide Extraction Based on Embedded Multichannel Spectral–Topographic Feature Fusion: A Case Study of the Jiuzhaigou Ms7.0 Earthquake in Sichuan, China." Remote Sensing 15, no. 4 (2023): 1084. http://dx.doi.org/10.3390/rs15041084.

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The rapid and accurate extraction of wide-area coseismic landslide locations is critical in earthquake emergencies. At present, the extraction of coseismic landslides is mainly based on post-earthquake site investigation or the interpretation of human–computer interactions based on remote sensing images. However, the identification efficiency is low, which seriously delays the earthquake emergency response. On the basis of the available multisource and multiscale remote sensing data, numerous studies have been carried out on the methods of coseismic landslide extraction, such as pixel analysis

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8

Zhou, Nan, Jin Hong, Wenyu Cui, Shichao Wu, and Ziheng Zhang. "A Multiscale Attention Segment Network-Based Semantic Segmentation Model for Landslide Remote Sensing Images." Remote Sensing 16, no. 10 (2024): 1712. http://dx.doi.org/10.3390/rs16101712.

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Landslide disasters have garnered significant attention due to their extensive devastating impact, leading to a growing emphasis on the prompt and precise identification and detection of landslides as a prominent area of research. Previous research has primarily relied on human–computer interactions and visual interpretation from remote sensing to identify landslides. However, these methods are time-consuming, labor-intensive, subjective, and have a low level of accuracy in extracting data. An essential task in deep learning, semantic segmentation, has been crucial to automated remote sensing

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9

Tang, Meng, Yuelin He, Muhammed Aslam, Edore Akpokodje, and Syeda Fizzah Jilani. "Enhanced U-Net++ for Improved Semantic Segmentation in Landslide Detection." Sensors 25, no. 9 (2025): 2670. https://doi.org/10.3390/s25092670.

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Landslide detection and segmentation are critical for disaster risk assessment and management. However, achieving accurate segmentation remains challenging due to the complex nature of landslide terrains and the limited availability of high-quality labeled datasets. This paper proposes an enhanced U-Net++ model for semantic segmentation of landslides in the Wenchuan region using the CAS Landslide Dataset. The proposed model integrates multi-scale feature extraction and attention mechanisms to enhance segmentation accuracy and robustness. The experimental results demonstrate that ASK-UNet++ out

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Cui, Wenqi, Xin He, Meng Yao, et al. "Landslide Image Captioning Method Based on Semantic Gate and Bi-Temporal LSTM." ISPRS International Journal of Geo-Information 9, no. 4 (2020): 194. http://dx.doi.org/10.3390/ijgi9040194.

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When a landslide happens, it is important to recognize the hazard-affected bodies surrounding the landslide for the risk assessment and emergency rescue. In order to realize the recognition, the spatial relationship between landslides and other geographic objects such as residence, roads and schools needs to be defined. Comparing with semantic segmentation and instance segmentation that can only recognize the geographic objects separately, image captioning can provide richer semantic information including the spatial relationship among these objects. However, the traditional image captioning m

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Jin, Yuanhang, Xiaosheng Liu, and Xiaobin Huang. "EMR-HRNet: A Multi-Scale Feature Fusion Network for Landslide Segmentation from Remote Sensing Images." Sensors 24, no. 11 (2024): 3677. http://dx.doi.org/10.3390/s24113677.

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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 Att

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Huang, Jingwen, Weijing Song, Tao Liu, Xiaoyu Cui, Jining Yan, and Xiaoyu Wang. "Submarine Landslide Identification Based on Improved DeepLabv3 with Spatial and Channel Attention." Remote Sensing 16, no. 22 (2024): 4205. http://dx.doi.org/10.3390/rs16224205.

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As one of the most destructive, hazardous, and frequent marine geohazards, correctly recognizing submarine landslides holds substantial importance for regional risk assessment, disaster prevention, and marine resource development. Many conventional approaches to prediction and mapping necessitate the involvement of expert insights, oversight, and extensive field investigations, which can result in significant time and effort invested in the prediction process. This paper focuses on employing a deep neural network semantic segmentation technique to detect submarine landslides to replace previou

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Liang, Yiwen, Yi Zhang, Yuanxi Li, and Jiaqi Xiong. "Automatic Identification for the Boundaries of InSAR Anomalous Deformation Areas Based on Semantic Segmentation Model." Remote Sensing 15, no. 21 (2023): 5262. http://dx.doi.org/10.3390/rs15215262.

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Interferometric synthetic aperture radar (InSAR) technology has become one of the mainstream techniques for active landslide identification over a large area. However, the method for interpreting anomalous deformation areas derived from InSAR data is still mainly manual delineation through human–computer interaction. This study focuses on using a deep learning semantic segmentation model to identify the boundaries of anomalous deformation areas automatically. We experimented with the delineation results based on an InSAR deformation map, hot spot map, and different combinations of topographic

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Fang, Chengyong, Xuanmei Fan, Hao Zhong, Luigi Lombardo, Hakan Tanyas, and Xin Wang. "A Novel Historical Landslide Detection Approach Based on LiDAR and Lightweight Attention U-Net." Remote Sensing 14, no. 17 (2022): 4357. http://dx.doi.org/10.3390/rs14174357.

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Rapid and accurate identification of landslides is an essential part of landslide hazard assessment, and in particular it is useful for land use planning, disaster prevention, and risk control. Recent alternatives to manual landslide mapping are moving in the direction of artificial intelligence—aided recognition of these surface processes. However, so far, the technological advancements have not produced robust automated mapping tools whose domain of validity holds in any area across the globe. For instance, capturing historical landslides in densely vegetated areas is still a challenge. This

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Cao, W., X. H. Tong, S. C. Liu, and D. Wang. "LANDSLIDES EXTRACTION FROM DIVERSE REMOTE SENSING DATA SOURCES USING SEMANTIC REASONING SCHEME." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 22, 2016): 25–31. http://dx.doi.org/10.5194/isprs-archives-xli-b8-25-2016.

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Using high resolution satellite imagery to detect, analyse and extract landslides automatically is an increasing strong support for rapid response after disaster. This requires the formulation of procedures and knowledge that encapsulate the content of disaster area in the images. Object-oriented approach has been proved useful in solving this issue by partitioning land-cover parcels into objects and classifies them on the basis of expert rules. Since the landslides information present in the images is often complex, the extraction procedure based on the object-oriented approach should conside

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Cao, W., X. H. Tong, S. C. Liu, and D. Wang. "LANDSLIDES EXTRACTION FROM DIVERSE REMOTE SENSING DATA SOURCES USING SEMANTIC REASONING SCHEME." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 22, 2016): 25–31. http://dx.doi.org/10.5194/isprsarchives-xli-b8-25-2016.

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Using high resolution satellite imagery to detect, analyse and extract landslides automatically is an increasing strong support for rapid response after disaster. This requires the formulation of procedures and knowledge that encapsulate the content of disaster area in the images. Object-oriented approach has been proved useful in solving this issue by partitioning land-cover parcels into objects and classifies them on the basis of expert rules. Since the landslides information present in the images is often complex, the extraction procedure based on the object-oriented approach should conside

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17

Yang, Qiyuan, Xianmin Wang, Xinlong Zhang, et al. "A Novel Deep Learning Method for Automatic Recognition of Coseismic Landslides." Remote Sensing 15, no. 4 (2023): 977. http://dx.doi.org/10.3390/rs15040977.

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Massive earthquakes generally trigger thousands of coseismic landslides. The automatic recognition of these numerous landslides has provided crucial support for post-earthquake emergency rescue, landslide risk mitigation, and city reconstruction. The automatic recognition of coseismic landslides has always been a difficult problem due to the relatively small size of a landslide and various complicated environmental backgrounds. This work proposes a novel semantic segmentation network, EGCN, to improve the landslide identification accuracy. EGCN conducts coseismic landslide recognition by a rec

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18

Su, Riya, and Yanming Yang. "Landslide detection of optical remote sensing image based on attention and u-net." ITM Web of Conferences 45 (2022): 01062. http://dx.doi.org/10.1051/itmconf/20224501062.

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With the improvement of remote sensing image technology, researchers pay more and more attention to detecting landslides in optical remote sensing images. In this paper, the landslide is detected by semantic segmentation model based on deep learning, U-shaped network is used to enhance the extraction ability of landslide features, and the model pays more attention to landslide area through attention mechanism, so as to make the model detect landslide more accurately. Through experiments on the Bijie Landslide Dataset, the values of OA and mIoU in this model are increased by 1% and 16% respecti

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Vega, Johnny, and César Hidalgo. "Evaluation of U-Net transfer learning model for semantic segmentation of landslides in the Colombian tropical mountain region." MATEC Web of Conferences 396 (2024): 19002. http://dx.doi.org/10.1051/matecconf/202439619002.

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Landslides in tropical regions, like the Colombian Andean region, pose unique challenges due to factors such as intense rainfall, steep slopes, and complex terrains. Mapping historical and current landslide activity through inventory maps is essential in tropical mountainous regions. While satellite data is commonly used for mapping, it can be time-consuming and manual-intensive, limiting inventory availability. Deep Learning (DL) models, especially Convolutional Neural Networks (CNNs), have shown promise in remote sensing applications with High Resolution (HR) imagery, including landslide det

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Liu, Peng, Yongming Wei, Qinjun Wang, et al. "A Research on Landslides Automatic Extraction Model Based on the Improved Mask R-CNN." ISPRS International Journal of Geo-Information 10, no. 3 (2021): 168. http://dx.doi.org/10.3390/ijgi10030168.

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Landslides are the most common and destructive secondary geological hazards caused by earthquakes. It is difficult to extract landslides automatically based on remote sensing data, which is import for the scenario of disaster emergency rescue. The literature review showed that the current landslides extraction methods mostly depend on expert interpretation which was low automation and thus was unable to provide sufficient information for earthquake rescue in time. To solve the above problem, an end-to-end improved Mask R-CNN model was proposed. The main innovations of this paper were (1) repla

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Derevianko, V. "PRAGMATIC ASPECTS OF SEMANTICS OF TRANSFORMED PHRASES." Comparative studies of Slavic languages and literatures. In memory of Academician Leonid Bulakhovsky, no. 36 (2020): 13–24. http://dx.doi.org/10.17721/2075-437x.2020.36.02.

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The article deals with the problem of pragmatic aspects of the semantics of transformed phrases. Functional-pragmatic features of transformed phrases are explored on examples of structural-semantic transformations of the distributionof the component composition. The structural analysis is presented and the formal features of the phraseological transformations updated by the spreading of the component composition is analyzed. An attempt to find out the causes and purposes of the authors using of the structural-semantic transformations of the component composition spreading of phrases and their

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Morales, Bastian, Angel Garcia-Pedrero, Elizabet Lizama, et al. "Patagonian Andes Landslides Inventory: The Deep Learning’s Way to Their Automatic Detection." Remote Sensing 14, no. 18 (2022): 4622. http://dx.doi.org/10.3390/rs14184622.

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Landslide inventories are crucial to studying the dynamics, associated risks, and effects of these geomorphological processes on the evolution of mountainous landscapes. The production of landslide maps is mainly based on manual visual interpretation methods of aerial and satellite images combined with field surveys. In recent times, advances in machine learning methods have made it possible to explore new semi-automated landslide detection methodologies using remotely detected images. In this sense, developing new artificial intelligence models based on Deep Learning (DL) opens up an excellen

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Xi, Laidian, Junchuan Yu, Daqing Ge, et al. "SAM-CFFNet: SAM-Based Cross-Feature Fusion Network for Intelligent Identification of Landslides." Remote Sensing 16, no. 13 (2024): 2334. http://dx.doi.org/10.3390/rs16132334.

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Landslides are common hazardous geological events, and accurate and efficient landslide identification methods are important for hazard assessment and post-disaster response to geological disasters. Deep learning (DL) methods based on remote sensing data are currently widely used in landslide identification tasks. The recently proposed segment anything model (SAM) has shown strong generalization capabilities in zero-shot semantic segmentation. Nevertheless, SAM heavily relies on user-provided prompts, and performs poorly in identifying landslides on remote sensing images. In this study, we pro

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Yang, Zhiqiang, Chong Xu, and Lei Li. "Landslide Detection Based on ResU-Net with Transformer and CBAM Embedded: Two Examples with Geologically Different Environments." Remote Sensing 14, no. 12 (2022): 2885. http://dx.doi.org/10.3390/rs14122885.

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An efficient method of landslide detection can provide basic scientific data for emergency command and landslide susceptibility mapping. Compared to a traditional landslide detection approach, convolutional neural networks (CNN) have been proven to have powerful capabilities in reducing the time consumed for selecting the appropriate features for landslides. Currently, the success of transformers in natural language processing (NLP) demonstrates the strength of self-attention in global semantic information acquisition. How to effectively integrate transformers into CNN, alleviate the limitatio

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Zhu, Qing, Junxiao Zhang, Yulin Ding, et al. "Semantics-Constrained Advantageous Information Selection of Multimodal Spatiotemporal Data for Landslide Disaster Assessment." ISPRS International Journal of Geo-Information 8, no. 2 (2019): 68. http://dx.doi.org/10.3390/ijgi8020068.

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Although abundant spatiotemporal data are collected before and after landslides, the volume, variety, intercorrelation, and heterogeneity of multimodal data complicates disaster assessments, so it is challenging to select information from multimodal spatiotemporal data that is advantageous for credible and comprehensive disaster assessment. In disaster scenarios, multimodal data exhibit intrinsic relationships, and their interactions can greatly influence selection results. Previous data retrieval methods have mainly focused on candidate ranking while ignoring the generation and evaluation of

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Prakash, Nikhil, Andrea Manconi, and Simon Loew. "Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models." Remote Sensing 12, no. 3 (2020): 346. http://dx.doi.org/10.3390/rs12030346.

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Mapping landslides using automated methods is a challenging task, which is still largely done using human efforts. Today, the availability of high-resolution EO data products is increasing exponentially, and one of the targets is to exploit this data source for the rapid generation of landslide inventory. Conventional methods like pixel-based and object-based machine learning strategies have been studied extensively in the last decade. In addition, recent advances in CNN (convolutional neural network), a type of deep-learning method, has been widely successful in extracting information from im

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Roberti, Gioachino, Jacob McGregor, Sharon Lam, et al. "INSPIRE standards as a framework for artificial intelligence applications: a landslide example." Natural Hazards and Earth System Sciences 20, no. 12 (2020): 3455–83. http://dx.doi.org/10.5194/nhess-20-3455-2020.

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Abstract. This study presents a landslide susceptibility map using an artificial intelligence (AI) approach based on standards set by the INSPIRE (Infrastructure for Spatial Information in the European Community) framework. INSPIRE is a European Union spatial data infrastructure (SDI) initiative to standardize spatial data across borders to ensure interoperability for management of cross-border infrastructure and environmental issues. However, despite the theoretical effectiveness of the SDI, few real-world applications make use of INSPIRE standards. In this study, we show how INSPIRE standard

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Chen, Ximing, Xin Yao, Zhenkai Zhou, Yang Liu, Chuangchuang Yao, and Kaiyu Ren. "DRs-UNet: A Deep Semantic Segmentation Network for the Recognition of Active Landslides from InSAR Imagery in the Three Rivers Region of the Qinghai–Tibet Plateau." Remote Sensing 14, no. 8 (2022): 1848. http://dx.doi.org/10.3390/rs14081848.

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At present, Synthetic Aperture Radar Interferometry (InSAR) has been an important technique for active landslides recognition in the geological survey field. However, the traditional interpretation method through human–computer interaction highly relies on expert experience, which is time-consuming and subjective. To solve the problem, this study designed an end-to-end semantic segmentation network, called deep residual shrinkage U-Net (DRs-UNet), to automatically extract potential active landslides in InSAR imagery. The proposed model was inspired by the structure of U-Net and adopted a resid

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Wang, Xiao, Dongsheng Zhong, Chenghao Liu, et al. "DS Net: A Dual-Coded Segmentation Network Leveraging Large Model Prior Knowledge for Intelligent Landslide Extraction." Remote Sensing 17, no. 11 (2025): 1912. https://doi.org/10.3390/rs17111912.

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Landslides are characterized by their suddenness and destructive power, making rapid and accurate identification crucial for emergency rescue and disaster assessment in affected areas. To address the challenges of limited landslide samples and data complexity, a landslide identification sample library was constructed using high-resolution remote sensing imagery combined with field validation. An innovative Dual-Coded Segmentation Network (DS Net), which realizes dynamic alignment and deep fusion of local details and global context, image features and domain knowledge through the multi-attentio

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Chen, Xinfang, Shiwei Wang, Venkata Dinavahi, Lijia Yang, Dibai Wu, and Meiyi Shen. "Landslide Recognition Based on DeepLabv3+ Framework Fusing ResNet101 and ECA Attention Mechanism." Applied Sciences 15, no. 5 (2025): 2613. https://doi.org/10.3390/app15052613.

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A landslide is one of the most common geological disasters, which is associated with great destructive power and harm. In recent years, semantic segmentation models have been applied to landslide recognition research and have made some achievements. However, the current method still has issues, overlooking small targets like fine cracks, missegmenting boundaries, and struggling to differentiate spectral signatures such as those of different rock types in landslide-prone areas. In this paper, a landslide detection model based on the DeepLabv3+ framework, DeepLabv3+-ResNet101-ECA, is proposed. T

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Fang, Chengyong, Xuanmei Fan, Xin Wang, et al. "A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images." Earth System Science Data 16, no. 10 (2024): 4817–42. http://dx.doi.org/10.5194/essd-16-4817-2024.

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Abstract. Rapid and accurate mapping of landslides triggered by extreme events is essential for effective emergency response, hazard mitigation, and disaster management. However, the development of generalized machine learning models for landslide detection has been hindered by the absence of a high-resolution, globally distributed, event-based dataset. To address this gap, we introduce the Globally Distributed Coseismic Landslide Dataset (GDCLD), a comprehensive dataset that integrates multi-source remote sensing images, including PlanetScope, Gaofen-6, Map World, and uncrewed aerial vehicle

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Chen, Luanjie, Ling Peng, and Lina Yang. "Improving Landslide Prediction: Innovative Modeling and Evaluation of Landslide Scenario with Knowledge Graph Embedding." Remote Sensing 16, no. 1 (2023): 145. http://dx.doi.org/10.3390/rs16010145.

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The increasing frequency and magnitude of landslides underscore the growing importance of landslide prediction in light of factors like climate change. Traditional methods, including physics-based methods and empirical methods, are beset by high costs and a reliance on expert knowledge. With the advancement of remote sensing and machine learning, data-driven methods have emerged as the mainstream in landslide prediction. Despite their strong generalization capabilities and efficiency, data-driven methods suffer from the loss of semantic information during training due to their reliance on a ‘s

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Franceschini, Rachele, Ascanio Rosi, Filippo Catani, and Nicola Casagli. "Exploring a landslide inventory created by automated web data mining: the case of Italy." Landslides 19, no. 4 (2022): 841–53. http://dx.doi.org/10.1007/s10346-021-01799-y.

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AbstractNowadays, several systems to set up landslide inventories exist although they rarely rely on automated or real-time updates. Mass media can provide reliable info about natural hazard events with a relatively high temporal and spatial resolution. The news publication about a natural disaster inside newspaper or crowdsourcing platforms allows a faster observation, survey, and classification of these phenomena. Several techniques have been developed for data mining inside social media for many natural events, but they have been rarely applied to the automatic extraction of “landslide even

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Corzo, U. G. "Semantic segmentation for images of tropical landscapes." Journal of Research in Engineering Science- JRES 3 (January 1, 2018): 135–46. http://dx.doi.org/10.33133/jres-3-2018-185.

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In the present work a method was established for semantic segmentation of cartographic images of Colombian tropical landscapes through the supervised analysis of groups of píxels in three classes: vegetation, water sources and other objects or strange elements in the nature of the tropical landscape that have Mainly human influence, the study of these classes allows recognizing those areas of píxels in a cartographic photograph where there may be events such as deforestation, illegal mining or illicit crops, it also allows detecting other phenomena such as flood zones or with the risk of erosi

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Vysotska, Ol'viya. "FUNCTIONAL ROLE OF STYLISTIC FIGURES: METAPHOR AND METONYMY." RESEARCH TRENDS IN MODERN LINGUISTICS AND LITERATURE 2 (November 7, 2019): 104–12. http://dx.doi.org/10.29038/2617-6696.2019.2.104.112.

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The article deals with the word-forming role of stylistic figures (metaphors and metonymy) in the formation of polysemantic words-terms. The common regularities of the use of stylistic figures in various spheres of the humanities, ways of their change (semantic landslides), especially the semantic filling of terms in professional texts are revealed. It is noted that the semantic paradigm of metaphor is a complex system-forming unit that forms a set of derivatives motivated by the same sign. A metamorphic nominative function, capable of forming new concepts, is also traced. It is generalized th

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Ahn, Eun-Young, and Seong-Yong Kim. "Digital Twin Application and Bibliometric Analysis for Digitization and Intelligence Studies in Geology and Deep Underground Research Areas." Data 8, no. 4 (2023): 73. http://dx.doi.org/10.3390/data8040073.

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As deep underground digital twins have not yet been established worldwide, this study extracted keywords from national or city-led digital twin practices and elements of digital twins and through these keywords selected research papers and topics that could contribute to the establishment of deep underground digital twins in the future. We applied the concept of digital twins in geology and underground research to collect 1702 papers from the Web of Science and conducted semantic network analysis and topic modeling. The keywords digital, three dimensions, and real time were placed in the middl

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Pyo, JongCheol, Kuk-jin Han, Yoonrang Cho, Doyeon Kim, and Daeyong Jin. "Generalization of U-Net Semantic Segmentation for Forest Change Detection in South Korea Using Airborne Imagery." Forests 13, no. 12 (2022): 2170. http://dx.doi.org/10.3390/f13122170.

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Forest change detection is essential to prevent the secondary damage occurring by landslides causing profound results to the environment, ecosystem, and human society. The remote sensing technique is a solid candidate for identifying the spatial distribution of the forest. Even though the acquiring and processing of remote sensing images are costly and time- and labor-consuming, the development of open source data platforms relieved these burdens by providing free imagery. The open source images also accelerate the generation of algorithms with large datasets. Thus, this study evaluated the ge

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Kubo, Shiori, Tatsuro Yamane, and Pang-jo Chun. "Study on Accuracy Improvement of Slope Failure Region Detection Using Mask R-CNN with Augmentation Method." Sensors 22, no. 17 (2022): 6412. http://dx.doi.org/10.3390/s22176412.

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We proposed an automatic detection method of slope failure regions using a semantic segmentation method called Mask R-CNN based on a deep learning algorithm to improve the efficiency of damage assessment in the event of slope failure disaster. There is limited research on detecting landslides by deep learning, and the lack of training data is an important issue to be resolved, as aerial photographs are not taken with sufficient frequency during a disaster. This study attempts to use CutMix-based augmentation to improve detection accuracy. We also compare the detection results obtained by augme

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Zhang, J. "CHARACTERISTICS COGNITION OF TYPICAL SURFACE GEOHAZARDS SCENE IN MINING AREAS AND REPRESENTATION OF GEO-INFOGRAPHICS SPECTRUM." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-4-2022 (May 18, 2022): 25–31. http://dx.doi.org/10.5194/isprs-annals-v-4-2022-25-2022.

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Abstract. The concept of surface geohazards in mining area is put forward, and the surface geohazards in mining area are divided into three categories: first is surface subsidence fissures, collapse, second is instability of structures, slope deformation hazards (high and steep slopes, landslides and collapses) , debris flows (debris flows, tailings reservoir dam break), third is vegetation cover degradation and environmental and ecological damage in mining area. Author analyses the research status of spatio-temporal characteristics of surface geohazards in mining areas and the representation

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Wang, Zhaoqiu, Tao Sun, Kun Hu, Yueting Zhang, Xiaqiong Yu, and Ying Li. "A Deep Learning Semantic Segmentation Method for Landslide Scene Based on Transformer Architecture." Sustainability 14, no. 23 (2022): 16311. http://dx.doi.org/10.3390/su142316311.

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Semantic segmentation technology based on deep learning has developed rapidly. It is widely used in remote sensing image recognition, but is rarely used in natural disaster scenes, especially in landslide disasters. After a landslide disaster occurs, it is necessary to quickly carry out rescue and ecological restoration work, using satellite data or aerial photography data to quickly analyze the landslide area. However, the precise location and area estimation of the landslide area is still a difficult problem. Therefore, we propose a deep learning semantic segmentation method based on Encoder

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Zhou, Yongxiu, Honghui Wang, Ronghao Yang, Guangle Yao, Qiang Xu, and Xiaojuan Zhang. "A Novel Weakly Supervised Remote Sensing Landslide Semantic Segmentation Method: Combing CAM and cycleGAN Algorithms." Remote Sensing 14, no. 15 (2022): 3650. http://dx.doi.org/10.3390/rs14153650.

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With the development of deep learning algorithms, more and more deep learning algorithms are being applied to remote sensing image classification, detection, and semantic segmentation. The landslide semantic segmentation of a remote sensing image based on deep learning mainly uses supervised learning, the accuracy of which depends on a large number of training data and high-quality data annotation. At this stage, high-quality data annotation often requires the investment of significant human effort. Therefore, the high cost of remote sensing landslide image data annotation greatly restricts th

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Li, Yankui, Wu Zhu, Jing Wu, Ruixuan Zhang, Xueyong Xu, and Ye Zhou. "DBSANet: A Dual-Branch Semantic Aggregation Network Integrating CNNs and Transformers for Landslide Detection in Remote Sensing Images." Remote Sensing 17, no. 5 (2025): 807. https://doi.org/10.3390/rs17050807.

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Deep learning-based semantic segmentation algorithms have proven effective in landslide detection. For the past decade, convolutional neural networks (CNNs) have been the prevailing approach for semantic segmentation. Nevertheless, the intrinsic limitations of convolutional operations hinder the acquisition of global contextual information. Recently, Transformers have garnered attention for their exceptional global modeling capabilities. This study proposes a dual-branch semantic aggregation network (DBSANet) by integrating ResNet and a Swin Transformer. A Feature Fusion Module (FFM) is design

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Dai, Xiangyan, and Lin Li. "A semantic segmentation network based on CNN and Swin transformer for landslide detection." Journal of Physics: Conference Series 2858, no. 1 (2024): 012026. http://dx.doi.org/10.1088/1742-6596/2858/1/012026.

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Abstract To address the issues of poor object localization, difficulty in object recognition, and inadequate segmentation performance of CNN-based semantic segmentation methods in landslide detection, a semantic segmentation model based on the Swin transformer and ResUNet architectures for landslide detection in remote sensing images is proposed in this paper. This method combines the global feature capabilities of the Swin transformer with the local feature extraction abilities of CNN and integrates our proposed RCCT module for landslide segmentation in remote sensing images. We apply this mo

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Nandhini Abirami, R., P. M. Durai Raj Vincent, Kathiravan Srinivasan, Usman Tariq, and Chuan-Yu Chang. "Deep CNN and Deep GAN in Computational Visual Perception-Driven Image Analysis." Complexity 2021 (April 15, 2021): 1–30. http://dx.doi.org/10.1155/2021/5541134.

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Computational visual perception, also known as computer vision, is a field of artificial intelligence that enables computers to process digital images and videos in a similar way as biological vision does. It involves methods to be developed to replicate the capabilities of biological vision. The computer vision’s goal is to surpass the capabilities of biological vision in extracting useful information from visual data. The massive data generated today is one of the driving factors for the tremendous growth of computer vision. This survey incorporates an overview of existing applications of de

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Du, Bowen, Zirong Zhao, Xiao Hu, et al. "Landslide susceptibility prediction based on image semantic segmentation." Computers & Geosciences 155 (October 2021): 104860. http://dx.doi.org/10.1016/j.cageo.2021.104860.

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Hu, Zekun, Bangjin Yi, Hui Li, et al. "Comparative Evaluation of State-of-the-Art Semantic Segmentation Networks for Long-Term Landslide Map Production." Sensors 23, no. 22 (2023): 9041. http://dx.doi.org/10.3390/s23229041.

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The production of long-term landslide maps (LAM) holds crucial importance in estimating landslide activity, vegetation disturbance, and regional stability. However, the availability of LAMs remains limited in many regions, despite the application of various machine-learning methods, deep-learning (DL) models, and ensemble strategies in landslide detection. While transfer learning is considered an effective approach to tackle this challenge, there has been limited exploration and comparison of the temporal transferability of state-of-the-art deep-learning models in the context of LAM production

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Yang, Fan, Xiaozhi Men, Yangsheng Liu, et al. "Estimation of Landslide and Mudslide Susceptibility with Multi-Modal Remote Sensing Data and Semantics: The Case of Yunnan Mountain Area." Land 12, no. 10 (2023): 1949. http://dx.doi.org/10.3390/land12101949.

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Landslide and mudslide susceptibility predictions play a crucial role in environmental monitoring, ecological protection, settlement planning, etc. Currently, multi-modal remote sensing data have been used for precise landslide and mudslide disaster prediction with spatial details, spectral information, or terrain attributes. However, features regarding landslide and mudslide susceptibility are often hidden in multi-modal remote sensing images, beyond the features extracted and learnt by deep learning approaches. This paper reports our efforts to conduct landslide and mudslide susceptibility p

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Xu, Hao, Li Wang, Bao Shu, Qin Zhang, and Xinrui Li. "Automatic Detection of Landslide Surface Cracks from UAV Images Using Improved U-Network." Remote Sensing 17, no. 13 (2025): 2150. https://doi.org/10.3390/rs17132150.

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Surface cracks are key indicators of landslide deformation, crucial for early landslide identification and deformation pattern analysis. However, due to the complex terrain and landslide extent, manual surveys or traditional digital image processing often face challenges with efficiency, precision, and interference susceptibility in detecting these cracks. Therefore, this study proposes a comprehensive automated pipeline to enhance the efficiency and accuracy of landslide surface crack detection. First, high-resolution images of landslide areas are collected using unmanned aerial vehicles (UAV

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Farmakis, Ioannis, David Bonneau, D. Jean Hutchinson, and Nicholas Vlachopoulos. "Targeted Rock Slope Assessment Using Voxels and Object-Oriented Classification." Remote Sensing 13, no. 7 (2021): 1354. http://dx.doi.org/10.3390/rs13071354.

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Reality capture technologies, also known as close-range sensing, have been increasingly popular within the field of engineering geology and particularly rock slope management. Such technologies provide accurate and high-resolution n-dimensional spatial representations of our physical world, known as 3D point clouds, that are mainly used for visualization and monitoring purposes. To extract knowledge from point clouds and inform decision-making within rock slope management systems, semantic injection through automated processes is necessary. In this paper, we propose a model that utilizes a seg

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Zhang, Rongchun, Shang Shi, Xuefeng Yi, et al. "A Slope Structural Plane Extraction Method Based on Geo-AINet Ensemble Learning with UAV Images." Remote Sensing 15, no. 5 (2023): 1441. http://dx.doi.org/10.3390/rs15051441.

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In the construction of large-scale water conservancy and hydropower transportation projects, the rock mass structural information is often used to evaluate and analyze various engineering geological problems such as high and steep slope stability, dam abutment stability, and natural rock landslide geological disasters. The complex shape and extremely irregular distribution of the structural planes make it challenging to identify and extract automatically. This study proposes a method for extracting structural planes from UAV images based on Geo-AINet ensemble learning. The UAV images of the sl

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