Relevant bibliographies by topics / Semantic landslides

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Semantic landslides'

Author: Grafiati
Published: 28 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Semantic landslides"

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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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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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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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Dissertations / Theses on the topic "Semantic landslides"

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Біла, Ю. А. "Лексико-семантичні транспозиції в лексиці євролекту". Thesis, Сумський державний університет, 2018. http://essuir.sumdu.edu.ua/handle/123456789/67322.

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Під поняттям «євролект» розуміють фахову підмову, що використовується для укладання офіційних текстів ЄС [2, c. 2]. Його офіційна мета – обслуговування та позначення реалій різноманітних сфер діяльності ЄС. Оскільки лексика євролекту є трансплантованою з двох робочих мов ЄС – англійської та французької, її реалізація у новому мовному контексті актуалізує семантичні зсуви.
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Book chapters on the topic "Semantic landslides"

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Syed, Hasnain Murtaza, Mahdi Maktabdar Oghaz, and Lakshmi Babu Saheer. "Semantic Segmentation for Landslide Detection Using Segformer." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-77918-3_3.

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Zhou, Yongxiu, Honghui Wang, Guangle Yao, Mingzhe Liu, and Qiang Xu. "A Novel Remote Sensing Landslide Semantic Segmentation Method: Using CycleGAN-Based Change Detection Algorithms." In Environmental Science and Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9069-6_3.

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Huang, Shiya, Qiang Li, Jiajun Li, and Jinzheng Lu. "4RATFNet: Four-Dimensional Residual-Attention Improved-Transfer Few-Shot Semantic Segmentation Network for Landslide Detection." In Advances in Computer Graphics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-50075-6_6.

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Llaves, Alejandro, and Thomas Everding. "Discovering Geosensor Data By Means of an Event Abstraction Layer." In Geographic Information Systems. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2038-4.ch120.

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Environmental monitoring is a critical process in areas potentially affected by natural disasters. Nowadays, the distributed processing of vast amounts of heterogeneous sensor data in real time is a challenging task. Event processing tools allow creating an event abstraction layer on top of sensor data. Users can define event patterns to filter in real-time the information they are interested in and avoid irrelevant data. Extreme events are usually related to other environmental occurrences, e.g. landslides are related (among others) to precipitations and earthquakes. To be able to determine w
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Llaves, Alejandro, and Thomas Everding. "Discovering Geosensor Data By Means of an Event Abstraction Layer." In Discovery of Geospatial Resources. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0945-7.ch006.

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Environmental monitoring is a critical process in areas potentially affected by natural disasters. Nowadays, the distributed processing of vast amounts of heterogeneous sensor data in real time is a challenging task. Event processing tools allow creating an event abstraction layer on top of sensor data. Users can define event patterns to filter in real-time the information they are interested in and avoid irrelevant data. Extreme events are usually related to other environmental occurrences, e.g. landslides are related (among others) to precipitations and earthquakes. To be able to determine w
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Conference papers on the topic "Semantic landslides"

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Yali, Roy, Pablo Fonseca, and Cesar Beltran. "Assessment of Semantic Segmentation Models for Landslide Monitoring Using Satellite Imagery in Peruvian Andes." In LatinX in AI at Neural Information Processing Systems Conference 2023. Journal of LatinX in AI Research, 2023. http://dx.doi.org/10.52591/lxai202312108.

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In the domain of machine learning, one persistent challenge is the availability of ample data, especially pertinent to computer vision. Moreover, this challenge is amplified within the realm of remote sensing, where annotations for addressing problems are frequently scarce. This manuscript critically examines the daunting task of monitoring a geophysical phenomenon —landslides— within the Peruvian landscape, a nation profoundly impacted by such events on a global scale. In this paper, we present three contributions in that direction. Our first contribution is to expand a well-known satellite i
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Achariyaviriya, Witthawin, Toshiaki Kondo, Jessada Karnjana, and Takayuki Nishio. "Landslide Semantic Segmentation Using Satellite Imagery." In 2022 19th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2022. http://dx.doi.org/10.1109/ecti-con54298.2022.9795537.

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Pargaonkar, Atharva, Shreyas Konjerla, Atharva Pansare, and Prof Pranali Kosamkar. "Remote Landslide Detection Using Semantic Segmentation." In 2023 IEEE 11th Region 10 Humanitarian Technology Conference (R10-HTC). IEEE, 2023. http://dx.doi.org/10.1109/r10-htc57504.2023.10461895.

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Qu, Hanqi, Chengda Lu, Yutong Pan, Yuxuan Zeng, and Min Wu. "Semantic Segmentation of Historical Landslide Based on Improved U-Net." In 2023 42nd Chinese Control Conference (CCC). IEEE, 2023. http://dx.doi.org/10.23919/ccc58697.2023.10240652.

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Li, Zhun, and Yonggang Guo. "Semantic segmentation of landslide images in Nyingchi region based on PSPNet network." In 2020 7th International Conference on Information Science and Control Engineering (ICISCE). IEEE, 2020. http://dx.doi.org/10.1109/icisce50968.2020.00256.

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Zhang, Qin, Jie Zhang, Wencheng Sun, and ZhangJian Qin. "Landslide Recognition in High Resolution Remote Sensing Images Based on Semantic Segmentation." In 2022 10th International Conference on Intelligent Computing and Wireless Optical Communications (ICWOC). IEEE, 2022. http://dx.doi.org/10.1109/icwoc55996.2022.9809850.

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Zhou, Yongxiu, Honghui Wang, Ronghao Yang, Dalan Xie, and Jie Liu. "Semantic Segmentation Algorithm of Landslide Based on Remote Sensing Image and DEM." In 2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2022. http://dx.doi.org/10.1109/prai55851.2022.9904119.

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Liu, Jie, Ying Liu, Yongxiu Zhou, and Yiru Wang. "Comparison of Deep Learning Methods for Landslide Semantic Segmentation Based on Remote Sensing Images." In 2022 5th International Conference on Pattern Recognition and Artificial Intelligence (PRAI). IEEE, 2022. http://dx.doi.org/10.1109/prai55851.2022.9904163.

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Musaev, Aibek, De Wang, Saajan Shridhar, Chien-An Lai, and Calton Pu. "Toward a Real-Time Service for Landslide Detection: Augmented Explicit Semantic Analysis and Clustering Composition Approaches." In 2015 IEEE International Conference on Web Services (ICWS). IEEE, 2015. http://dx.doi.org/10.1109/icws.2015.74.

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