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Relevant bibliographies by topics / Coastline extraction

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Academic literature on the topic 'Coastline extraction'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Coastline extraction"

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Zhou, Xixuan, Jinyu Wang, Fengjie Zheng, Haoyu Wang, and Haitao Yang. "An Overview of Coastline Extraction from Remote Sensing Data." Remote Sensing 15, no. 19 (2023): 4865. http://dx.doi.org/10.3390/rs15194865.

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The coastal zone represents a unique interface between land and sea, and addressing the ecological crisis it faces is of global significance. One of the most fundamental and effective measures is to extract the coastline’s location on a large scale, dynamically, and accurately. Remote sensing technology has been widely employed in coastline extraction due to its temporal, spatial, and sensor diversity advantages. Substantial progress has been made in coastline extraction with diversifying data types and information extraction methods. This paper focuses on discussing the research progress rela

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Tahir, Hamzah, and Ami Hassan Md Din. "The Potential of Landsat 8 OLI Images in Coastline Identification: The Case Study of Basra, Iraq." Engineering, Technology & Applied Science Research 14, no. 1 (2024): 13041–46. http://dx.doi.org/10.48084/etasr.6580.

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Coastline extraction plays important functions in coastal resource management, natural resource preservation, and sustainable coastal development. Long-term records of Landsat data series are available for free downloading, being highly potential for coastline extraction. Furthermore, remote sensing imagery systems along with the application of digital image processing techniques can be utilized in coastline extraction. However, it is challenging to accurately extract coastlines with coarse spatial resolution due to the dynamic properties of various types of coastlines produced by sea-level ch

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Domazetović, Fran, Ante Šiljeg, Ivan Marić, Josip Faričić, Emmanuel Vassilakis, and Lovre Panđa. "Automated Coastline Extraction Using the Very High Resolution WorldView (WV) Satellite Imagery and Developed Coastline Extraction Tool (CET)." Applied Sciences 11, no. 20 (2021): 9482. http://dx.doi.org/10.3390/app11209482.

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The accurate extraction of a coastline is necessary for various studies of coastal processes, as well as for the management and protection of coastal areas. Very high-resolution satellite imagery has great potential for coastline extraction; however, noises in spectral data can cause significant errors. Here, we present a newly developed Coastal Extraction Tool (CET) that overcomes such errors and allows accurate and time-efficient automated coastline extraction based on a combination of WorldView-2 (WV-2) multispectral imagery and stereo-pair-derived digital surface model (DSM). Coastline ext

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Woo, H. S., K. S. Kwon, and B. G. Kim. "STUDY ON VERTEX INTERVAL DETERMINATION FOR CONSISTENT CALCULATION OF COASTLINE LENGTH." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 12, 2017): 169–73. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-169-2017.

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Coastline extraction and decision have important implications for efficient land management and national policy formulation. However, coastlines extracted without certain criteria are difficult to obtain consistent results for the same region, and therefore, have a lot of restrictions on land management and application. For example, in some South Korea coastlines surveyed in this study, it was confirmed that there is a large difference in coastline lengths calculated for the same area depending on how the vertex interval is set. In this study, we propose and discuss the guidelines to enable co

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Tang, Wei, Chengyi Zhao, Jing Lin, et al. "Improved Spectral Water Index Combined with Otsu Algorithm to Extract Muddy Coastline Data." Water 14, no. 6 (2022): 855. http://dx.doi.org/10.3390/w14060855.

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Based on the spectral reflection characteristics analysis of the muddy coastline in Jiangsu, an improved spectral water index (IWI) combined with the Otsu algorithm is proposed to extract muddy coastlines from Landsat Operational Land Imager (OLI) images. The IWI-extracted coastline results are compared with those extracted by the modified normalized difference water index (MNDWI), normalized difference water index (NDWI), enhanced water index (EWI), revised normalized different water index (RNDWI) and automated water extraction index (AWEI). The results show that the IWI is not affected by ti

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Ding, Yaxin, Xiaomei Yang, Hailiang Jin, et al. "Monitoring Coastline Changes of the Malay Islands Based on Google Earth Engine and Dense Time-Series Remote Sensing Images." Remote Sensing 13, no. 19 (2021): 3842. http://dx.doi.org/10.3390/rs13193842.

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The use of remote sensing to monitor coastlines with wide distributions and dynamic changes is significant for coastal environmental monitoring and resource management. However, most current remote sensing information extraction of coastlines is based on the instantaneous waterline, which is obtained by single-period imagery. The lack of a unified standard is not conducive to the dynamic change monitoring of a changeable coastline. The tidal range observation correction method can be used to correct coastline observation to a unified climax line, but it is difficult to apply on a large scale b

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Yang, Zhihai, Guangjun Wang, Lei Feng, Yuxian Wang, Guowei Wang, and Sihai Liang. "A Transformer Model for Coastline Prediction in Weitou Bay, China." Remote Sensing 15, no. 19 (2023): 4771. http://dx.doi.org/10.3390/rs15194771.

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The simulation and prediction of coastline changes are of great significance for the development and scientific management of coastal zones. Coastline changes are difficult to capture completely but appear significantly periodic over a long time series. In this paper, the transformer model is used to learn the changing trend of the coastline so as to deduce the position of the coastline in the coming year. First, we use the distance regularization level set evolution (DRLSE) model for instantaneous waterline extraction (IWE) from preprocessed Landsat time-series images from 2010–2020 in Weitou

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Adnan, Nor Aizam, Najiehah Norazman, Khairul Nizam Maulud, Ernieza Suhana Mokhtar, and Zaharah Mohd Yusoff. "Coastlines estimation and erosion rate assessment in Tuba Island, Langkawi using remotely-sensed digital imageries analysis." IOP Conference Series: Earth and Environmental Science 1240, no. 1 (2023): 012018. http://dx.doi.org/10.1088/1755-1315/1240/1/012018.

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Abstract Malaysia’s coastline is constantly exposed to ocean threats, resulting in coastal erosion and sea-level rise. Pulau Tuba’s is a part of Pulau Langkawi which known as a popular tourist destination in Malaysia. This study focuses on using remote sensing and Geographical Information System (GIS) tools to examine changes along the coastline of Pulau Tuba, Langkawi, during a six-year period. In this research, changes to the coastline were analyzed using Sentinel-2 imagery from 2016 to 2021, as a result of the comparison of coastline extraction methods. Coastline extraction methods used in

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Wang, Wenliang. "Automatic extraction of coastline based on Google Earth engine." International Journal of Computer Science and Information Technology 1, no. 1 (2023): 102–11. http://dx.doi.org/10.62051/ijcsit.v1n1.14.

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Traditional remote sensing image-based coastline extraction is limited by data volume and processing speed, and the extracted coastline is susceptible to noise. Therefore, this paper proposes a method based on the Google Earth Engine geospatial platform, which combines threshold segmentation, the Otsu method, and morphological algorithms. First, remote sensing images are preprocessed on the platform, and the Normalized Difference Water Index (NDWI) is calculated. Then, the Otsu method is used to calculate the NDWI threshold for water-land segmentation, resulting in a binary water-land image. N

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Zheng, Hongxia, Xiao Li, Jianhua Wan, Mingming Xu, Shanwei Liu, and Muhammad Yasir. "Automatic Coastline Extraction Based on the Improved Instantaneous Waterline Extraction Method and Correction Criteria Using SAR Imagery." Sustainability 15, no. 9 (2023): 7199. http://dx.doi.org/10.3390/su15097199.

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Coastlines with different morphologies form boundaries between the land and ocean, and play a vital role in tourism, integrated coastal zone management, and marine engineering. Therefore, determining how to extract the coastline from satellite images quickly, accurately, and intelligently without manual intervention has become a hot topic. However, the instantaneous waterline extracted directly from the image must be corrected to the coastline using the tide survey station data. This process is challenging due to the scarcity of tide stations. Therefore, an improved instantaneous waterline ext

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Dissertations / Theses on the topic "Coastline extraction"

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Kim, Kee-Tae. "Satellite mapping and automated feature extraction: geographic information system-based change detection of the Antarctic coast." The Ohio State University, 2004. http://rave.ohiolink.edu/etdc/view?acc_num=osu1072898409.

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Book chapters on the topic "Coastline extraction"

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Ma, Zhongyao, Zhong Liu, Jincai Huang, and Keyu Wu. "Coastline Classification and Extraction Based on Deep Learning." In Proceedings of 2022 10th China Conference on Command and Control. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6052-9_77.

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Alcaras, Emanuele, Angela Errico, Ugo Falchi, Claudio Parente, and Andrea Vallario. "Coastline Extraction from Optical Satellite Imagery and Accuracy Evaluation." In R3 in Geomatics: Research, Results and Review. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62800-0_26.

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Alcaras, Emanuele, Pier Paolo Amoroso, Francesco Giuseppe Figliomeni, Claudio Parente, and Andrea Vallario. "Machine Learning Approaches for Coastline Extraction from Sentinel-2 Images: K-Means and K-Nearest Neighbour Algorithms in Comparison." In Geomatics for Green and Digital Transition. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17439-1_27.

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Philp, Tom J., Adrian J. Champion, Kevin I. Hodges, et al. "Identifying Limitations when Deriving Probabilistic Views of North Atlantic Hurricane Hazard from Counterfactual Ensemble NWP Re-forecasts." In Hurricane Risk in a Changing Climate. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08568-0_10.

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AbstractDownward counterfactual analysis – or quantitatively estimating how our observed history could have been worse – is increasingly being used by the re/insurance industry to identify, quantify, and mitigate against as-yet-unrealised “grey-swan” catastrophic events. While useful for informing site-specific adaptation strategies, the extraction of probabilistic information remains intangible from such downside-only focused analytics. We hypothesise that combined upward and downward counterfactual analysis (i.e., how history could have been either better or worse) may allow us to obtain pro

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Li, Hui, Chenyi Jiang, Jinzhuang Shi, Qiyuan Xie, and Linhai Jing. "Coastline Extraction Based on Deep Learning Using Gaofen-1 and Ziyuan-3 Satellite Imagery." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2025. https://doi.org/10.3233/faia250120.

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The potential of Chinese high-spatial satellite imagery and deep learning models in coastline extraction were explored in this work. The performances of three deep learning models including U-Net, ResUnet, and SegNet were compared using 2-m resolution pansharpened products of GaoFen-1(GF1), and Ziyuan-3 (ZY3) imagery. The prediction results of ResUnet were significantly more accurate than those of U-Net and SegNet. The trained ResUnet model was then used to predict and extract the coastlines of Haikou City, Hainan Province. The 2-m resolution coastline products of Haikou City in 2016, 2018, an

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Wang, Ping, Yangchun Wan, and Fuzhou Duan. "The study of coastline extraction of the Pearl River Estuary based on ETM+ remote sensing image." In Advances in Energy Equipment Science and Engineering. CRC Press, 2015. http://dx.doi.org/10.1201/b19126-193.

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Devienne, Elsa. "Introduction." In Sand Rush. Oxford University PressNew York, 2024. http://dx.doi.org/10.1093/oso/9780197539750.003.0001.

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Abstract The introduction argues that while the Los Angeles shoreline is today one of the most iconic landscapes in the United States, it was not always the case. In the late 1920s, the beaches of Los Angeles were dirty, crowded, and eroded. Pollution from untreated sewage, industrial effluents, and oil extraction was a critical issue. In addition, most of the coastline was private and inaccessible to the public. Beginning in the 1920s, and reaching a peak in the postwar years, reinventing the beach for the modern age became the obsession of a group of LA-based engineers, urban planners, busin

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Brooks, Shelley Alden. "Jeffers’s Country." In Big Sur. University of California Press, 2017. http://dx.doi.org/10.1525/california/9780520294417.003.0002.

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Chapter 1 opens with poet Robinson Jeffers’s introduction to the Big Sur landscape in 1914. Big Sur's rugged setting had long served as an obstacle to settlement or exploration, so that in the early century this coastline was sparsely populated and without modern technologies. Human endeavors had produced few permanent edifices, despite centuries of habitation and decades of small-scale extractive industries. The Spanish name for this coastline, “el sur,” represented how most people viewed the area in the eighteenth, nineteenth, and even into the twentieth centuries: as a rather inconsequentia

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Ng, Wing Lon. "Improving Long-Term Financial Risk Forecasts using High-Frequency Data and Scaling Laws." In Simulation in Computational Finance and Economics. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2011-7.ch013.

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This chapter uses the abundance of high frequency data to estimate scaling law models and then apply appropriately scaled measures to provide long-term market risk forecasts. The objective is to analyse extreme price movements from tick-by-tick real-time data to trace the footprints of traders that eventually form the overall movement of market prices (price coastline) and potential bubbles. The framework is applied to empirical limit order book data from the London Stock Exchange. The sample period ranges from June 2007 to June 2008 and covers the start of the subprime crisis that later escal

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Villegas-Sierra, J., F. Cortés-Pérez, and A. Solís-Fierro. "Las Playas de la Isla del Carmen, Prácticas Sociales del Turismo Convencional." In Tópicos Selectos de Contaminación Ambiental. EPOMEX-UAC, 2025. https://doi.org/10.26359/epomex01202518.

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Conventional tourism is a social practice embraced by the foreign population who view the beaches of the Mexican coasts as a way to highlight their social status. This was done without much consideration for the local population living along the coasts. These social practices during the 20th century also held significance for the national dominant classes. As sun and beach tourism became more popular, it also gained meaning for all social classes, as the State promoted it as a social security right. However, these social practices resulted in environmental pollution problems. Therefore, in the

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Conference papers on the topic "Coastline extraction"

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Passarello, Gianpaolo, Sergio Vitale, Giampaolo Ferraioli, Gilda Schirinzi, and Vito Pascazio. "Coastline Extraction Using SAR Images and Deep Learning." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642204.

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Qian, Junfeng, Yuan Ji, Ruman Yang, Wenyang Cai, and Wei Xia. "A scene classification supported coastline extraction method on high-resolution remote sensing image." In International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2024), edited by Xin Xu and Azlan bin Mohd Zain. SPIE, 2025. https://doi.org/10.1117/12.3061830.

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Amoroso, Pier Paolo, Angelo Ciaramella, Alessio Ferone, Claudio Parente, and Antonino Staiano. "Application of Decision Tree Algorithm on Landsat 9 OLI-2 Images for Coastline Extraction." In 2024 IEEE International Workshop on Metrology for the Sea; Learning to Measure Sea Health Parameters (MetroSea). IEEE, 2024. https://doi.org/10.1109/metrosea62823.2024.10765729.

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Liu, Shiyu. "Recent Extraction and Spatial Analysis of Yangtze River Estuary Coastline Using Multi-Source Remote Sensing Data." In International Conference on Innovations in Applied Mathematics, Physics and Astronomy. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0013042900004601.

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Gong, Shaojun, and Chao Chen. "A New Method for Coastline Extracting Considering the Wetness of Ground Objects from Remote Sensing Images." In IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2024. http://dx.doi.org/10.1109/igarss53475.2024.10642103.

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O’Sullivan, Conor, Seamus Coveney, Xavier Monteys, and Soumyabrata Dev. "Automated Coastline Extraction Using Edge Detection Algorithms." In IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2023. http://dx.doi.org/10.1109/igarss52108.2023.10282621.

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Xing, Kun, Yili Fu, and Feng Zhou. "A new coastline extraction in remote sensing images." In SPIE Remote Sensing, edited by Lorenzo Bruzzone. SPIE, 2012. http://dx.doi.org/10.1117/12.970478.

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Yang, Heein, Dal-Guen Lee, Tu-Hwan Kim, Josaphat Tetuko Sri Sumantyo, and Jae-Hyun Kim. "Semi-automatic coastline extraction method using Synthetic Aperture Radar images." In 2014 16th International Conference on Advanced Communication Technology (ICACT). Global IT Research Institute (GIRI), 2014. http://dx.doi.org/10.1109/icact.2014.6779049.

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Del Frate, Fabio, Daniele Latini, Andrea Minchella, and Francesco Palazzo. "A new automatic technique for coastline extraction from SAR images." In SPIE Remote Sensing, edited by Claudia Notarnicola, Simonetta Paloscia, and Nazzareno Pierdicca. SPIE, 2012. http://dx.doi.org/10.1117/12.976856.

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Ge, Xizhi, Xiliang Sun, and Zhaoqin Liu. "Object-oriented coastline classification and extraction from remote sensing imagery." In Remote Sensing of the Environment: 18th National Symposium on Remote Sensing of China, edited by Qingxi Tong, Jie Shan, and Boqin Zhu. SPIE, 2014. http://dx.doi.org/10.1117/12.2063845.

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