Abstract
Coastlines are vital geographical features that interact dynamically with land and sea, making their accurate mapping essential for coastal management, environmental monitoring and climate adaptation. Recent advances in remote sensing technology and the increasing availability of various data sources, such as satellite imagery, unmanned aerial vehicle (UAV) data and synthetic aperture radar (SAR) imagery, have enabled more accurate and dynamic extraction of the coastline. In this context, deep learning models have proven to be a powerful tool that can handle the complexity of shoreline detection in different coastal geomorphologies. This review provides a comprehensive analysis of the current literature and focuses on the development and application of deep learning techniques for shoreline extraction using remote sensing data. In particular, the study highlights the performance of models such as U-Net variants, Convolutional Neural Networks (CNNs) and hybrid approaches and emphasizes the impact of different input data types on model accuracy. The study also addresses important challenges such as the need for high-resolution datasets, the difficulty of dealing with different coastal environments, and the limitations of generalizing across different geographical regions. By analyzing the strengths and limitations of these models, this systematic review offers practical insights into existing methods and highlights key directions for future research.




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References
Aghdami-Nia M, Shah-Hosseini R, Rostami A, Homayouni S (2022) Automatic coastline extraction through enhanced sea-land segmentation by modifying Standard U-Net. Int J Appl Earth Obs Geoinf 109:102785. https://doi.org/10.1016/j.jag.2022.102785
Ahmed A, Drake F, Nawaz R, Woulds C (2018) Where is the coast? Monitoring coastal land dynamics in Bangladesh: An integrated management approach using GIS and remote sensing techniques. Ocean Coast Manag 151:10–24. https://doi.org/10.1016/j.ocecoaman.2017.10.030
Al-Hatrushi S, Ramadan E, Charabi Y (2015) Application of Geo-Processing Model for a Quantitative Assessment of Coastal Exposure and Sensitivity to Sea Level Rise in the Sultanate of Oman. Am J Clim Chang 04(04):379–384. https://doi.org/10.4236/ajcc.2015.44030
Arpitha M, Ahmed SA, Harishnaika N (2023) Land use and land cover classification using machine learning algorithms in google earth engine. Earth Sci Inf 16(4):3057–3073. https://doi.org/10.1007/S12145-023-01073-W/TABLES/5
Asokan A, Anitha J, Ciobanu M, Gabor A, Naaji A, Hemanth DJ (2020) Image Processing Techniques for Analysis of Satellite Images for Historical Maps Classification—An Overview. Applied Sciences 10(12):4207. https://doi.org/10.3390/APP10124207
Babbar, J., & Rathee, N. (2019). Satellite Image Analysis: A Review. Proceedings of 2019 3rd IEEE International Conference on Electrical, Computer and Communication Technologies, ICECCT 2019. https://doi.org/10.1109/ICECCT.2019.8869481
Behling R, Milewski R, Chabrillat S (2018) Spatiotemporal shoreline dynamics of Namibian coastal lagoons derived by a dense remote sensing time series approach. Int J Appl Earth Obs Geoinf 68:262–271. https://doi.org/10.1016/J.JAG.2018.01.009
Bengoufa, S., Niculescu, S., Mihoubi, M. K., Belkessa, R., & Abbad, K. (2021a). ROCKY SHORELINE EXTRACTION USING A DEEP LEARNING MODEL AND OBJECT-BASED IMAGE ANALYSIS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2021(B3-2021), 23–29. 10.5194/isprs-archives-XLIII-B3-2021-23-2021
Bengoufa, S., Niculescu, S., Mihoubi, M. K., Belkessa, R., Rami, A., Rabehi, W., & Abbad, K. (2021b). Machine learning and shoreline monitoring using optical satellite images: case study of the Mostaganem shoreline, Algeria. Journal of Applied Remote Sensing, 15(02). https://doi.org/10.1117/1.JRS.15.026509
Blais, M.-A., & Akhloufi, M. A. (2021). Deep learning for low altitude coastline segmentation. In W. “Will” Hou (Ed.), Ocean Sensing and Monitoring XIII (Vol. 11752, p. 16). SPIE. https://doi.org/10.1117/12.2586977
Boussetta A, Niculescu S, Bengoufa S, Zagrarni MF (2023) Deep and machine learning methods for the (semi-)automatic extraction of sandy shoreline and erosion risk assessment basing on remote sensing data (case of Jerba island -Tunisia). Remote Sensing Applications: Society and Environment 32:101084. https://doi.org/10.1016/j.rsase.2023.101084
Çelik Oİ, Gazioğlu C (2022) Coast type based accuracy assessment for coastline extraction from satellite image with machine learning classifiers. The Egyptian Journal of Remote Sensing and Space Science 25(1):289–299. https://doi.org/10.1016/j.ejrs.2022.01.010
CEOS Database. (2020). A Catalogue of Earth Observation Satellites | CEOS Database. https://database.eohandbook.com/index.aspx
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. Proceedings of the European Conference on Computer Vision, 801–818. https://github.com/tensorflow/models/tree/master/
Chen J-C, Wang Y-M (2020) Comparing Activation Functions in Modeling Shoreline Variation Using Multilayer Perceptron Neural Network. Water 12(5):1281. https://doi.org/10.3390/w12051281
Cheng D, Meng G, Cheng G, Pan C (2017) SeNet: Structured Edge Network for Sea-Land Segmentation. IEEE Geosci Remote Sens Lett 14(2):247–251. https://doi.org/10.1109/LGRS.2016.2637439
Cracknell MJ, Reading AM (2014) Geological mapping using remote sensing data: A comparison of five machine learning algorithms, their response to variations in the spatial distribution of training data and the use of explicit spatial information. Comput Geosci 63:22–33. https://doi.org/10.1016/J.CAGEO.2013.10.008
Dang KB, Vu KC, Nguyen H, Nguyen DA, Nguyen TDL, Pham TPN, Giang TL, Nguyen HD, Do TH (2022) Application of deep learning models to detect coastlines and shorelines. J Environ Manag 320:115732. https://doi.org/10.1016/j.jenvman.2022.115732
Demir, N., Kaynarca, M., & Oy, S. (2016). EXTRACTION OF COASTLINES WITH FUZZY APPROACH USING SENTINEL-1 SAR IMAGE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B7, 747–751. 10.5194/ISPRS-ARCHIVES-XLI-B7-747-2016
Ding Z, Su F, Zhang J, Zhang Y, Luo S, Tang X (2019) Clustering Coastal Land Use Sequence Patterns along the Sea-Land Direction: A Case Study in the Coastal Zone of Bohai Bay and the Yellow River Delta. China. Remote Sensing 11(17):2024. https://doi.org/10.3390/RS11172024
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). AN IMAGE IS WORTH 16X16 WORDS: TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE. ICLR 2021 - 9th International Conference on Learning Representations.
Ennouali Z, Fannassi Y, Benmohammadi A, Al-Mutiry M, Masria A (2023) Shoreline change detection along North Sebou-Moulay Bousselham, based on remote sensing analysis. Regional Studies in Marine Science 62:102935. https://doi.org/10.1016/j.rsma.2023.102935
Erdem F, Bayram B, Bakirman T, Bayrak OC, Akpinar B (2021) An ensemble deep learning based shoreline segmentation approach (WaterNet) from Landsat 8 OLI images. Adv Space Res 67(3):964–974. https://doi.org/10.1016/j.asr.2020.10.043
Fanikiso L, Shoko M (2024) Deep Learning-based Derivatives for Shoreline Change Detection in Cape Town, South Africa. South African Journal of Geomatics 13(1):51–64. https://doi.org/10.4314/sajg.v13i1.4
Gao A, Ai T, Yu H, Xiao T, Chen Y, Li J, Huang H (2024) A vector-based coastline shape classification approach using sequential deep learning model. Int J Appl Earth Obs Geoinf 129:103810. https://doi.org/10.1016/j.jag.2024.103810
García-Rubio G, Huntley D, Russell P (2015) Evaluating shoreline identification using optical satellite images. Mar Geol 359:96–105. https://doi.org/10.1016/J.MARGEO.2014.11.002
Gens R (2010) Remote sensing of coastlines: detection, extraction and monitoring. Int J Remote Sens 31(7):1819–1836. https://doi.org/10.1080/01431160902926673
Giang Linh, T., Dang Kinh, B., & Bui Thanh, Q. (2023). Coastline and shoreline change assessment in sandy coasts based on machine learning models and high-resolution satellite images. Vietnam Journal of Earth Sciences, 45(2), 251–270. https://doi.org/10.15625/2615-9783/18407
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems, 27. http://www.github.com/goodfeli/adversarial
Heidler K, Mou L, Baumhoer C, Dietz A, Zhu XX (2022) HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline. IEEE Trans Geosci Remote Sens 60:1–14. https://doi.org/10.1109/TGRS.2021.3064606
Hochreiter S, Schmidhuber J (1997) Long Short-Term Memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Hurtik P, Vajgl M (2021) Coastline extraction from ALOS-2 satellite SAR images. Remote Sensing Letters 12(9):879–889. https://doi.org/10.1080/2150704X.2021.1944691
IPCC. (2022). The Ocean and Cryosphere in a Changing Climate. Cambridge University Press. https://doi.org/10.1017/9781009157964
Jing W, Cui B, Lu Y, Huang L (2021) BS-Net: Using Joint-Learning Boundary and Segmentation Network for Coastline Extraction from Remote Sensing Images. Remote Sensing Letters 12(12):1260–1268. https://doi.org/10.1080/2150704X.2021.1979271
Jo, T. (2021). Machine learning foundations: Supervised, unsupervised, and advanced learning. Machine Learning Foundations: Supervised, Unsupervised, and Advanced Learning, 1–391. https://doi.org/10.1007/978-3-030-65900-4/COVER
Karaman M (2021) Comparison of thresholding methods for shoreline extraction from Sentinel-2 and Landsat-8 imagery: Extreme Lake Salda, track of Mars on Earth. J Environ Manage 298:113481. https://doi.org/10.1016/J.JENVMAN.2021.113481
Karras T, Laine S, Aila T (2021) A Style-Based Generator Architecture for Generative Adversarial Networks. IEEE Trans Pattern Anal Mach Intell 43(12):4217–4228. https://doi.org/10.1109/TPAMI.2020.2970919
Kumar L, Afzal MS, Afzal MM (2020) Mapping shoreline change using machine learning: a case study from the eastern Indian coast. Acta Geophys 68(4):1127–1143. https://doi.org/10.1007/s11600-020-00454-9
Li R, Liu W, Yang L, Sun S, Hu W, Zhang F, Li W (2018) DeepUNet: A Deep Fully Convolutional Network for Pixel-Level Sea-Land Segmentation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11(11):3954–3962. https://doi.org/10.1109/JSTARS.2018.2833382
Li X, Liu B, Zheng G, Ren Y, Zhang S, Liu Y, Gao L, Liu Y, Zhang B, Wang F (2020) Deep-learning-based information mining from ocean remote-sensing imagery. Natl Sci Rev 7:1584–1605. https://doi.org/10.1093/nsr/nwaa047
Ma, Z., Liu, Z., Huang, J., & Wu, K. (2022). Coastline Classification and Extraction Based on Deep Learning. In Lecture Notes in Electrical Engineering: Vol. 949 LNEE (pp. 858–871). Springer, Singapore. https://doi.org/10.1007/978-981-19-6052-9_77
Makalesi, A., & Özkaya, U. (2020). Automatic Target Recognition (ATR) from SAR Imaginary by Using Machine Learning Techniques. European Journal of Science and Technology, 165–169. https://doi.org/10.31590/EJOSAT.802811
Manaf SA, Mustapha N, Sulaiman MN, Husin NA, Abdul Hamid MR (2018) Change analysis on historical shorelines extracted from medium resolution satellite images: a case study on the southern coast of Peninsular Malaysia. IOP Conference Series: Earth and Environmental Science 169(1):012101. https://doi.org/10.1088/1755-1315/169/1/012101
Mao Y, Harris DL, Xie Z, Phinn S (2021) Efficient measurement of large-scale decadal shoreline change with increased accuracy in tide-dominated coastal environments with Google Earth Engine. ISPRS J Photogramm Remote Sens 181:385–399. https://doi.org/10.1016/J.ISPRSJPRS.2021.09.021
McInnes, M. D. F., Moher, D., Thombs, B. D., McGrath, T. A., Bossuyt, P. M., Clifford, T., Cohen, J. F., Deeks, J. J., Gatsonis, C., Hooft, L., Hunt, H. A., Hyde, C. J., Korevaar, D. A., Leeflang, M. M. G., Macaskill, P., Reitsma, J. B., Rodin, R., Rutjes, A. W. S., Salameh, J.-P., … Willis, B. H. (2018). Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies. JAMA, 319(4), 388. https://doi.org/10.1001/jama.2017.19163
McMichael C, Dasgupta S, Ayeb-Karlsson S, Kelman I (2020) A review of estimating population exposure to sea-level rise and the relevance for migration. Environ Res Lett 15(12):123005. https://doi.org/10.1088/1748-9326/abb398
Minghelli A, Spagnoli J, Lei M, Chami M, Charmasson S (2020) Shoreline Extraction from WorldView2 Satellite Data in the Presence of Foam Pixels Using Multispectral Classification Method. Remote Sensing 12(16):2664. https://doi.org/10.3390/RS12162664
Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, Shekelle P, Stewart LA (2015) Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev 4(1):1. https://doi.org/10.1186/2046-4053-4-1
Monteys X, Hedley JD, Vahtmäe E, Wattelez G, Dupouy C, Juillot F (2022) Unsupervised Optical Classification of the Seabed Color in Shallow Oligotrophic Waters from Sentinel-2 Images: A Case Study in the Voh-Koné-Pouembout Lagoon (New Caledonia). Remote Sensing 14(4):836. https://doi.org/10.3390/RS14040836
Narumalani S, Mishra DR, Burkholder J, Merani PBT, Willson G (2006) A Comparative Evaluation of ISODATA and Spectral Angle Mapping for the Detection of Saltcedar Using Airborne Hyperspectral Imagery. Geocarto Int 21(2):59–66. https://doi.org/10.1080/10106040608542384
Okhrimchuk, R., Demidov, V., & Brudko, K. (2022). Semantic Segmentation of Western Crimean Coastline for High-Resolution Satellite Images using Deep Learning Based on U-Net Architecture. 16th International Conference Monitoring of Geological Processes and Ecological Condition of the Environment, 2022(1), 1–5. https://doi.org/10.3997/2214-4609.2022580213
Oliver, A., Odena, A., Raffel, C., Cubuk, E. D., & Goodfellow Google Brain, I. J. (2018). Realistic Evaluation of Deep Semi-Supervised Learning Algorithms. Advances in Neural Information Processing Systems, 31. https://github.com/brain-research/realistic-ssl-evaluation
Pina P, Vieira G (2022) UAVs for Science in Antarctica. Remote Sensing 14(7):1610. https://doi.org/10.3390/RS14071610
Pucino N, Kennedy DM, Young M, Ierodiaconou D (2022) Assessing the accuracy of Sentinel-2 instantaneous subpixel shorelines using synchronous UAV ground truth surveys. Remote Sens Environ 282:113293. https://doi.org/10.1016/j.rse.2022.113293
Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9351, pp. 234–241). Springer, Cham. https://doi.org/10.1007/978-3-319-24574-4_28
Santos CA, do Nascimento TV, Mishra M, da Silva RM (2021) Analysis of long- and short-term shoreline change dynamics: A study case of João Pessoa city in Brazil. Science of The Total Environment 769:144889. https://doi.org/10.1016/J.SCITOTENV.2020.144889
Seale C, Redfern T, Chatfield P, Luo C, Dempsey K (2022) Coastline detection in satellite imagery: A deep learning approach on new benchmark data. Remote Sens Environ 278:113044. https://doi.org/10.1016/J.RSE.2022.113044
Shamseer L, Moher D, Clarke M, Ghersi D, Liberati A, Petticrew M, Shekelle P, Stewart LA (2015) Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ 349:g7647. https://doi.org/10.1136/bmj.g7647
Shirmard H, Farahbakhsh E, Müller RD, Chandra R (2022) A review of machine learning in processing remote sensing data for mineral exploration. Remote Sens Environ 268:112750. https://doi.org/10.1016/J.RSE.2021.112750
Sun S, Mu L, Feng R, Chen Y, Han W (2024) Quadtree decomposition-based Deep learning method for multiscale coastline extraction with high-resolution remote sensing imagery. Science of Remote Sensing 9:100112. https://doi.org/10.1016/j.srs.2023.100112
Thanh Doan N (2021) Improving the efficiency of using deep learning model to determine shoreline position in high-resolution satellite imagery. E3S Web of Conferences 310:04002. https://doi.org/10.1051/e3sconf/202131004002
Tsiakos C-AD, Chalkias C (2023) Use of Machine Learning and Remote Sensing Techniques for Shoreline Monitoring: A Review of Recent Literature. Appl Sci 13(5):3268. https://doi.org/10.3390/app13053268
Vaswani, A., Brain, G., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need. 31st Conference on Neural Information Processing Systems (NIPS 2017). https://user.phil.hhu.de/~cwurm/wp-content/uploads/2020/01/7181-attention-is-all-you-need.pdf
Vicens-Miquel M, Medrano FA, Tissot PE, Kamangir H, Starek MJ, Colburn K (2022) A Deep Learning Based Method to Delineate the Wet/Dry Shoreline and Compute Its Elevation Using High-Resolution UAS Imagery. Remote Sensing 14(23):5990. https://doi.org/10.3390/rs14235990
Wang Y, Marsooli R (2021) Dynamic modeling of sea-level rise impact on coastal flood hazard and vulnerability in New York City’s built environment. Coast Eng 169:103980. https://doi.org/10.1016/j.coastaleng.2021.103980
Wang Y, Liu Y, Jin S, Sun C, Wei X (2019) Evolution of the topography of tidal flats and sandbanks along the Jiangsu coast from 1973 to 2016 observed from satellites. ISPRS J Photogramm Remote Sens 150:27–43. https://doi.org/10.1016/J.ISPRSJPRS.2019.02.001
Wang N, Chen Q, Chen Z (2022) Reconstruction of nearshore wave fields based on physics-informed neural networks. Coast Eng 176:104167. https://doi.org/10.1016/j.coastaleng.2022.104167
Wei X, Zheng W, Xi C, Shang S (2021) Shoreline Extraction in SAR Image Based on Advanced Geometric Active Contour Model. Remote Sensing 13(4):642. https://doi.org/10.3390/RS13040642
Xu S, Ye N, Xu S (2019) A new method for shoreline extraction from airborne LiDAR point clouds. Remote Sensing Letters 10(5):496–505. https://doi.org/10.1080/2150704X.2019.1569277
Yao L, Kanoulas D, Ji Z, Liu Y (2021) ShorelineNet: An Efficient Deep Learning Approach for Shoreline Semantic Segmentation for Unmanned Surface Vehicles. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021:5403–5409. https://doi.org/10.1109/IROS51168.2021.9636614
Yao J, Wu J, Xiao C, Zhang Z, Li J (2022) The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine. Remote Sensing 14(12):2758. https://doi.org/10.3390/RS14122758
Zhang L, Zhang L, Du B (2016) Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art. IEEE Geoscience and Remote Sensing Magazine 4(2):22–40. https://doi.org/10.1109/MGRS.2016.2540798
Zhao Q, Yu L, Du Z, Peng D, Hao P, Zhang Y, Gong P (2022) An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends. Remote Sensing 14(8):1863. https://doi.org/10.3390/RS14081863/S1
Zhu H, Fang Q, Huang Y, Xu K (2020) Semi-supervised method for image texture classification of pituitary tumors via CycleGAN and optimized feature extraction. BMC Med Inform Decis Mak 20(1):215. https://doi.org/10.1186/s12911-020-01230-x
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We thank University Malaysia Terengganu (UMT) for providing the facilities for this study.
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This study was conducted under the Research Intensified Grant Scheme (RIGS) (vote no: 55436) funding provided by the University Malaysia Terengganu Research Fund (DP-UMT).
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Author Contributions: Conceptualization, S.K.; methodology, A.BP. and S.K.; writing—original draft preparation, S.K., A.B.P, writing—review and editing, A.B.P., M.B.; visualization, E.H.A, M.F. A, S.B.H. All authors have read and agreed to the published version of the manuscript.
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Khurram, S., Pour, A.B., Bagheri, M. et al. Developments in deep learning algorithms for coastline extraction from remote sensing imagery: a systematic review. Earth Sci Inform 18, 292 (2025). https://doi.org/10.1007/s12145-025-01805-0
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DOI: https://doi.org/10.1007/s12145-025-01805-0