VEdge_Detector: automated coastal vegetation edge detection using a convolutional neural network

References

• Almonacid-Caballer, J., E. Sánchez-García, J. E. Pardo-Pascual, A. A. Balaguer-Beser, and J. Palomar-Vázquez. 2016. “Evaluation of Annual Mean Shoreline Position Deduced from Landsat Imagery as a Mid-term Coastal Evolution Indicator.” Marine Geology 372: 79–88. doi:https://doi.org/10.1016/j.margeo.2015.12.015.
   Web of Science ®Google Scholar
• Arbelaez, P., C. Fowlkes, and M. Martin 2007. “The Berkeley Segmentation Dataset and Benchmark.” Available at: https://www2.eecs.berkeley.edu/ Accessed 13 January 2020
   Google Scholar
• Bayram, B., F. Erdem, B. Akpinar, A. K. Ince, S. Bozkurt, H. C. Reis, and D. Z. Seker. 2017. “The efficiency of random forest method for shoreline extraction from LANDSAT-8 and GOKTURK-2 imageries.” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 4: 141–145. doi:https://doi.org/10.5194/isprs-annals-IV-4-W4-141-2017.
   Google Scholar
• Boak, E. H., and I. L. Turner. 2005. “Shoreline definition and detection: a review.” Journal of Coastal Research 214: 688–703. doi:https://doi.org/10.2112/03-0071.1.
   Web of Science ®Google Scholar
• Breiman, L. 2001. “Random forests.” Machine Learning 45 (1): 5–32. doi:https://doi.org/10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Brock, J. C., and S. J. Purkis. 2009. “The emerging role of LiDAR remote sensing in coastal research and resource management.” Journal of Coastal Research 53: 1–5. doi:https://doi.org/10.2112/SI53-001.1.
   Web of Science ®Google Scholar
• Brooks, S., and T. Spencer, 2019. “Long-term Trends, Short-term Shocks and Cliff Responses for Areas of Critical Coastal Infrastructure.” In Coastal Sediments 2019: Proceedings of the 9th International Conference, (pp. 1179–1187). Tampa, Florida, USA.
   Google Scholar
• Brooks, S. M., and T. Spencer. 2010. “Temporal and Spatial Variations in Recession Rates and Sediment Release from Soft Rock Cliffs, Suffolk Coast, UK.” Geomorphology 124 (1–2): 26–41. doi:https://doi.org/10.1016/j.geomorph.2010.08.005.
   Web of Science ®Google Scholar
• Burningham, H., and J. French. 2017. “Understanding Coastal Change Using Shoreline Trend Analysis Supported by Cluster-based Segmentation.” Geomorphology 282: 131–149. doi:https://doi.org/10.1016/j.geomorph.2016.12.029.
   Web of Science ®Google Scholar
• Cefas, 2020. “WaveNet Interactive Map.” [online] Available at: http://wavenet.cefas.co.uk/Map [ Accessed 21 May 2020].
   Google Scholar
• Choung, Y. J., and M. H. Jo. 2017. “Comparison between a Machine-learning-based Method and a Water-index-based Method for Shoreline Mapping Using a High-resolution Satellite Image Acquired in Hwado Island, South Korea.” Journal of Sensors 2017: 1–13. doi:https://doi.org/10.1155/2017/8245204.
   Web of Science ®Google Scholar
• Committee on Climate Change, 2018. “Managing the Coast in a Changing Climate.” Available online: https://www.theccc.org.uk/publication/managing-the-coast-in-a-changing-climate/(accessed 14 March 2020
   Google Scholar
• De Andrés, M., J. M. Barragán, and M. Scherer. 2018. “Urban Centres and Coastal Zone Definition: Which Area Should We Manage?” Land Use Policy 71: 121–128. doi:https://doi.org/10.1016/j.landusepol.2017.11.038.
   Web of Science ®Google Scholar
• Demir, N., S. Oy, F. Erdem, D. Z. Şeker, and B. Bayram. 2017. “Integrated Shoreline Extraction Approach with Use of Rasat MS and SENTINEL-1A SAR Images.” ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 4: 445–449. doi:https://doi.org/10.5194/isprs-annals-IV-2-W4-445-2017.
   Google Scholar
• Elnabwy, M. T., E. Elbeltagi, M. M. El Banna, M. M. Elshikh, I. Motawa, and M. R. Kaloop. 2020. “An Approach Based on Landsat Images for Shoreline Monitoring to Support Integrated Coastal Management—A Case Study, Ezbet Elborg, Nile Delta, Egypt.” ISPRS International Journal of Geo-Information 9 (4): 199–218. doi:https://doi.org/10.3390/ijgi9040199.
   Web of Science ®Google Scholar
• Environment Agency, 2020. “Vertical Aerial Photography.” Available online: https://data.gov.uk/dataset, (accessed 19 June 2020
   Google Scholar
• Ferreira, O., T. Garcia, A. Matias, R. Taborda, and J. A. Dias. 2006. “An Integrated Method for the Determination of Set-back Lines for Coastal Erosion Hazards on Sandy Shores.” Continental Shelf Research 26 (9): 1030–1044. doi:https://doi.org/10.1016/j.csr.2005.12.016.
   Web of Science ®Google Scholar
• Floodlist., 2017. “UK – Storm Surge Causes Flooding Along England’s East Coast.” Available online: www.http://floodlist.com/europe/united-kingdom (accessed 17 June 2020
   Google Scholar
• Foody, G. M., A. M. Muslim, and P. M. Atkinson. 2005. “Super‐resolution Mapping of the Waterline from Remotely Sensed Data.” International Journal of Remote Sensing 26 (24): 5381–5392. doi:https://doi.org/10.1080/01431160500213292.
   Web of Science ®Google Scholar
• Genovese, G., C. Vignolles, T. Nègre, and G. Passera. 2001. “A Methodology for A Combined Use of Normalised Difference Vegetation Index and CORINE Land Cover Data for Crop Yield Monitoring and Forecasting. A Case Study on Spain.” Agronomie 21 (1): 91–111. doi:https://doi.org/10.1051/agro:2001111.
   Google Scholar
• Gorelick, N., M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore. 2017. “Google Earth Engine: Planetary-scale Geospatial Analysis for Everyone.” Remote Sensing of Environment 202: 18–27. doi:https://doi.org/10.1016/j.rse.2017.06.031.
   Web of Science ®Google Scholar
• Hagenaars, G., S. De Vries, A. P. Luijendijk, W. P. De Boer, and A. J. Reniers. 2018. “On the Accuracy of Automated Shoreline Detection Derived from Satellite Imagery: A Case Study of the Sand Motor Mega-scale Nourishment.” Coastal Engineering 133: 113–125. doi:https://doi.org/10.1016/j.coastaleng.2017.12.011.
   Web of Science ®Google Scholar
• Kokkinos, I., 2015. “Pushing the Boundaries of Boundary Detection Using Deep Learning. arXiv Preprint.” arXiv:1511.07386.
   Google Scholar
• Lab, S. V., 2016. “ImageNet. [Online].” Available at http://www.image-net.org/ [Accessed 18 November 2019
   Google Scholar
• Li, W., and P. Gong. 2016. “Continuous Monitoring of Coastline Dynamics in Western Florida with a 30-year Time Series of Landsat Imagery.” Remote Sensing of Environment 179: 196–209. doi:https://doi.org/10.1016/j.rse.2016.03.031.
   Web of Science ®Google Scholar
• Liu, C. (2018). “HED: Keras Implementation.” Github repository https://github.com/lc82111
   Google Scholar
• Liu, H., J. Luo, Y. Sun, L. Xia, W. Wu, H. Yang, X. Hu, and L. Gao, 2019. “Contour-oriented Cropland Extraction from High Resolution Remote Sensing Imagery Using Richer Convolution Features Network.” In 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics). IEEE : 1–6. Istanbul, Turkey.
   Google Scholar
• Liu, X. Y., R. S. Jia, Q. M. Liu, C. Y. Zhao, and H. M. Sun. 2019. “Coastline Extraction Method Based on Convolutional Neural Networks—A Case Study of Jiaozhou Bay in Qingdao, China.” IEEE Access 7: 180281–180291. doi:https://doi.org/10.1109/ACCESS.2019.2959662.
   Web of Science ®Google Scholar
• Lu, T., D. Ming, X. Lin, Z. Hong, X. Bai, and J. Fang. 2018. “Detecting Building Edges from High Spatial Resolution Remote Sensing Imagery Using Richer Convolution Features Network.” Remote Sensing 10 (9): 1496–1515.
   Web of Science ®Google Scholar
• Luijendijk, A., G. Hagenaars, R. Ranasinghe, F. Baart, G. Donchyts, and S. Aarninkhof. 2018. “The State of the World’s Beaches.” Scientific Reports 8: 6641.
   PubMed Web of Science ®Google Scholar
• Marmanis, D., K. Schindler, J. D. Wegner, S. Galliani, M. Datcu, and U. Stilla. 2018. “Classification with an Edge: Improving Semantic Image Segmentation with Boundary Detection.” ISPRS Journal of Photogrammetry and Remote Sensing 135: 158–172. doi:https://doi.org/10.1016/j.isprsjprs.2017.11.009.
   Web of Science ®Google Scholar
• McFeeters, S. K. 1996. “The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features.” International Journal of Remote Sensing 17 (7): 1425–1432. doi:https://doi.org/10.1080/01431169608948714.
   Web of Science ®Google Scholar
• Mentaschi, L., M. I. Vousdoukas, J.-F. Pekel, E. Voukouvalas, and L. Feyen. 2018. “Global Long-term Observations of Coastal Erosion and Accretion.” Scientific Reports 8 (1): 12876. doi:https://doi.org/10.1038/s41598-018-30904-w.
   PubMed Web of Science ®Google Scholar
• Mohajerani, Y., M. Wood, I. Velicogna, and E. Rignot. 2019. “Detection of Glacier Calving Margins with Convolutional Neural Networks: A Case Study.” Remote Sensing 11 (1): 74–87. doi:https://doi.org/10.3390/rs11010074.
   Web of Science ®Google Scholar
• Moore, L. J., P. Ruggiero, and J. H. List. 2006. “Comparing Mean High Water and High Water Line Shorelines: Should Proxy-datum Offsets Be Incorporated into Shoreline Change Analysis?” Journal of Coastal Research 224: 894–905. doi:https://doi.org/10.2112/04-0401.1.
   Web of Science ®Google Scholar
• Pardo-Pascual, J. E., E. Sánchez-García, J. Almonacid-Caballer, J. M. Palomar-Vázquez, E. Priego De Los Santos, A. Fernández-Sarría, and Á. Balaguer-Beser. 2018. “Assessing the Accuracy of Automatically Extracted Shorelines on Microtidal Beaches from Landsat 7, Landsat 8 and Sentinel-2 Imagery.” Remote Sensing 10 (2): 326–346. doi:https://doi.org/10.3390/rs10020326.
   Web of Science ®Google Scholar
• Pardo-Pascual, J. E., J. Almonacid-Caballer, L. A. Ruiz, and J. Palomar-Vázquez. 2012. “Automatic Extraction of Shorelines from Landsat TM and ETM+ Multi-temporal Images with Subpixel Precision.” Remote Sensing of Environment 123: 1–11. doi:https://doi.org/10.1016/j.rse.2012.02.024.
   Web of Science ®Google Scholar
• Pekel, J. F., A. Cottam, N. Gorelick, and A. S. Belward. 2016. “High-resolution Mapping of Global Surface Water and Its Long-term Changes.” Nature 540 (7633): 418–422. doi:https://doi.org/10.1038/nature20584.
   PubMed Web of Science ®Google Scholar
• Planet Team, P. 2017. Planet Application Program Interface: In Space for Life on Earth. San Francisco: CA. https://api.planet.com
   Google Scholar
• Pollard, J. A., S. M. Brooks, and T. Spencer. 2019a. “Harmonising Topographic & Remotely Sensed Datasets, a Reference Dataset for Shoreline and Beach Change Analysis.” Nature Scientific Data 6 (1): 1–42.
   PubMed Web of Science ®Google Scholar
• Pollard, J. A., T. Spencer, and S. M. Brooks. 2019b. “The Interactive Relationship between Coastal Erosion and Flood Risk.” Progress in Physical Geography: Earth and Environment 43 (4): 574–585. doi:https://doi.org/10.1177/0309133318794498.
   Google Scholar
• Pugh, D., and P. Woodworth. 2014. Sea-level Science: Understanding Tides, Surges, Tsunamis and Mean Sea-level Changes. Cambridge, UK: Cambridge University Press.
   Google Scholar
• Pye, K., and S. J. Blott. 2006. “Coastal Processes and Morphological Change in the Dunwich-Sizewell Area, Suffolk, UK.” Journal of Coastal Research 22 (3): 453–473. doi:https://doi.org/10.2112/05-0603.1.
   Web of Science ®Google Scholar
• Rahman, A. F., D. Dragoni, and B. El-Masri. 2011. “Response of the Sundarbans Coastline to Sea Level Rise and Decreased Sediment Flow: A Remote Sensing Assessment.” Remote Sensing of Environment 115 (12): 3121–3128. doi:https://doi.org/10.1016/j.rse.2011.06.019.
   Web of Science ®Google Scholar
• Ren, X. 2008. “Multi-scale Improves Boundary Detection in Natural Images”, In Forsyth, D., P. Torr, and Zisserman, A. (Eds.), European Conference on Computer Vision, 533–545. Springer: Heidelberg.
   Google Scholar
• Roy, S., 2017. “Planet to Google Earth Engine Pipeline (Command Line Interface).”
   Google Scholar
• Simonyan, K., and A. Zisserman, 2014. “Very Deep Convolutional Networks for Large-scale Image Recognition.” arXiv preprint arXiv:1409.1556.
   Google Scholar
• Spencer, T., S. M. Brooks, B. R. Evans, J. A. Tempest, and I. Möller. 2015. “Southern North Sea Storm Surge Event of 5 December 2013: Water Levels, Waves and Coastal Impacts.” Earth-Science Reviews 146: 120–145. doi:https://doi.org/10.1016/j.earscirev.2015.04.002.
   Web of Science ®Google Scholar
• Thieler, E. R., E. A. Himmelstoss, J. L. Zichichi, and A. Ergul, 2009. “Digital Shoreline Analysis System (DSAS) Version 4.0 – An ArcGIS Extension for Calculating Shoreline Change.” US Geological Survey Open-file Report, 2008–1278. US Geological Survey, Reston, VA.
   Google Scholar
• Toure, S., O. Diop, K. Kpalma, and A. S. Maiga. 2019. “Shoreline Detection Using Optical Remote Sensing: A Review.” ISPRS International Journal of Geo-Information 8 (2): 75. doi:https://doi.org/10.3390/ijgi8020075.
   Web of Science ®Google Scholar
• USGS, 2018. “Digital Shoreline Analysis System (DSAS).Version 5.0 User Guide.” Available online: https://www.usgs.gov/centers/whcmsc/science/digital-shoreline-analysis-system-dsas?qt-science_center_objects=0#qt-science_center_objects (accessed 15 April 2020
   Google Scholar
• Vos, K., M. D. Harley, K. D. Splinter, J. A. Simmons, and I. L. Turner. 2019. “Sub-annual to Multi-decadal Shoreline Variability from Publicly Available Satellite Imagery.” Coastal Engineering 150: 160–174. doi:https://doi.org/10.1016/j.coastaleng.2019.04.004.
   Web of Science ®Google Scholar
• Wadey, M. P., I. D. Haigh, R. J. Nicholls, J. M. Brown, K. Horsburgh, B. Carroll, S. L. Gallop, T. Mason, and E. Bradshaw. 2015. “A Comparison of the 31 January – 1 February 1953 and 5 – 6 December 2013 Coastal Flood Events around the UK.” Frontiers in Marine Science 2: 84–111. doi:https://doi.org/10.3389/fmars.2015.00084.
   Web of Science ®Google Scholar
• Xie, S., and Z. Tu, 2015. “Holistically-nested Edge Detection.” Proceedings of the IEEE International Conference on Computer Vision. 1395–1403. Santiago, Chile.
   Google Scholar
• Zarillo, G. A., J. Kelley, and V. Larson, 2008. “A GIS Based Tool for Extracting Shoreline Positions from Aerial Imagery (Beachtools) Revised (No. ERDC/CHL-CHETN-IV-73).” Engineer research and development center Vicksburg MS coastal and hydraulics lab.
   Google Scholar
• Zhang, H., Q. Jiang, and J. Xu. 2013. “Coastline Extraction Using Support Vector Machine from Remote Sensing Image.” Journal of Multimedia 8 (2): 175–182.
   Google Scholar
• Zhang, L., L. Zhang, and B. Du, 2016. “Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art.” IEEE Geoscience and Remote Sensing Magazine 4, 22–40.
   Google Scholar