{"refrec":{"BRefID":318199,"RR":"<b>Vos, K.; Splinter, K.D.; Harley, M.D.; Simmons, J.A.; Turner, I.L.</b> (2019). <i>CoastSat</i>: a Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery. <i>Environ. Model. Softw. 122</i>: 104528. <a href=\"https://dx.doi.org/10.1016/j.envsoft.2019.104528\" target=\"_blank\">https://dx.doi.org/10.1016/j.envsoft.2019.104528</a>","BEntID":311636,"PublicFlag":1,"CheckedFlag":0,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Environ. Model. Softw. 122</i>: 104528. <a href=\"https://dx.doi.org/10.1016/j.envsoft.2019.104528\" target=\"_blank\">https://dx.doi.org/10.1016/j.envsoft.2019.104528</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Vos, K.; Splinter, K.D.; Harley, M.D.; Simmons, J.A.; Turner, I.L.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Vos, K. <i>et al.</i>","Englishabstract":"<i>CoastSat</i> is an open-source software toolkit written in Python that enables the user to obtain time-series of shoreline position at any sandy coastline worldwide from 30+ years (and growing) of publicly available satellite imagery. The toolkit exploits the capabilities of Google Earth Engine to efficiently retrieve Landsat and Sentinel-2 images cropped to any user-defined region of interest. The resulting images are pre-processed to remove cloudy pixels and enhance spatial resolution, before applying a robust and generic shoreline detection algorithm. This novel shoreline detection technique combines a supervised image classification and a sub-pixel resolution border segmentation to map the position of the shoreline with an accuracy of ~10 m. The purpose of <i>CoastSat</i> is to provide coastal managers, engineers and scientists a user-friendly and practical toolkit to monitor and explore their coastlines. 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