Segmentation, or the classification of pixels (grid cells) in imagery, is ubiquitously applied in the natural sciences. Manual methods are often prohibitively time‐consuming, especially those images consisting of small objects and/or significant spatial heterogeneity of colors or textures. Labeling complicated regions of transition that in Earth surface imagery are represented by collections of mixed‐pixels, ‐textures, and ‐spectral signatures, can be especially error‐prone because it is difficult to reliably unmix, identify and delineate consistently. However, the success of supervised machine learning (ML) approaches is entirely dependent on good label data. We describe a fast, semi‐automated, method for interactive segmentation of N‐dimensional (x, y, N) images into two‐dimensional (x, y) label images. It uses human‐in‐the‐loop ML to achieve consensus between the labeler and a model in an iterative workflow. The technique is reproducible; the sequence of decisions made by human labeler and ML algorithms can be encoded to file, so the entire process can be played back and new outputs generated with alternative decisions and/or algorithms. We illustrate the scientific potential of segmentation of imagery of diverse settings and image types using six case studies from river, estuarine, and open coast environments. These photographic and non‐photographic imagery consist of 1‐ and 3‐bands on regular and irregular grids ranging from centimeters to tens of meters. We demonstrate high levels of agreement in label images generated by several labelers on the same imagery, and make suggestions to achieve consensus and measure uncertainty, ideal for widespread application in training supervised ML for image segmentation.

Shoreline measurement techniques using satellite-derived imagery can provide decades of observations of shoreline change. Here we apply these techniques to the western south shore of Long Island, New York, which has three distinct beaches, Rockaway Peninsula, Long Beach, and Jones Beach Island, which are 18, 15, and 24 km in length, respectively. These beaches are recreation areas for millions of regional residents and include several groin fields, sediment dredging and nourishment operations, and a coastal wave climate that includes winter northeasterly storms and summer hurricanes. The shorelines along the western ends of these three beaches have been accreting at ~4 m/yr during the observation record (1984–2022) resulting from net westward longshore drift. The central 10–12 km of the beaches have lower shoreline change rates, and these rates are generally lowest within the groin fields (0.5–1.5 m/yr). Shoreline change observations also provide evidence for westward propagating accretion and erosion sediment waves that have durations of several years. Beach nourishment projects are shown to significantly influence rates of shoreline accretion, and this is commonly followed by significant shoreline retreat during the subsequent years.

Satellite-derived shorelines (SDS) have the potential to help researchers answer critical coastal science questions and support work to predict coastal change by filling in the spatial and temporal gaps present in current field-based and remote-sensing data collection methods. The U.S. Geological Survey conducted comparison analyses of traditionally sourced shorelines and SDS in diverse coastal landscapes to determine how SDS could be used in ongoing and future work across varied coastal environments and provided some initial findings that could be used for implementation. Using CoastSeg, a browser-based program for SDS detection and mapping, SDS for the period 1984–2023 for multiple locations across the United States were compared to shoreline positions from traditionally sourced shoreline data. In this report, the authors present these comparisons alongside lessons learned and challenges encountered when building SDS workflows in different coastal locations. Results show that individual SDS have larger uncertainty and yet produced similar linear trends to sparser, traditionally sourced shoreline data; because SDS methods provide orders of magnitude more data than traditional shoreline-detection methods, they can be used to evaluate shoreline behaviors. Refining average scalar slopes used in tidal corrections did not result in substantial decreases in uncertainty. Using lessons from this work to outline needs for regional implementation, initial setup time would be considerable, being on the order of weeks. However, once complete, shoreline detections and analyses are fast (on the order of minutes to hours) and achievable using a desktop computer.

Satellite imagery has helped to provide decades of shoreline change data to coastal regions all over the world. Although shorelines around the globe have been armored with different types of coastal structures to try and protect beaches and properties from erosion, there are few studies of shoreline dynamics within structures such as groin fields using satellite images. Here we apply satellite-derived shoreline techniques to three areas of California: Ventura, Santa Monica, and Newport Beach, each site containing groin fields of varying number and length. Shoreline trends during 1984–2022 reveal that the presence of these coastal structures may alter the overall shoreline variability at these locations. Peclet numbers have been calculated for each site to better characterize the variability of longshore transport within and across the study areas and to compare with shoreline change characteristics. The satellite-derived shoreline data reveal offsets or “steps” in the shoreline position across the groins with sediment pilling up on one side more than the other due to longshore transport. The magnitudes and dynamics of these shoreline offsets were measured from satellite techniques, and we find that the average offsets are commonly related to groin length. Some shoreline offsets reveal seasonal patterns, with Newport Beach having the largest seasonal patterns of the three sites. We find that these seasonal patterns in shoreline offsets at the Newport Beach groins are related to the seasonal variability in wave direction and littoral transport. We conclude that satellite-derived shoreline techniques with Landsat and Sentinel-2 imagery are adequate to characterize shoreline behaviors within the complex coastal settings of groin fields.