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Abstract Given the inevitability of sea-level rise, investigating processes of human-altered coastlines at the intermediate timescales of years to decades can sometimes feel like an exercise in futility. Returning to the big picture and long view of feedbacks, emergent dynamics, and wider context, here we offer 10 existential questions for research into human–coastal coupled systems. 

Abstract Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data‐driven models are only as good as the data used for training, and this points to the importance of high‐quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time‐consuming, manual process. Here, we investigate the process of labeling data, with a specific focus on coastal aerial imagery captured in the wake of hurricanes that affected the Atlantic and Gulf Coasts of the United States. The imagery data set is a rich observational record of storm impacts and coastal change, but the imagery requires labeling to render that information accessible. We created an online interface that served labelers a stream of images and a fixed set of questions. A total of 1,600 images were labeled by at least two or as many as seven coastal scientists. We used the resulting data set to investigate interrater agreement: the extent to which labelers labeled each image similarly. Interrater agreement scores, assessed with percent agreement and Krippendorff's alpha, are higher when the questions posed to labelers are relatively simple, when the labelers are provided with a user manual, and when images are smaller. Experiments in interrater agreement point toward the benefit of multiple labelers for understanding the uncertainty in labeling data for machine learning research. 

Abstract The world’s coastlines are spatially highly variable, coupled-human-natural systems that comprise a nested hierarchy of component landforms, ecosystems, and human interventions, each interacting over a range of space and time scales. Understanding and predicting coastline dynamics necessitates frequent observation from imaging sensors on remote sensing platforms. Machine Learning models that carry out supervised (i.e., human-guided) pixel-based classification, or image segmentation, have transformative applications in spatio-temporal mapping of dynamic environments, including transient coastal landforms, sediments, habitats, waterbodies, and water flows. However, these models require large and well-documented training and testing datasets consisting of labeled imagery. We describe “Coast Train,” a multi-labeler dataset of orthomosaic and satellite images of coastal environments and corresponding labels. These data include imagery that are diverse in space and time, and contain 1.2 billion labeled pixels, representing over 3.6 million hectares. We use a human-in-the-loop tool especially designed for rapid and reproducible Earth surface image segmentation. Our approach permits image labeling by multiple labelers, in turn enabling quantification of pixel-level agreement over individual and collections of images. 

Overwash is the cross‐shore transport of water and sediment from a waterbody over the crest of a sand or gravel barrier beach, and washover is the resulting sedimentary deposit. Washover volume, and alongshore patterns of washover distribution, are fundamental components of sediment budgets for low‐lying coastal barrier systems. Accurate sediment budgets are essential to forecasting barrier system sustainability under future climate‐driven forcing. However, comprehensive surveys of three‐dimensional washover morphology are challenging to deliver. Here, we use the results of a physical experiment, analysis of lidar data, and examples of washover characteristics reported in the literature to develop scaling relationships for washover morphometry that demonstrate volume can be reasonably inferred from planform measurements, for washover in natural (non‐built) and built barrier settings. Gaining three‐dimensional insight into washover deposits from two‐dimensional information unlocks the ability to analyze past aerial imagery and estimate contributions from washover flux to sediment budgets for past storms. 

The growing push for open data resulted in an abundance of data for coastal researchers, which can lead to problems for individual researchers related to data discoverability. One solution is to explicitly develop services for coastal researchers to help curate data for discovery, hosting discussions around reuse, community building, and finding collaborators. To develop the idea of a coastal data curation service, we investigate aspects of the UNESCO International Coastal Atlas Network member sites that could be used to build a curation service. We develop a minimal example of a coastal data curation service, deploy this as a website, and describe the next steps to move beyond the prototype phase. We envision a coastal data curation service as a way to cultivate a community focused on coastal data discovery and reuse. 

We present an active learning pipeline to identify hurricane impacts on coastal landscapes. Previously unlabeled post-storm images are used in a three component workflow — first an online interface is used to crowd-source labels for imagery; second, a convolutional neural network is trained using the labeled images; third, model predictions are displayed on an interactive map. Both the labeler and interactive map allow coastal scientists to provide additional labels that will be used to develop a large labeled dataset, a refined model, and improved hurricane impact assessments.