BeachLISA: Web-based system for semantic segmentation and analysis of beach-litter images

Mitigating marine plastic pollution requires continuous quantitative characterization of litter transport and accumulation processes to inform monitoring and cleanup plans. Deep learning-based image analysis methods have been recently developed to automatically detect beach litter in drone and fixed-camera imagery. However, these methods require image-analysis expertise and a dedicated computing environment, thus making them difficult for non-specialists. In this study, we develop Beach-Litter Image Segmentation Analysis (BeachLISA), a system that enables image-based beach-litter analysis in a web browser, and evaluate its performance and practical applicability. Users upload images and specify analysis settings to obtain classification results and quantitative information on beach litter. For high-resolution images, we implement slicing-aided hyper inference (SAHI), which performs inference on overlapping image slices, and compare it with tiling without overlap and with downsampling, in which an entire image's size is reduced. Evaluation using drone imagery with a ground sampling distance of 1 cm shows that tiling and SAHI achieved higher F1-scores than downsampling and improved the detection of small litter-classified regions. CPU-based parallel processing enabled 150 images to be processed concurrently in approximately 6 min. Moreover, analysis of 2987 images obtained at Rokudoji Beach shows that decreases in the number of pixels classified as beach litter were temporally associated with recorded cleanup events. Because BeachLISA requires neither a specialized computing environment nor image-analysis expertise, it offers a practical system for analyzing image-derived beach-litter metrics in drone and fixed-camera imagery.