Automatic Extraction of Oxbow Lakes from Landsat Imagery Using Deep Learning Across the Amazon Basin

Oxbow lakes are key geomorphic and ecological components of river floodplains, but basin-scale inventories remain limited because these features are narrow, fragmented, and spectrally similar to active channels and other floodplain water bodies. This study developed a deep-learning-based workflow for oxbow lake extraction in the Amazon Basin using Landsat imagery. A reference label dataset was constructed by combining automated cutoff extraction, surface-water transition information, and manual interpretation. An enhanced DeepLabV3+ semantic segmentation model was trained with nine-band Landsat image patches and applied to basin-wide Landsat 8/9 composite images through sliding-window inference and post-processing. Independent validation against manually digitized PlanetScope imagery showed strong detection performance, with an overall F1-score of 0.819 and recall of 0.966. A spatially independent sub-basin validation further indicated partial transferability in an unseen Amazon sub-basin, with an F1-score/Dice coefficient of 0.532, precision of 0.498, recall of 0.571, and IoU of 0.362. Basin-scale inference detected 1836 oxbow lake objects, approximately 2.34 times the number of reference labels. Our study provides a new image-based semantic segmentation approach for basin-scale oxbow lake mapping and offers an expanded spatial inventory to support floodplain geomorphology, river dynamics, and river–floodplain ecosystem studies.