Coastal Aquaculture Pond Recognition via Scale and Boundary Dynamic Awareness Network in Remote Sensing Imagery

Coastal aquaculture ponds, vital to marine aquaculture, are widely distributed along coastlines and hold significant economic and ecological value. Accurately mapping their distribution and types is essential for optimizing marine spatial resources and protecting coastal ecosystems. While deep learning has improved detection from remote sensing imagery, precise recognition still faces challenges such as large scale and shape variations, blurred boundaries in dense areas, and a lack of high-resolution fine-grained datasets. To this end, a Scale and Boundary Dynamic Awareness Network (BDA-Net) is proposed in this paper. Specifically, A Taylor-optimized Scale-adaptive Feature Interaction (TSFI) structure is designed to achieve efficient multi-scale feature adaptation for diverse pond sizes and shapes, overcoming fixed-scale Transformer limitations while preserving accuracy with lower computation via Taylor approximation. Moreover, a Frequency-guided Adaptive Boundary Convolution (FABC) is developed to resolve boundary confusion and detail loss by adaptively fusing multi-directional frequency features, enhancing contour-aligned sampling for improved fine-grained discrimination and recognition performance. Additionally, a high-resolution fine-grained dataset is constructed to fill the gap in low spatial resolution and coarse category subdivision of existing aquaculture pond datasets. Extensive experiments on this dataset and public benchmarks demonstrate that the proposed BDA-Net achieves an mIoU of 85.71%, outperforming the existing methods by over 5.45%.