Fine-Scale Spartina alterniflora Mapping Using Advanced Deep Learning and High-Resolution UAV Imagery

Accurate mapping of Spartina alterniflora (S. alterniflora) is critical for coastal conservation. While deep learning has improved mapping, current algorithms that rely on moderate-resolution satellite imagery struggle to detect minute, early-stage invasive patches amid complex backgrounds with similar textures (e.g., water ripples, native vegetation). To overcome this resolution bottleneck, we construct UAV-SaSeg, the first high-resolution UAV semantic segmentation dataset targeting these complicated scenarios. Furthermore, we propose DINOsegNext, a novel mapping model integrating the DINOv3 foundation model with a Global Filter (GF) multi-frequency convolution. This architecture leverages generalized knowledge and spatial-frequency feature decoupling to filter out confusing background noise effectively. Validated in real-world intertidal zones, DINOsegNext outperforms state-of-the-art models, achieving an IoU of 0.7420 and an F1-Score of 0.8519. Crucially, it maintains high computational efficiency, with an inference latency of merely 8.5 ms per image, making it well-suited for onboard edge computing. This work provides urgently needed high-resolution data and a robust technical path for the precise, efficient monitoring of invasive species.