Enhancing semantic segmentation of Ecuadorian shrimp ponds through fine-tuned vision transformers and U-Net architectures utilizing open-source remote sensing data - Daniel Jacome; Jianghao Wang; Yong Ge (International Journal of Digital Earth)
Aquaculture has emerged as an important pillar of global food production, and shrimp farming plays a critical role in fulfilling the growing demand for seafood. This is especially true in Ecuador, which is recognized as one of the world's largest exporters and producers of shrimp. However, conventional shrimp pond monitoring has limitations owing to the extensive scale and operational complexity. Traditional methods using low-resolution imagery and ground surveys are hampered by cloud cover, outdated maps, and insufficient temporal resolution, leading to inaccurate pond area estimations and hindering timely management. Our framework accurately segmented shrimp ponds from high-resolution satellite images. Using a fine-tuned Prithvi 100M model, we achieved a state-of-the-art mIoU of 0.970 and 0.993 accuracy, respectively. This significantly surpasses other models, such as ViT-base (mIoU = 0.878) and U-Net variants (mIoU = 0.949). The pre-training of the Prithvi 100M model allowed it to effectively capture intricate pond boundaries and subtle internal structures, resulting in highly accurate and detailed segmentation masks. Fine-tuning the encoder proved to be the most effective (mIoU = 0.991), whereas standard data augmentation negatively impacted the performance. This methodology offers a valuable tool for enhancing water resource management and promoting sustainable aquaculture practices in Ecuadorian coastal regions.