Large-Scale Foundation Model Enhanced Few-Shot Learning for Open-Pit Minefield Extraction

High-resolution remote sensing data enable the extraction of fine-detailed boundaries of open-pit minefields, which is crucial for various applications, such as ecological restoration, environment impact assessment, mining field disaster minoring, and so on. In the last decade, a variety of convolutional neural networks (CNNs) and vision transformers (ViT) approaches have been developed for extracting the boundary and coverage of open-pit minefields. However, these deep learning approaches are always computationally expensive in pretraining and fine-tuning the network parameters. In addition, the diverse land cover/land use of different open-pit minefields poses a big challenge in building a large-scale benchmark dataset. To conduct efficient open-pit minefield extraction with limited labeled data, this letter employs a large-scale foundation model called segment anything model (SAM) to develop the few-shot learning strategy for extracting open-pit minefield with slightly fine-tuning SAM and without fine-tuning SAM, respectively. The experiment demonstrates that the proposed SAM-enhanced few-shot learning outperforms pretraining the state-of-the-art semantic segmentation approaches in terms of extraction precision and time cost. We hope our work can provide a solution for complex open-pit minefield extraction with a small number of labeled datasets.