Drone-based remote sensing combined with AI-driven methodologies has shown great potential for accurate mapping and monitoring of coral reef ecosystems. This study presents a novel multi-scale approach to coral reef monitoring, integrating fine-scale underwater imagery with medium-scale aerial imagery. Underwater images are captured using an Autonomous Surface Vehicle (ASV), while aerial images are acquired with an aerial drone. A transformer-based deep-learning model is trained on underwater images to detect the presence of 31 classes covering various coral morphotypes, associated fauna, and habitats. These predictions serve as annotations for training a second model applied to aerial images. The transfer of information across scales is achieved through a weighted footprint method that accounts for partial overlaps between underwater image footprints and aerial image tiles. The results show that the multi-scale methodology successfully extends fine-scale classification to larger reef areas, achieving a high degree of accuracy in predicting coral morphotypes and associated habitats. The method showed a strong alignment between underwater-derived annotations and ground truth data, reflected by an AUC (Area Under the Curve) score of 0.9251. This shows that the integration of underwater and aerial imagery, supported by deep-learning models, can facilitate scalable and accurate reef assessments. This study demonstrates the potential of combining multi-scale imaging and AI to facilitate the monitoring and conservation of coral reefs. Our approach leverages the strengths of underwater and aerial imagery, ensuring the precision of fine-scale analysis while extending it to cover a broader reef area.

翻译：基于无人机的遥感技术与人工智能驱动方法相结合，在珊瑚礁生态系统的精确测绘与监测方面展现出巨大潜力。本研究提出了一种新型多尺度珊瑚礁监测方法，将精细尺度的水下影像与中尺度的航空影像相融合。水下影像通过自主水面航行器（ASV）采集，而航空影像则由无人机获取。首先基于Transformer深度学习模型在水下影像上训练，用于检测涵盖多种珊瑚形态类型、伴生动物群及栖息地的31个类别。这些预测结果作为标注数据，用于训练应用于航空影像的第二个模型。通过加权足迹方法实现跨尺度信息传递，该方法考虑了水下影像足迹与航空影像图块之间的部分重叠。结果表明，多尺度方法成功将精细尺度分类扩展至更大范围的珊瑚礁区域，在预测珊瑚形态类型及相关栖息地方面实现了高度准确性。该方法显示水下衍生标注与地面真实数据高度吻合，AUC（曲线下面积）得分达0.9251。这表明在深度学习模型支持下，水下与航空影像的融合能够促进可扩展且精确的珊瑚礁评估。本研究论证了结合多尺度成像与人工智能技术在促进珊瑚礁监测与保护方面的潜力。我们的方法充分发挥水下与航空影像的各自优势，在确保精细尺度分析精度的同时，将其扩展到更广阔的珊瑚礁区域。