Résumé
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.