SeaBee Data Analysis Products

Summary

This report details the data analysis products developed within the SeaBee project, a national infrastructure for drone-based services for use in coastal and aquatic research, mapping and monitoring of habitats, animal communities, and anthropogenic impacts. We present an advanced, automated data analysis pipeline that leverages deep learning for two primary tasks: pixel-wise thematic mapping of coastal habitats and object detection for counting wildlife. The pipeline utilizes models such as U-Net and Faster R-CNN to process high-resolution drone imagery (RGB, MSI, and HSI) and incorporates a novel hierarchical classification structure for habitat mapping and a robust method for detecting out-of-distribution (OOD) samples. We demonstrate the pipeline's pixel-wise mapping effectiveness through extensive experiments at three diverse Norwegian coastal sites—Remøy, Vega, and Ølbergholmen—achieving high accuracy in mapping complex habitats like kelp forests and various substrate types. Furthermore, the object detection framework shows strong performance in the automated counting and classification of 11 seabird species and coastal seals, offering a significant improvement in efficiency over traditional survey methods. The results confirm that the SeaBee pipeline is a powerful, scalable tool for environmental research and management, though we also discuss challenges such as data imbalance and model generalizability that will inform future work. This research is funded by the Research Council of Norway, project ID #296478, to the Norwegian Infrastructure for drone-based research, mapping, and monitoring in the coastal zone (SeaBee).