Collecting and curating data plays a crucial role in environmental surveying. In order to gather meaningful samples, it is often necessary to develop a real-time data curation system that processes data on-the-fly and enriches it with validated models from domain experts. One area where this is particularly important is underwater marine surveys, where the vastness of the sea requires human interaction in the curation process to focus on relevant areas for exploration. Additionally, ongoing surveys are susceptible to poor performance due to data drift, which hinders the ability to provide valuable feedback for guiding data collection. While recent advancements have shown promise in addressing these challenges, they often overlook the practical constraints associated with remote data collection, such as limited processing power and latency. To overcome these limitations, this paper proposes a real-time system that adapts to data drift and enables the recording of uncertain samples for further processing and analysis on shore. The results of our approach demonstrate a remarkable improvement in species recognition, achieving an almost 18% improvement compared to the best baseline method in unseen areas. Importantly, this improvement is achieved while meeting the real-time requirements of surveys and consuming only 15W of power. By effectively addressing the challenges of underwater data drift, our proposed approach provides an efficient and effective solution for environmental surveys.

Machine learning (ML) algorithms have shown great potential in edge-computing environments, however, the literature mainly focuses on model inference only. We investigate how ML can be operationalized and how in-situ curation can improve the quality of edge applications, in the context of ML-assisted environmental surveys. We show that camera-enabled ML systems deployed on edge devices can enable scientists to perform real-time monitoring of species of interest or characterization of natural habitats. However, the benefit of this new technology is only as good as the quality and accuracy of the edge ML model inferences. In this demonstration, we show that with small additional time investment, domain scientists can manually curate ML model outputs and thus obtain highly reliable scientific insights, leading to more effective and scalable environmental surveys.

Crown-of-Thorn Starfish (COTS) outbreaks are a major cause of coral loss on the Great Barrier Reef (GBR) and substantial surveillance and control programs are ongoing to manage COTS populations to ecologically sustainable levels. In this paper, we present a comprehensive real-time machine learning-based underwater data collection and curation system on edge devices for COTS monitoring. In particular, we leverage the power of deep learning-based object detection techniques, and propose a resource-efficient COTS detector that performs detection inferences on the edge device to assist marine experts with COTS identification during the data collection phase. The preliminary results show that several strategies for improving computational efficiency (e.g., batch-wise processing, frame skipping, model input size) can be combined to run the proposed detection model on edge hardware with low resource consumption and low information loss.