2021/11/29 by Jiajun Liu, Brano Kusy, Liu, Jiajun +42
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Environmental Science · #Artificial Intelligence (cs.AI) #Artificial intelligence #Biology #Cartography #Competition (biology) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Coral and Marine Ecosystems Studies #Coral reef #Ecology #Engineering #FOS: Computer and information sciences #Fishery #Geography #Ichthyology and Marine Biology #Identification and Quantification in Food #Machine Learning (cs.LG) #Reef #Scale (ratio) #Starfish #Systems engineering #Task (project management) #cs.AI #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2111.14311
arxiv created 2021/11/29 · openalex publication_date 2021/11/29 · arxiv updated 2021/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
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 underway in an attempt to manage COTS populations to ecologically sustainable levels. We release a large-scale, annotated underwater image dataset from a COTS outbreak area on the GBR, to encourage research on Machine Learning and AI-driven technologies to improve the detection, monitoring, and management of COTS populations at reef scale. The dataset is released and hosted in a Kaggle competition that challenges the international Machine Learning community with the task of COTS detection from these underwater images.