Deep learning for multi-label classification of coral conditions in the Indo-Pacific via underwater photogrammetry

arXiv:2403.05930v1 Announce Type: new
Abstract: Since coral reef ecosystems face threats from human activities and climate change, coral conservation programs are implemented worldwide. Monitoring coral health provides references for guiding conservation activities. However, current labor-intensive methods result in a backlog of unsorted images, highlighting the need for automated classification. Few studies have simultaneously utilized accurate annotations along with updated algorithms and datasets. This study aimed to create a dataset representing common coral conditions and associated stressors in the Indo-Pacific. Concurrently, …

abstract arxiv change classification climate climate change conservation coral cs.cv current deep learning ecosystems face health highlighting however human images indo-pacific labor monitoring pacific photogrammetry threats type underwater via