Projet CNDO - Sessions Underwater images collected by an Underwater Vision Census - 20210308_MDG-Toliara_UVC-01_01 - 20210308_MDG-Toliara_UVC-01_02 - 20210309_MDG-Toliara_UVC-01_01
Aina Le Don NOMENISOA;Yves Amoros MITONDRASOA;Gildas TODINANAHARY;Hubert Zafimampiravo EDWIN;Israel John Bunyan;Toky RAZAKARISOA;Tsiresimiary MANDIBILAZA;Michel RATSIZAFY;Saverio Raseta;Henitsoa Jaonalison;Jamal Mahafina;Igor Eeckhaut
Description
Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on the images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps..
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Metrics Over Time
Publication Details
Subfield
Ecology
Field
Environmental Science
Domain
Physical Sciences
Confidence Score
55%
Source
Scholar Data Model
Keywords
Artificial IntelligenceComputer VisionCoral ReefCoral Reef HabitatDeep LearningEcologyFOS: Biological sciencesGeoAIGlobal Coral Reef Monitoring NetworkHabitat MappingIndian OceanMachine LearningMadagascarMappingReef EcosystemRemote SensingUVCUnderwater Vision Census