Full description
A subset of photoquadrats were uploaded to the CoralNet machine learning interface (https://coralnet.ucsd.edu/) and manually labelled for coral, algae or substrate type using 50 points per quadrat. Follow training of the machine, this enabled automatic annotation of all unclassified field images: the remaining field photos were uploaded to the database and 50 annotation points were overlaid on each of the images. Every point was assigned a benthic cover category from a label list automatically by the program. The resulting benthic cover data of each photo was linked to gps coordinates, saved as an ArcMap point shapefile, and projected to Universal Transverse Mercator WGS84 Zone 55 South.Issued: 2018
Data time period: 2017 to 31 05 2017
Subjects
Biological Sciences |
Earth Sciences |
Ecology |
Environmental Science and Management |
Environmental Sciences |
Environmental Management |
Environmental Monitoring |
Environmental Science and Management Not Elsewhere Classified |
Marine and Estuarine Ecology (Incl. Marine Ichthyology) |
Physical Geography and Environmental Geoscience |
Physical Geography and Environmental Geoscience Not Elsewhere Classified |
eng |
User Contributed Tags
Login to tag this record with meaningful keywords to make it easier to discover
