RapidBenthos

Link to 12 3D photogrammetric models and underlying images to replicate the method presented in the publication titled “RapidBenthos – Automated segmentation and multi-view classification of coral reef communities from photogrammetric reconstruction”. These sites were selected to assess the performance of RapidBenthos in different environmental conditions and reef habitats, as they ranged from high visibility offshore reefs to turbid inshore reefs, spanned depths from 5 to 15 meters, and included a range of intra-reefal environments (i.e., reef front, flank, back, and lagoon).

All sites were imaged using a standardized diver-rig photogrammetry workflow described by Gordon et al. (2023). High-resolution benthic images (5686 x 3217 pixels) were captured using two Nikon D850 DSLR cameras with 20 mm Nikkor prime lens shooting at 0.5 second intervals (full camera settings described in (Gordon et al., 2023). Cameras were housed in Nauticam underwater housings with 8-inch dome ports and were mounted on an aluminium rig at a distance of 57 cm between lenses (60 % overlap between adjacent images, Figure 1c). Each site was imaged by a single diver on SCUBA over a period of 10-15 minutes to capture approximately 3,000 photos. Nadiral and oblique imagery was captured at an altitude of approximately 1.5 m using a “lawn-mowing” swim pattern consisting of 5 longitudinal passes and an additional 4-8 perpendicular passes. The swim pattern and speed used ensured a minimum overlap of 80 and 60 % between temporally and spatially adjacent photos, respectively (Figure 1d). Six GPCs were distributed across the depth gradient of the site prior imaging to scale resultant models in X, Y, and Z axes (details provided in(Gordon et al., 2023). Depth was also recorded for each GPC to incorporate bathymetric information into 3D model building.

The RapidBenthos workflow was applyed to each plots, segmenting and calssifying benthic constituents on the orthomosaics. This method resulted in extracting community compostion and colony-level metrics (i.e., colony planar-area and colony frequency). The significance of this research lies in devlopping a workflow that automatically extract community composition information from close-range photogrammetry in any coral reefs environment. We eveluated that our method was 195 time faster than manual segmentation and classification allowing to sustainably scale 3D photogrammetry mointoring, both in replication and size of reefs surveyed compared to manual data extraction.

Due to the large data files, the data can be accessed on request.