These data consist of 20-point score estimates randomly placed on individual high resolution downward facing benthic digital images taken at Barracouta shoal using the AIMS towed video system.

The AIMS towed video system comprises a towed camera platform sending a live camera feed to a vessel-based, realtime image classification system (see Heyward et al. 2011) and a downward-facing high resolution still camera and strobe system programmed to take sequential still images at fixed time intervals of 10 seconds. The towed platform was deployed over the stern of the vessel, maintained as near as possible within a metre of the seabed and towed at 1-2 knots (1.5 nominal). Transect lengths varied among the years of data collection.

The downward-looking still images were geo-referenced during post-processing then analysed using a point-intercept approach. Information on benthic biota at each shoal was extracted from images using a point intercept approach with the AIMS Reefmon software (Jonker et al., 2008). All images were analysed using the Reefmon database system, with five overlaid points classified per photo and data logged against transect, depth and position.

The data provided here are derived using a machine learning model trained using the original manual annotations. The artificial intelligence engine called BenthoBot was used to re-analyse all seabed images from all years 2010-2016, processing each image using exactly the same approach. BenthoBot is a computer algorithm developed to classify points on an image, based on the spectral properties extracted from each image. It has been developed specifically by the Australian Institute of Marine Science to providean efficient and consistent means of generating the point based broad scale benthic classification data. The benefits of using BenthoBot include standardisation of the number of points sampled per image across all years (20 points per image) and removal of inconsistency in point classification associated with numerous technicians scoring images that may cause spatial and temporal artifacts.

Secondary (textural) datasets correlated with seafloor properties were developed from multibeam bathymetry to provide information on environmental characteristics, and are also provided here extracted for each image location as covariates.