anonymous 16352 Dense and taxonomically detailed habitat maps of coral reef benthos machine-generated from underwater hyperspectral transects in Curaçao Schürholz, Daniel; Chennu, Arjun (2022): Dense and taxonomically detailed habitat maps of coral reef benthos machine-generated from underwater hyperspectral transects in Curaçao [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.946315 This dataset contains 248 benthic habitat maps, that were created from 31 underwater hyperspectral images captured with the HyperDiver device in 8 reef sites across the western coastline of Curacao (see https://doi.org/10.3390/data5010019 for information on the acquisition of the transects). The maps were produced by 8 combinations of two semantic labelspaces (detailed and reefgroups), two machine learning classifiers (patched and segmented), and two spectral signals (radiance and reflectance). Maps in the detailed labelspace have each pixel assigned to one of 43 labels, which are taxonomic labels at family, genus and species levels for biotic components of the reef (corals, sponges, macroalgae, etc.), as well as substrate labels (sediment, cyanobacterial mats, turf algae) and survey material labels (transect tape, reference board, etc.). The set of maps in the reefgroups labelspace cluster the labels in the detailed labelspace into 11 classes that describe reef functional groups (i.e. corals, sponges, algae, etc.). All habitat maps were produced with high accuracy (Fbeta 87%), by two different machine learning methods: a random forest ensemble classifier (segmented method) and a deep learning neural network classifier (patched method). The maps are further divided by the signal type from the hyperspectral image that was used, either radiance or reflectance (the latter was calculated with a reference board located at the beginning and end of each transect). These benthic habitat maps can be used to obtain accurate descriptions of the benthic community and habitat structure of coral reef sites in Curacao. The dataset also contains: an assessment of the accuracy and data efficiency of the machine learning methods, a consistency assessment of the mapped regions, a comparison of habitat metrics (class coverage, biodiversity indices, composition and configuration) between habitat maps produced by each method, and an effort-vs-error analysis of sparse sampling techniques on the densely classified maps. 16360 https://doi.org/10.1594/PANGAEA.946315 2022-07-13T08:42:02+0000 2022 19878 19988 14820 19838 15183 19836 19986 19984 19700 19698 19982 19980 19978 19976 19974 19972 19970 19968 19962 19960 19958 19964 18545