The MASSIMAL Dataset: UAV Hyperspectral Images and Ground Truth Observations for Coastal Habitat Mapping in Norway
Martin Hansen Skjelvareid, Eli Rinde, Kasper Hancke, Katalin Blix, Galice Guillaume Hoarau, Nicolo Grometto, Maia Røst Kile
Abstract
The MASSIMAL dataset was collected in an effort to develop new remote sensing methods for mapping shallow water coastal habitats, such as kelp forests, seagrass meadows and maerl beds. The main component of the dataset is a set of hyperspectral images collected using a unmanned aerial vehicle (UAV). Images were collected during 45 UAV flights performed at 4 main areas along the Norwegian coast, spanning across a wide latitude range, from 59 to 67 degrees north. The ground sampling distance of the hyperspectral images is 3.5 cm, and the spectral range is 400-1000 nm, sampled across 300 spectral bands. During acquisition, push-broom imagery was automatically split into images with a maximum along-track length of 2000 lines. Out of approximately 1000 hyperspectral images, 221 have been annotated, using segmentation masks and a hierarchical taxonomy. An open source image processing pipeline has been developed as part of the dataset preparation, enabling efficient generation of reflectance images and glint-corrected images based on published radiance images. The dataset also includes color images (RGB) and multispectral images collected using UAV, and georeferenced image mosaics constructed from these. Ground truth data supporting the remote sensing images include hours of underwater video, thousands of geotagged underwater images, and tens of kilometers of single-beam sonar data. The data provide a foundation for assessing habitat distribution, tracking changes, and evaluating ecosystem services, thus supporting evidence-based management in Norwegian coastal environments.