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Citizen Science initiatives have a worldwide impact on environmental research by providing data at a global scale and high resolution. Mapping marine biodiversity remains a key challenge to which citizen initiatives can contribute. Here we describe a dataset made of both underwater and aerial imagery collected in shallow tropical coastal areas by using various low cost platforms operated either by citizens or researchers. This dataset is regularly updated and contains >1.6 M images from the Southwest Indian Ocean. Most of images are geolocated, and some are annotated with 51 distinct classes (e.g. fauna, and habitats) to train AI models. The quality of these photos taken by action cameras along the trajectories of different platforms, is highly heterogeneous (due to varying speed, depth, turbidity, and perspectives) and well reflects the challenges of underwater image recognition. Data discovery and access rely on DOI assignment while data interoperability and reuse is ensured by complying with widely used community standards. The open-source data workflow is provided to ease contributions from anyone collecting pictures. [ABSTRACT FROM AUTHOR]

Bouet, A.; Collin, A.; James, D.; Rintz, C-L.; Dusseau, P.; Lesacher, M., and Feunteun, E., 2024. On the synergy of the UAV spatial modelling and citizen science for tidal coasts: The case of canopy-forming macroalgae and gastropods on the Emerald Coast, Brittany, France. In: Phillips, M.R.; Al-Naemi, S., and Duarte, C.M. (eds.), Coastlines under Global Change: Proceedings from the International Coastal Symposium (ICS) 2024 (Doha, Qatar). Journal of Coastal Research, Special Issue No. 113, pp. 154-158. Charlotte (North Carolina), ISSN 0749-0208. Coastal marine ecosystems provide crucial services to cope with accelerating global changes. Among them, intertidal rocky shores and their canopy-forming macroalgal habitats support a high biodiversity, provide food, offer coastal protection, and multiple cultural services. Due to their location, at the land-sea interface, these coastal areas are threatened by various distal and local anthropogenic pressures. Their monitoring at very high (dm) spatial resolution is necessary to better inform their sustainable management. The very high temporal resolution of the unmanned airborne vehicle (UAV) imagery can be agile enough to monitor intertidal shores during the low tide's short window. Active or passive UAV imagery, coupled with machine learning, has the capabilities to map habitats, but requires ground-truth data, whose amount is strongly constrained by the low tide period. The study proposes to combine quantitative information, collected by trained citizens, with UAV data. UAV-based data were collected using two different sensors: a topographic Light Detection and Ranging (LiDAR) system DJI L1 and a multispectral imager MicaSense Altum-PT. Linear regressions of the canopy-forming macroalgae cover and the abundance of the gastropod, Steromphala umbilicalis, were run based on UAV by-products. Multispectral orthomosaics better predicted the algae cover (R2 = 0.52) and the abundance of gastropod (R2 = 0.45) than lasergrammetric data (algae cover: R2 = 0.48; gastropod abundance: R2 = 0.42). A combination of both source of data revealed better prediction for the algae cover (R2 = 0.68), as well as for the gastropod abundance (R2 = 0.55). Results are discussed in the light of the cm-scale remote sensing but also in its aspect of environmental awareness and responsive action. [ABSTRACT FROM AUTHOR]