HLWATER V1.0 - Trained Mask R-CNN for surface water mapping in boreal forest-tundra ...

• Main Authors: Freitas, Pedro, Vieira, Gonçalo, Canário, João, Vincent, Warwick F., Pina, Pedro, Mora, Carla
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Zenodo 2023
• Subjects: Mask R-CNN; Deep Learning; PlanetScope; Water mapping; Small water bodies; Canada; Hudson; Hudson Bay; Nunavik; permafrost; Subarctic; Tundra
• Online Access: https://dx.doi.org/10.5281/zenodo.10203553; https://zenodo.org/doi/10.5281/zenodo.10203553

The HLWATER V1.0 is a Mask R-CNN supervised model trained over PlanetScope (including Dove and Dove-R) satellite imagery (Analytic MS – Surface Reflectance) with focus on the automated detection and delineation of small water bodies in the circumpolar North. The model was trained over a dataset of very-high resolution 23,432 manually delineated lakes, ponds, rivers, streams, creeks and coastal sectors, located in diverse landscapes from the sporadic to the continuous permafrost zones of Canada. The training dataset is freely available upon request to [e-mail]. Most training features (97%) were water bodies smaller than 1 hectare located in diverse environmental and hydrological settings. HLWATER V1.0 accuracy was tested for Eastern Hudson Bay (Nunavik, Subarctic Canada), a region that comprises a variety of water bodies, environments and permafrost in different stages of degradation. For the evaluation of model performance, a two-scale testing approach was used by comparing with manually ...