Articles | Volume 16, issue 9
https://doi.org/10.5194/essd-16-4311-2024
https://doi.org/10.5194/essd-16-4311-2024
Data description paper
 | 
23 Sep 2024
Data description paper |  | 23 Sep 2024

A globally sampled high-resolution hand-labeled validation dataset for evaluating surface water extent maps

Rohit Mukherjee, Frederick Policelli, Ruixue Wang, Elise Arellano-Thompson, Beth Tellman, Prashanti Sharma, Zhijie Zhang, and Jonathan Giezendanner

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Cited articles

Acharki, S.: PlanetScope contributions compared to Sentinel-2, and Landsat-8 for LULC mapping, Remote Sensing Applications: Society and Environment, 27, 100774, https://doi.org/10.1016/j.rsase.2022.100774, 2022. a
Alemohammad, H. and Booth, K.: LandCoverNet: A global benchmark land cover classification training dataset, arXiv [preprint], arXiv:2012.03111, 2020. a
Bamber, J. and Bindschadler, R.: An improved elevation dataset for climate and ice-sheet modelling: validation with satellite imagery, Ann. Glaciol., 25, 439–444, 1997. a
Bijeesh, T. and Narasimhamurthy, K.: Surface water detection and delineation using remote sensing images: A review of methods and algorithms, Sustainable Water Resour. Manag., 6, 1–23, 2020. a
Bonafilia, D., Tellman, B., Anderson, T., and Issenberg, E.: Sen1Floods11: A georeferenced dataset to train and test deep learning flood algorithms for Sentinel-1, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA, 835–845, https://doi.org/10.1109/CVPRW50498.2020.00113, 2020. a, b
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Global water resource monitoring is crucial due to climate change and population growth. This study presents a hand-labeled dataset of 100 PlanetScope images for surface water detection, spanning diverse biomes. We use this dataset to evaluate two state-of-the-art mapping methods. Results highlight performance variations across biomes, emphasizing the need for diverse, independent validation datasets to enhance the accuracy and reliability of satellite-based surface water monitoring techniques.

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