RiverPIXELS: paired Landsat images and expert-labeled sediment and water pixels for a selection of rivers v1.0 ...

• Main Authors: Schwenk, Jon, Rowland, Joel
• Format: Dataset
• Language: English
• Published: Environmental System Science Data Infrastructure for a Virtual Ecosystem; A Global, High-Resolution River Network Model for Improved Flood Risk Prediction 2022
• Subjects: 54 ENVIRONMENTAL SCIENCES; rivers; training data; landsat; sediment; floodplain; water; water occurrence; Arctic; Indigirka; Kolyma
• Online Access: https://dx.doi.org/10.15485/1865732; https://www.osti.gov/servlets/purl/1865732/

RiverPIXELS contains GeoTIFFs of hand-labeled water and sediment pixels from Landsat images containing rivers. Each of the 104 labeled patches contains 256 x 256 Landsat pixels (30 meter resolution). Our aim in releasing RiverPIXELS is to provide an "off-the-shelf" training and testing dataset for building machine-learned models to automatically identify rivers from multispectral imagery. While a number of trained models and/or surface water products already exist, RiverPIXELS aims for pixel-level accuracy in order to precisely identify river boundaries in particular. Our selection of rivers is heavily Arctic, but we include tropical and temperate rivers as well. Patches are provided for the Colville (7), Indigirka (6), Kolyma (4), Ucayali (54), Waitaki (21), and Yana (12) Rivers. For each patch, all surface water pixels are labeled (1) and all in-channel sediment pixels are labeled (2). Sediments not in-channel are considered part of the land (0) class. RiverPIXELS also includes paired surface water data ...