Landform classification through downscaling approaches for the Bislak, Laoag and Abra Rivers, the Philippines

Li, Q., Williams, R. , Hoey, T., Barrett, B. and Boothroyd, R. (2022) Landform classification through downscaling approaches for the Bislak, Laoag and Abra Rivers, the Philippines. [Data Collection]

Not all data is available to download from this page. Usually this is because the dataset is too large or it is restricted in some way.

Collection description

This dataset contains landform classification maps for the Bislak (16/01/2017), Laoag (10/07/2018) and Abra (21/11/2019) Rivers in northwest Luzon, the Philippines. River landforms (water, vegetated bars, unvegetated bars) were classified using the SVM machine learning model established on Sentinel-2 Level 2A data. The classification training/testing dataset have been enveloped in LibSVM format (.txt) for reading and processing in Python 3.7 using Scikit-Learn Tools. In addition to the landform classification maps, the dataset includes rasters (.tif) from three downscaling approaches (area-to-point regression kriging, nearest neighbour resampling and super-resolution method) and their associated image segmentation shapefiles (.shp) for the Bislak River on 1 January 2018. The area-to-point regression kriging (ATPRK) method was processed using codes developed by Wang et al (2016). The nearest neighbour resampling downscaling images were generated in SNAP software. The super-resolution downscaling images were obtained using the Sen2Res plugin in SNAP software.

College / School: College of Science and Engineering > School of Geographical and Earth Sciences
Date Deposited: 26 Oct 2022 12:23
URI: https://researchdata.gla.ac.uk/id/eprint/1355

Available Files

Data

Read me

Repository Staff Only: Update this record

Li, Q., Williams, R. , Hoey, T., Barrett, B. and Boothroyd, R. (2022); Landform classification through downscaling approaches for the Bislak, Laoag and Abra Rivers, the Philippines

University of Glasgow

DOI: 10.5525/gla.researchdata.1355

Retrieved: 2024-10-06

Downloads

Downloads per month over past year