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Experimental Dataset of Identifying Road Material Using GF-6 Images


CUI Yuping1
1 China Highway Engineering Consulting Corporation,Beijing 100097,China

DOI:10.3974/geodb.2022.08.10.V1

Published:Aug. 2022

Visitors:646       Data Files Downloaded:88      
Data Downloaded:47138.91 MB      Citations:

Key Words:

road material,machine learning,GF-6,Langfang

Abstract:

Based on the GF-6 images, the spectral characteristic indexes - spectral difference index, spectral ratio index, spectral variance index and spectral normalization index - were calculated in the experimental area in Langfang, Hebei Province of China. The sample data of road material was obtained based on Google Earth images and Baidu Maps. The machine learning technology was used to develop the experimental dataset of identifying road material from GF-6 images. Compared with the samples, the road material identification accuracy is 80.07%, and the Kappa coefficient is 0.70. The dataset consists of: (1) spectral characteristic index data; (2) road material sample data; (3) road material identification result. The dataset is archived in .dat, .shp. and .xlsx formats, and consists of 16 data files with data size of 3.69 GB (compressed into 4 files, 1.62 GB).

Foundation Item:

GF-6 (07-Y30B03-9001-19/21, 87-Y50G28-9001-22/23);

Data Citation:

CUI Yuping. Experimental Dataset of Identifying Road Material Using GF-6 Images[J/DB/OL]. Digital Journal of Global Change Data Repository, 2022. https://doi.org/10.3974/geodb.2022.08.10.V1.

References:

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Data Product:

ID Data Name Data Size Operation
1 1_SpectralCharacteristicIndex_SDI_SVI.rar 210870.80KB
2 1_SpectralCharacteristicIndex_SNI.rar 757602.06KB
3 1_SpectralCharacteristicIndex_SRI.rar 730480.19KB
4 2&3_SampleData_RoadMaterial.rar 1705.59KB
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