This dataset is a China terrace map at 30 m resolution in 2018. It was developed through supervised pixel-based classification using multisource and multi-temporal data based on the Google Earth Engine platform. The overall accuracy and kappa coefficient achieved 94% and 0.72, respectively. This first …
This dataset contains annual change information of global impervious surface area from 1985 to 2018 at a 30m resolution. Change from pervious to impervious was determined using a combined approach of supervised classification and temporal consistency checking. Impervious pixels are defined as above 50% impervious. …
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],[],[[["\u003cp\u003eThe DESS China Terrace Map provides a 30m resolution view of terrace farming across China in 2018, achieving high accuracy through supervised classification using multi-source data.\u003c/p\u003e\n"],["\u003cp\u003eThe Tsinghua FROM-GLC dataset offers insights into annual changes in global impervious surfaces from 1985 to 2018 at 30m resolution, identifying areas where pervious land has become impervious.\u003c/p\u003e\n"]]],["Two datasets are described: a 2018 China terrace map at 30m resolution, created via supervised pixel-based classification using multisource and multi-temporal data. The method had an overall accuracy of 94% and a kappa coefficient of 0.72. The second dataset provides annual changes in global impervious surface area, from 1985 to 2018 at 30m resolution. This was done by a combination of supervised classification and temporal consistency checking. Impervious pixels are above 50% impervious.\n"],null,["# Datasets tagged tsinghua in Earth Engine\n\n-\n\n |--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### DESS China Terrace Map v1](/earth-engine/datasets/catalog/Tsinghua_DESS_ChinaTerraceMap_v1) |\n | This dataset is a China terrace map at 30 m resolution in 2018. It was developed through supervised pixel-based classification using multisource and multi-temporal data based on the Google Earth Engine platform. The overall accuracy and kappa coefficient achieved 94% and 0.72, respectively. This first ... |\n | [agriculture](/earth-engine/datasets/tags/agriculture) [landcover](/earth-engine/datasets/tags/landcover) [landuse](/earth-engine/datasets/tags/landuse) [landuse-landcover](/earth-engine/datasets/tags/landuse-landcover) [tsinghua](/earth-engine/datasets/tags/tsinghua) |\n\n-\n\n |---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n | [### Tsinghua FROM-GLC Year of Change to Impervious Surface](/earth-engine/datasets/catalog/Tsinghua_FROM-GLC_GAIA_v10) |\n | This dataset contains annual change information of global impervious surface area from 1985 to 2018 at a 30m resolution. Change from pervious to impervious was determined using a combined approach of supervised classification and temporal consistency checking. Impervious pixels are defined as above 50% impervious. ... |\n | [built](/earth-engine/datasets/tags/built) [population](/earth-engine/datasets/tags/population) [tsinghua](/earth-engine/datasets/tags/tsinghua) [urban](/earth-engine/datasets/tags/urban) |"]]
