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DESS China Terrace Map v1
此数据集是中国 2018 年分辨率为 30 米的梯田地图。该模型是基于 Google Earth Engine 平台,使用多源多时段数据通过基于像素的监督分类开发的。总体准确率和 Kappa 系数分别达到 94% 和 0.72。第一个… 农业 土地覆盖 土地利用 土地利用-土地覆盖 清华 -
Tsinghua FROM-GLC 改为不透水表面的年份
此数据集包含 1985 年至 2018 年全球不透水表面面积的年变化信息,分辨率为 30 米。我们采用监督分类和时间一致性检查相结合的方法,确定了从透水性地表变为不透水性地表的变化。不透水像素是指不透水面积占比超过 50% 的像素。… 建成 人口 清华 城市
Datasets tagged tsinghua in Earth Engine
[null,null,[],[[["\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) |"]]