Google Earth Engine（GEE）——全球光伏发电目录（2016-2018）

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全球光伏发电目录（2016-2018）
自2009年以来，光伏（PV）太阳能发电能力每年增长41%。作者指出，缓解气候变化和帮助普及能源的能源系统预测显示，到2040年，光伏太阳能发电能力将增加近10倍。作者进一步找到并核实了68,661个设施，在以前可获得的资产层面的数据上，增加了432%（设施数量）。在手工标记的测试集的帮助下，我们估计2018年底全球发电装机容量为423千兆瓦（-75/+77千兆瓦）。

对于超过10,000平方米（约600千瓦）的装置，相对于我们的测试集，实现的精度为98.6%，召回率略有折损，下降到90%（补充图6）。对于超过10,000平方米的装置，最终数据集的IoU为90%--足以满足基于用户报告的广泛用途。A global inventory of photovoltaic solar energy generating units | Nature

Citation:¶

Kruitwagen, L., Story, K.T., Friedrich, J. et al. A global inventory of photovoltaic solar energy generating units.
Nature 598, 604–610 (2021). https://doi.org/10.1038/s41586-021-03957-7

Dataset Citation¶

Kruitwagen, Lucas, Story, Kyle, Friedrich, Johannes, Byers, Logan, Skillman, Sam, & Hepburn, Cameron. (2021). A global
inventory of solar photovoltaic generating units - dataset (1.0.0) [Data set].
Zenodo. https://doi.org/10.5281/zenodo.5005868

Google Earth Engine（GEE）——全球光伏发电目录（2016-2018）_gee

Earth Engine Snippet¶

var predicted_set = ee.FeatureCollection("projects/sat-io/open-datasets/global_photovoltaic/predicted_set");
var cv_polygons = ee.FeatureCollection("projects/sat-io/open-datasets/global_photovoltaic/cv_polygons");
var cv_tiles = ee.FeatureCollection("projects/sat-io/open-datasets/global_photovoltaic/cv_tiles");
var test_polygons = ee.FeatureCollection("projects/sat-io/open-datasets/global_photovoltaic/test_polygons");
var test_tiles = ee.FeatureCollection("projects/sat-io/open-datasets/global_photovoltaic/test_tiles");
var trn_tiles = ee.FeatureCollection("projects/sat-io/open-datasets/global_photovoltaic/trn_tiles");
var trn_polygons = ee.FeatureCollection("projects/sat-io/open-datasets/global_photovoltaic/trn_polygons");

Sample code: https://code.earthengine.google.com/?scriptPath=users/sat-io/awesome-gee-catalog-examples:global-utilities-assets-amenities/GLOBAL-PHOTOVOLTAICS-INVENTORY

Layer name and description table¶

File Name	Description
trn_tiles	18,570 rectangular areas-of-interest used for sampling training patch data.
trn_polygons	36,882 polygons obtained from OSM in 2017 used to label training patches
cv_tiles	560 rectangular areas-of-interest used for sampling cross-validation data seeded from WRI GPPDB
cv_polygons	6,281 polygons corresponding to all PV solar generating units present in cv_tiles at the end of 2018.
test_tiles	122 rectangular regions-of-interest used for building the test set.
test_polygons	7,263 polygons corresponding to all utility-scale (>10kW) solar generating units present in test_tiles at the end of 2018.
predicted_set	68,661 polygons corresponding to predicted polygons in global deployment, capturing the status of deployed photovoltaic solar energy generating capacity at the end of 2018.