全球电力系统综合地图

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关于全球电力基础设施的有限数据使得我们难以应对电力供应和气候变化方面的挑战。虽然经常有关于输电网络的高压数据，但中压和低压数据往往不存在或无法获得。这给从事电力接入议程、电力部门复原力或气候变化适应工作的从业人员带来了挑战。利用地理空间数据分析的最先进的算法，我们创建了第一个具有开放许可证的全球电力系统综合地图。我们发现，全球97%的人口生活在中压线10公里以内，但各地区和收入水平之间存在很大差异。我们在14个国家的验证集中显示了75%的准确率，并且我们证明了这些数据在国家和地区层面的价值。这项研究的结果为改进电力建模和规划工作铺平了道路，是实现可持续发展目标的重要一步。前言 – 床长人工智能教程

Download the dataset here

Use the following credit when these datasets are cited:

Arderne, Christopher, NIcolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data (Version 1.1.1) [Data set]. Nature Scientific Data. Zenodo. http://doi.org/10.5281/zenodo.3628142

Cite the paper using

Arderne, Christopher, Conrad Zorn, Claire Nicolas, and E. E. Koks. "Predictive mapping of the global power system using open data." Scientific data 7, no. 1 (2020): 1-12.

Current version: v1.1.1 released 2020-01-16 You can access the app here: https://gridfinder.org/

Earth Engine Snippet¶

var lv = ee.Image("projects/sat-io/open-datasets/predictive-global-power-system/lv");
var targets = ee.Image("projects/sat-io/open-datasets/predictive-global-power-system/targets");
var transmission = ee.FeatureCollection("projects/sat-io/open-datasets/predictive-global-power-system/distribution-transmission-lines");

Resolutions¶

lv is at 250m, targets at 463.83 m

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

Shared License: This work is licensed under a Creative Commons Attribution 4.0 International License. You are free to copy and redistribute the material in any medium or format, and to transform and build upon the material for any purpose, even commercially. You must give appropriate credit, provide a link to the license, and indicate if changes were made.

Curated by: Samapriya Roy

Keywords: Global transmission lines, electricity, infrastructure, power

Last updated: 2021-04-03

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