關於全球電力基礎設施的有限數據使得我們難以應對電力供應和氣候變化方面的挑戰。雖然經常有關於輸電網絡的高壓數據，但中壓和低壓數據往往不存在或無法獲得。這給從事電力接入議程、電力部門復原力或氣候變化適應工作的從業人員帶來了挑戰。利用地理空間數據分析的最先進的算法，我們創建了第一個具有開放許可證的全球電力系統綜合地圖。我們發現，全球97%的人口生活在中壓線10公里以內，但各地區和收入水平之間存在很大差異。我們在14個國家的驗證集中顯示了75%的準確率，並且我們證明了這些數據在國家和地區層面的價值。這項研究的結果為改進電力建模和規劃工作鋪平了道路，是實現可持續發展目標的重要一步。

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