Electric Transmission and Distribution Infrastructure Imagery Dataset
2018-08-03T21:00:04Z (GMT) by
The dataset contains fully annotated electric transmission and distribution infrastructure for approximately 321 sq km of high resolution satellite and aerial imagery from around the world. The imagery and associated infrastructure annotations span 14 cities and 5 continents, and were selected to represent diversity in human settlement density (i.e. rural vs urban), terrain type, and development index. This dataset may be of particular interest to those looking to train machine learning algorithms to automatically identify energy infrastructure in satellite imagery or for those working on domain adaptation for computer vision. Automated algorithms for identifying electricity infrastructure in satellite imagery may assist policy makers identify the best pathway to electrification for unelectrified areas.
This dataset contains data sourced from the LINZ Data Service licensed for reuse under CC BY 4.0.
This dataset also contained extracts from the SpaceNet dataset:
SpaceNet on Amazon Web Services (AWS). “Datasets.” The SpaceNet Catalog. Last modified April 30, 2018 (link below).
Other imagery data included in this dataset are from the Connecticut Department of Energy and Environmental Protection and the U.S. Geological Survey.
Links to each of the imagery data sources are provided below as well as the link to the annotation tool and the github repository that provides tools for using these data.
This dataset was created as part of the Duke University Data+ project, "Energy Infrastructure Map of the World" (link below) in collaboration with the Information Initiative at Duke and the Duke University Energy Initiative.
- Energy Generation, Conversion and Storage Engineering
- Power and Energy Systems Engineering (excl. Renewable Power)
- Renewable Power and Energy Systems Engineering (excl. Solar Cells)
- Computer Vision
- Knowledge Representation and Machine Learning
- Image Processing
- Artificial Intelligence and Image Processing
CC BY 4.0