Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery

Schmidt, Erik H.; Bhaduri, Budhendra L.; Nagle, Nicholas N.; Ralston, Bruce A.

doi:10.1080/15481603.2018.1460933

Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery

Journal Article · Thu Apr 12 00:00:00 EDT 2018 · GIScience & Remote Sensing

DOI:https://doi.org/10.1080/15481603.2018.1460933· OSTI ID:1471940

^[1]; ^[2]; Nagle, Nicholas N. ^[3]; Ralston, Bruce A. ^[3]

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Univ. of Tennessee, Knoxville, TN (United States)
Univ. of Tennessee, Knoxville, TN (United States)

For many researchers, government agencies, and emergency responders, access to the geospatial data of US electric power infrastructure is invaluable for analysis, planning, and disaster recovery. Historically, however, access to high quality geospatial energy data has been limited to few agencies because of commercial licenses restrictions, and those resources which are widely accessible have been of poor quality, particularly with respect to reliability. Recent efforts to develop a highly reliable and publicly accessible alternative to the existing datasets were met with numerous challenges – not the least of which was filling the gaps in power transmission line voltage ratings. To address the line voltage rating problem, we developed and tested a basic methodology that fuses knowledge and techniques from power systems, geography, and machine learning domains. Specifically, we identified predictors of nominal voltage that could be extracted from aerial imagery and developed a tree-based classifier to classify nominal line voltage ratings. Overall, we found that line support height, support span, and conductor spacing are the best predictors of voltage ratings, and that the classifier built with these predictors had a reliable predictive accuracy (that is, within one voltage class for four out of the five classes sampled). In conclusion, we applied our approach to a study area in Minnesota.

View Accepted Manuscript (DOE)

Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1471940

Journal Information:: GIScience & Remote Sensing, Journal Name: GIScience & Remote Sensing Journal Issue: 6 Vol. 55; ISSN 1548-1603

Publisher:: Taylor & FrancisCopyright Statement

Country of Publication:: United States

Language:: English

References (17)

An Introduction to Statistical Learning James, Gareth; Witten, Daniela; Hastie, Trevor Springer Texts in Statistics https://doi.org/10.1007/978-1-0716-1418-1	book	January 2021
An Introduction to Statistical Learning James, Gareth; Witten, Daniela; Hastie, Trevor Springer Texts in Statistics https://doi.org/10.1007/978-1-4614-7138-7	book	January 2013
Induction of decision trees Quinlan, J. R. Machine Learning, Vol. 1, Issue 1, p. 81-106 https://doi.org/10.1007/BF00116251	journal	March 1986
A Comparative Study of Least Square Support Vector Machines and Multiclass Alternating Decision Trees for Spatial Prediction of Rainfall-Induced Landslides in a Tropical Cyclones Area Pham, Binh Thai; Tien Bui, Dieu; Dholakia, M. B. Geotechnical and Geological Engineering, Vol. 34, Issue 6 https://doi.org/10.1007/s10706-016-9990-0	journal	February 2016
Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines Hong, Haoyuan; Pradhan, Biswajeet; Xu, Chong CATENA, Vol. 133 https://doi.org/10.1016/j.catena.2015.05.019	journal	October 2015
LiDAR-Landsat data fusion for large-area assessment of urban land cover: Balancing spatial resolution, data volume and mapping accuracy Singh, Kunwar K.; Vogler, John B.; Shoemaker, Douglas A. ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 74 https://doi.org/10.1016/j.isprsjprs.2012.09.009	journal	November 2012
Open Data in Science Murray-Rust, Peter Serials Review, Vol. 34, Issue 1 https://doi.org/10.1016/j.serrev.2008.01.001	journal	March 2008
Voltage collapse in complex power grids Simpson-Porco, John W.; Dörfler, Florian; Bullo, Francesco Nature Communications, Vol. 7, Issue 1 https://doi.org/10.1038/ncomms10790	journal	February 2016
Benefits, Adoption Barriers and Myths of Open Data and Open Government Janssen, Marijn; Charalabidis, Yannis; Zuiderwijk, Anneke Information Systems Management, Vol. 29, Issue 4 https://doi.org/10.1080/10580530.2012.716740	journal	September 2012
Machine learning approaches for forest classification and change analysis using multi-temporal Landsat TM images over Huntington Wildlife Forest Li, Manqi; Im, Jungho; Beier, Colin GIScience & Remote Sensing, Vol. 50, Issue 4 https://doi.org/10.1080/15481603.2013.819161	journal	July 2013
Mapping urban sprawl and impervious surfaces in the northeast United States for the past four decades Torbick, Nathan; Corbiere, Megan GIScience & Remote Sensing, Vol. 52, Issue 6 https://doi.org/10.1080/15481603.2015.1076561	journal	August 2015
Crowdsourcing geographic information for disaster response: a research frontier Goodchild, Michael F.; Glennon, J. Alan International Journal of Digital Earth, Vol. 3, Issue 3 https://doi.org/10.1080/17538941003759255	journal	September 2010
Simplifying data access: the Energy Data Collection project Ambite, J. L.; Arens, Y.; Hovy, E. Computer, Vol. 34, Issue 3 https://doi.org/10.1109/2.901167	journal	March 2001
Power Flow Algorithms for Multi-Terminal VSC-HVDC With Droop Control Wang, Wenyuan; Barnes, Mike IEEE Transactions on Power Systems, Vol. 29, Issue 4 https://doi.org/10.1109/TPWRS.2013.2294198	journal	July 2014
A Systematic Comparison of Supervised Classifiers Amancio, Diego Raphael; Comin, Cesar Henrique; Casanova, Dalcimar PLoS ONE, Vol. 9, Issue 4 https://doi.org/10.1371/journal.pone.0094137	journal	April 2014
Open Data and Beyond Welle Donker, Frederika; van Loenen, Bastiaan; Bregt, Arnold ISPRS International Journal of Geo-Information, Vol. 5, Issue 4 https://doi.org/10.3390/ijgi5040048	journal	April 2016
Voltage collapse in complex power grids Simpson-Porco, John W.; Dörfler, Florian; Bullo, Francesco ETH Zurich https://doi.org/10.3929/ethz-b-000113968	text	January 2016

Similar Records

Providing Geospatial Intelligence through a Scalable Imagery Pipeline

Book · Mon May 01 00:00:00 EDT 2023 · OSTI ID:1976065

A Hybrid Semi-supervised Classification Scheme for Mining Multisource Geospatial Data

Journal Article · Fri Dec 31 23:00:00 EST 2010 · GeoInformatica: An International Journal on Advances of Computer Science for Geographic Information Systems · OSTI ID:1003735

Genetic programming approach to extracting features from remotely sensed imagery

Conference · Sun Dec 31 23:00:00 EST 2000 · OSTI ID:975334

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
GIS
electricity
machine learning
open data
transmission network
voltage ratings

Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery

Citation Formats

References (17)

Similar Records

Related Subjects