publication . Other literature type . Article . 2018

Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images.

Chuanrong Zhang; Weidong Li;
Open Access
  • Published: 01 Aug 2018
  • Publisher: MDPI AG
  • Country: United States
Abstract
National Science Foundation (U.S.) (grant No. 1414108)
Subjects
free text keywords: Article, deep learning, utility pole, infrastructure mapping, Google Street View, line-of-bearing measurement, object detection, Chemical technology, TP1-1185
Funded by
NSF| CNH-Ex: Interactive Effects of Economics, Public Policy, Land-Use Change, and Invasive Plants in the Long Island Sound Watersheds
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1414108
  • Funding stream: Directorate for Social, Behavioral & Economic Sciences | Division of Behavioral and Cognitive Sciences
Download fromView all 7 versions
Sensors
Article . 2018
DSpace@MIT
Article . 2018
Provider: DSpace@MIT
25 references, page 1 of 2

Nagura, S.; Masumoto, T.; Endo, K.; Wakasa, F.; Watanabe, S.; Ikeda, K. Development of mapping system 15. Jwa, Y.; Sohn, G. A piecewise catenary curve model growing for 3D power line reconstruction.

Photogramm. Eng. Remote Sens. 2012, 78, 1227-1240. [CrossRef] 16. Kim, E.; Medioni, G. Urban scene understanding from aerial and ground LIDAR data. Mach. Vis. Appl. 2011, 22, 691-703. [CrossRef] 17. Kim, H.B.; Sohn, G. Point-based classification of power line corridor scene using random forests.

Photogramm. Eng. Remote Sens. 2013, 79, 821-833. [CrossRef] 18. McLaughlin, R.A. Extracting transmission lines from airborne LIDAR data. IEEE Geosci. Remote Sens. Lett.

2006, 3, 222-226. [CrossRef] 19. Wang, Y.; Chen, Q.; Liu, L.; Zheng, D.; Li, C.; Li, K. Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas. Remote Sens. 2017, 9, 771. [CrossRef] 20. Sun, C.; Jones, R.; Talbot, H.; Wu, X.; Cheong, K.; Beare, R.; Buckley, M.; Berman, M. Measuring the distance of vegetation from powerlines using stereo vision. ISPRS J. Photogramm. Remote Sens. 2006, 60, 269-283.

[CrossRef] 21. Matikainen, L.; Lehtomäki, M.; Ahokas, E.; Hyyppä, J.; Karjalainen, M.; Jaakkola, A.; Kukko, A.; Heinonen, T.

Remote sensing methods for power line corridor surveys. ISPRS J. Photogramm. Remote Sens. 2016, 119, 10-31. [CrossRef] 22. Moore, A.J.; Schubert, M.; Rymer, N. Autonomous Inspection of Electrical Transmission Structures with Airborne UV Sensors-NASA Report on Dominion Virginia Power Flights of November 2016. NASA/TM-2017-219611, L-20808, NF1676L-26882, Nasa Technical Reports Server; Document ID: 20170004692; 1 May 2017. Available online: https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20170004692.pdf (accessed on 12 February 2017).

23. Oh, J.; Lee, C. 3D power line extraction from multiple aerial images. Sensors 2017, 17, 2244. [CrossRef] [PubMed] 24. Zhang, Y.; Yuan, X.; Li, W.; Chen, S. Automatic Power Line Inspection Using UAV Images. Remote Sens. 2017, 9, 824. [CrossRef] 25. Zhu, L.; Hyyppä, J. Fully-automated power line extraction from airborne laser scanning point clouds in forest areas. Remote Sens. 2014, 6, 11267-11282. [CrossRef] 26. Cabo, C.; Ordoñez, C.; García-Cortés, S.; Martínez, J. An algorithm for automatic detection of pole-like street furniture objects from Mobile Laser Scanner point clouds. ISPRS J. Photogramm. Remote Sens. 2014, 87, 47-56.

[CrossRef] 27. Lehtomäki, M.; Jaakkola, A.; Hyyppä, J.; Kukko, A.; Kaartinen, H. Detection of vertical pole-like objects in a road environment using vehicle-based laser scanning data. Remote Sens. 2010, 2, 641-664. [CrossRef] 28. Cheng, L.; Tong, L.; Wang, Y.; Li, M. Extraction of urban power lines from vehicle-borne LiDAR data.

Remote Sens. 2014, 6, 3302-3320. [CrossRef] 29. Guan, H.; Yu, Y.; Li, J.; Ji, Z.; Zhang, Q. Extraction of power-transmission lines from vehicle-borne lidar data.

Int. J. Remote Sens. 2016, 37, 229-247. [CrossRef] 30. Sharma, H.; Adithya, V.; Dutta, T.; Balamuralidhar, P. Image Analysis-Based Automatic Utility Pole Detection for Remote Surveillance. In Proceedings of the 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Adelaide, Australia, 23-25 November 2015. [CrossRef] 31. Anguelov, D.; Dulong, C.; Filip, D.; Frueh, C.; Lafon, S.; Lyon, R.; Ogale, A.; Vincent, L.; Weaver, J. Google street view: Capturing the world at street level. Computer 2010, 43, 32-38. [CrossRef] 32. Li, X.; Zhang, C.; Li, W.; Ricard, R.; Meng, Q.; Zhang, W. Assessing street-level urban greenery using Google Street View and a modified green view index. Urban For. Urban Green. 2015, 14, 675-685. [CrossRef] 33. Li, X.; Zhang, C.; Li, W.; Kuzovkina, Y.A.; Weiner, D. Who lives in greener neighborhoods? The distribution of street greenery and its association with residents' socioeconomic conditions in Hartford, Connecticut, USA. Urban For. Urban Green. 2015, 14, 751-759. [CrossRef] 34. Li, X.; Zhang, C.; Li, W. Building block level urban land-use information retrieval based on Google Street View images. GIsci. Remote Sens. 2017, 54, 819-835. [CrossRef] 35. Zhang, W.; Li, W.; Zhang, C.; Hanink, D.M.; Li, X.; Wang, W. Parcel-based urban land use classification in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street View. Comput. Environ.

Urban Syst. 2017, 64, 215-228. [CrossRef] 36. Li, X.; Ratti, C.; Seiferling, I. Quantifying the shade provision of street trees in urban landscape: A case study in Boston, USA, using Google Street View. Landsc. Urban Plan. 2018, 169, 81-91. [CrossRef] 37. Cheng, W.; Song, Z. Power pole detection based on graph cut. In Proceedings of the 2008 Congress on Image and Signal Processing, Sanya, China, 27-30 May 2008. [CrossRef] 38. Murthy, V.S.; Gupta, S.; Mohanta, D.K. Digital image processing approach using combined wavelet hidden Markov model for well-being analysis of insulators. IET Image Process. 2011, 5, 171-183. [CrossRef] 39. Barranco-Gutiérrez, A.I.; Martínez-Díaz, S.; Gómez-Torres, J.L. An Approach for Utility Pole Recognition in Real Conditions. In Proceedings of the Image and Video Technology-PSIVT 2013 Workshops, Guanajuato, Mexico, 28-29 October 2013; Huang, F., Sugimoto, A., Eds.; Springer: Berlin/Heidelberg, Germany, 2013. [OpenAIRE]

[CrossRef] 40. Song, B.; Li, X. Power line detection from optical images. Neurocomputing 2014, 129, 350-361. [CrossRef] 41. Mills, S.J.; Castro, M.P.G.; Li, Z.; Cai, J.; Hayward, R.; Mejias, L.; Walker, R.A. Evaluation of aerial remote sensing techniques for vegetation management in power-line corridors. IEEE Trans. Geosci. Remote Sens.

2010, 48, 3379-3390. [CrossRef] 42. Buduma, N.; Locascio, N. Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms, 1st ed.; O'Reilly Media, Inc.: Sebastopol, CA, USA, 2017; ISBN 1491925612.

43. Géron, A. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 1st ed.; O'Reilly Media, Inc.: Sebastopol, CA, USA, 2017; ISBN 1491962291.

44. Goodfellow, I.; Bengio, Y.; Courville, A.; Bengio, Y. Deep Learning; MIT Press: Cambridge, MA, USA, 2016; ISBN 0262035618.

25 references, page 1 of 2
Abstract
National Science Foundation (U.S.) (grant No. 1414108)
Subjects
free text keywords: Article, deep learning, utility pole, infrastructure mapping, Google Street View, line-of-bearing measurement, object detection, Chemical technology, TP1-1185
Funded by
NSF| CNH-Ex: Interactive Effects of Economics, Public Policy, Land-Use Change, and Invasive Plants in the Long Island Sound Watersheds
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1414108
  • Funding stream: Directorate for Social, Behavioral & Economic Sciences | Division of Behavioral and Cognitive Sciences
Download fromView all 7 versions
Sensors
Article . 2018
DSpace@MIT
Article . 2018
Provider: DSpace@MIT
25 references, page 1 of 2

Nagura, S.; Masumoto, T.; Endo, K.; Wakasa, F.; Watanabe, S.; Ikeda, K. Development of mapping system 15. Jwa, Y.; Sohn, G. A piecewise catenary curve model growing for 3D power line reconstruction.

Photogramm. Eng. Remote Sens. 2012, 78, 1227-1240. [CrossRef] 16. Kim, E.; Medioni, G. Urban scene understanding from aerial and ground LIDAR data. Mach. Vis. Appl. 2011, 22, 691-703. [CrossRef] 17. Kim, H.B.; Sohn, G. Point-based classification of power line corridor scene using random forests.

Photogramm. Eng. Remote Sens. 2013, 79, 821-833. [CrossRef] 18. McLaughlin, R.A. Extracting transmission lines from airborne LIDAR data. IEEE Geosci. Remote Sens. Lett.

2006, 3, 222-226. [CrossRef] 19. Wang, Y.; Chen, Q.; Liu, L.; Zheng, D.; Li, C.; Li, K. Supervised Classification of Power Lines from Airborne LiDAR Data in Urban Areas. Remote Sens. 2017, 9, 771. [CrossRef] 20. Sun, C.; Jones, R.; Talbot, H.; Wu, X.; Cheong, K.; Beare, R.; Buckley, M.; Berman, M. Measuring the distance of vegetation from powerlines using stereo vision. ISPRS J. Photogramm. Remote Sens. 2006, 60, 269-283.

[CrossRef] 21. Matikainen, L.; Lehtomäki, M.; Ahokas, E.; Hyyppä, J.; Karjalainen, M.; Jaakkola, A.; Kukko, A.; Heinonen, T.

Remote sensing methods for power line corridor surveys. ISPRS J. Photogramm. Remote Sens. 2016, 119, 10-31. [CrossRef] 22. Moore, A.J.; Schubert, M.; Rymer, N. Autonomous Inspection of Electrical Transmission Structures with Airborne UV Sensors-NASA Report on Dominion Virginia Power Flights of November 2016. NASA/TM-2017-219611, L-20808, NF1676L-26882, Nasa Technical Reports Server; Document ID: 20170004692; 1 May 2017. Available online: https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20170004692.pdf (accessed on 12 February 2017).

23. Oh, J.; Lee, C. 3D power line extraction from multiple aerial images. Sensors 2017, 17, 2244. [CrossRef] [PubMed] 24. Zhang, Y.; Yuan, X.; Li, W.; Chen, S. Automatic Power Line Inspection Using UAV Images. Remote Sens. 2017, 9, 824. [CrossRef] 25. Zhu, L.; Hyyppä, J. Fully-automated power line extraction from airborne laser scanning point clouds in forest areas. Remote Sens. 2014, 6, 11267-11282. [CrossRef] 26. Cabo, C.; Ordoñez, C.; García-Cortés, S.; Martínez, J. An algorithm for automatic detection of pole-like street furniture objects from Mobile Laser Scanner point clouds. ISPRS J. Photogramm. Remote Sens. 2014, 87, 47-56.

[CrossRef] 27. Lehtomäki, M.; Jaakkola, A.; Hyyppä, J.; Kukko, A.; Kaartinen, H. Detection of vertical pole-like objects in a road environment using vehicle-based laser scanning data. Remote Sens. 2010, 2, 641-664. [CrossRef] 28. Cheng, L.; Tong, L.; Wang, Y.; Li, M. Extraction of urban power lines from vehicle-borne LiDAR data.

Remote Sens. 2014, 6, 3302-3320. [CrossRef] 29. Guan, H.; Yu, Y.; Li, J.; Ji, Z.; Zhang, Q. Extraction of power-transmission lines from vehicle-borne lidar data.

Int. J. Remote Sens. 2016, 37, 229-247. [CrossRef] 30. Sharma, H.; Adithya, V.; Dutta, T.; Balamuralidhar, P. Image Analysis-Based Automatic Utility Pole Detection for Remote Surveillance. In Proceedings of the 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Adelaide, Australia, 23-25 November 2015. [CrossRef] 31. Anguelov, D.; Dulong, C.; Filip, D.; Frueh, C.; Lafon, S.; Lyon, R.; Ogale, A.; Vincent, L.; Weaver, J. Google street view: Capturing the world at street level. Computer 2010, 43, 32-38. [CrossRef] 32. Li, X.; Zhang, C.; Li, W.; Ricard, R.; Meng, Q.; Zhang, W. Assessing street-level urban greenery using Google Street View and a modified green view index. Urban For. Urban Green. 2015, 14, 675-685. [CrossRef] 33. Li, X.; Zhang, C.; Li, W.; Kuzovkina, Y.A.; Weiner, D. Who lives in greener neighborhoods? The distribution of street greenery and its association with residents' socioeconomic conditions in Hartford, Connecticut, USA. Urban For. Urban Green. 2015, 14, 751-759. [CrossRef] 34. Li, X.; Zhang, C.; Li, W. Building block level urban land-use information retrieval based on Google Street View images. GIsci. Remote Sens. 2017, 54, 819-835. [CrossRef] 35. Zhang, W.; Li, W.; Zhang, C.; Hanink, D.M.; Li, X.; Wang, W. Parcel-based urban land use classification in megacity using airborne LiDAR, high resolution orthoimagery, and Google Street View. Comput. Environ.

Urban Syst. 2017, 64, 215-228. [CrossRef] 36. Li, X.; Ratti, C.; Seiferling, I. Quantifying the shade provision of street trees in urban landscape: A case study in Boston, USA, using Google Street View. Landsc. Urban Plan. 2018, 169, 81-91. [CrossRef] 37. Cheng, W.; Song, Z. Power pole detection based on graph cut. In Proceedings of the 2008 Congress on Image and Signal Processing, Sanya, China, 27-30 May 2008. [CrossRef] 38. Murthy, V.S.; Gupta, S.; Mohanta, D.K. Digital image processing approach using combined wavelet hidden Markov model for well-being analysis of insulators. IET Image Process. 2011, 5, 171-183. [CrossRef] 39. Barranco-Gutiérrez, A.I.; Martínez-Díaz, S.; Gómez-Torres, J.L. An Approach for Utility Pole Recognition in Real Conditions. In Proceedings of the Image and Video Technology-PSIVT 2013 Workshops, Guanajuato, Mexico, 28-29 October 2013; Huang, F., Sugimoto, A., Eds.; Springer: Berlin/Heidelberg, Germany, 2013. [OpenAIRE]

[CrossRef] 40. Song, B.; Li, X. Power line detection from optical images. Neurocomputing 2014, 129, 350-361. [CrossRef] 41. Mills, S.J.; Castro, M.P.G.; Li, Z.; Cai, J.; Hayward, R.; Mejias, L.; Walker, R.A. Evaluation of aerial remote sensing techniques for vegetation management in power-line corridors. IEEE Trans. Geosci. Remote Sens.

2010, 48, 3379-3390. [CrossRef] 42. Buduma, N.; Locascio, N. Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms, 1st ed.; O'Reilly Media, Inc.: Sebastopol, CA, USA, 2017; ISBN 1491925612.

43. Géron, A. Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 1st ed.; O'Reilly Media, Inc.: Sebastopol, CA, USA, 2017; ISBN 1491962291.

44. Goodfellow, I.; Bengio, Y.; Courville, A.; Bengio, Y. Deep Learning; MIT Press: Cambridge, MA, USA, 2016; ISBN 0262035618.

25 references, page 1 of 2
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