Automated building energy consumption estimation from aerial imagery

Artem Streltsov; Kyle Bradbury; Jordan Malof

doi:10.1109/IGARSS.2018.8517624

Automated building energy consumption estimation from aerial imagery

Artem Streltsov
, Kyle Bradbury
, Jordan Malof

Computer Science

Duke University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Scopus citations

Abstract

This paper presents a methodology for automatically estimating the energy consumption of buildings from aerial imagery using data from Gainesville, Florida. By detecting buildings in the imagery using convolutional neural networks and extracting features from those building annotations, we use only imagery-derived features to estimate building energy consumption using random forests regression. For individual buildings, we achieve a predictive R ² value of 0.26, and with spatial aggregation over an area of 400m×400m our predictive R ² value increases to 0.95. We also explore the sensitivity of these estimates to errors in the building estimation process. Our results indicate that information limited to the size and shape of buildings, provides substantial predictive potential for the energy consumption of buildings.

Original language	English
Title of host publication	2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1676-1679
Number of pages	4
ISBN (Electronic)	9781538671504
DOIs	https://doi.org/10.1109/IGARSS.2018.8517624
State	Published - Oct 31 2018
Event	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain Duration: Jul 22 2018 → Jul 27 2018

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2018-July

Conference

Conference	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Country/Territory	Spain
City	Valencia
Period	07/22/18 → 07/27/18

Funding

We would like to thank the NVIDIA Corporation, for donating the graphics processing unit (GPU) for this work.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Aerial imagery
Building detection
Energy consumption
Machine learning

Access to Document

10.1109/IGARSS.2018.8517624

Cite this

Streltsov, A., Bradbury, K., & Malof, J. (2018). Automated building energy consumption estimation from aerial imagery. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings (pp. 1676-1679). Article 8517624 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2018.8517624

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title = "Automated building energy consumption estimation from aerial imagery",

abstract = " This paper presents a methodology for automatically estimating the energy consumption of buildings from aerial imagery using data from Gainesville, Florida. By detecting buildings in the imagery using convolutional neural networks and extracting features from those building annotations, we use only imagery-derived features to estimate building energy consumption using random forests regression. For individual buildings, we achieve a predictive R 2 value of 0.26, and with spatial aggregation over an area of 400m×400m our predictive R 2 value increases to 0.95. We also explore the sensitivity of these estimates to errors in the building estimation process. Our results indicate that information limited to the size and shape of buildings, provides substantial predictive potential for the energy consumption of buildings.",

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author = "Artem Streltsov and Kyle Bradbury and Jordan Malof",

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Streltsov, A, Bradbury, K & Malof, J 2018, Automated building energy consumption estimation from aerial imagery. in 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings., 8517624, International Geoscience and Remote Sensing Symposium (IGARSS), vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., pp. 1676-1679, 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, 07/22/18. https://doi.org/10.1109/IGARSS.2018.8517624

Automated building energy consumption estimation from aerial imagery. / Streltsov, Artem; Bradbury, Kyle; Malof, Jordan.
2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1676-1679 8517624 (International Geoscience and Remote Sensing Symposium (IGARSS); Vol. 2018-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PY - 2018/10/31

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N2 - This paper presents a methodology for automatically estimating the energy consumption of buildings from aerial imagery using data from Gainesville, Florida. By detecting buildings in the imagery using convolutional neural networks and extracting features from those building annotations, we use only imagery-derived features to estimate building energy consumption using random forests regression. For individual buildings, we achieve a predictive R 2 value of 0.26, and with spatial aggregation over an area of 400m×400m our predictive R 2 value increases to 0.95. We also explore the sensitivity of these estimates to errors in the building estimation process. Our results indicate that information limited to the size and shape of buildings, provides substantial predictive potential for the energy consumption of buildings.

AB - This paper presents a methodology for automatically estimating the energy consumption of buildings from aerial imagery using data from Gainesville, Florida. By detecting buildings in the imagery using convolutional neural networks and extracting features from those building annotations, we use only imagery-derived features to estimate building energy consumption using random forests regression. For individual buildings, we achieve a predictive R 2 value of 0.26, and with spatial aggregation over an area of 400m×400m our predictive R 2 value increases to 0.95. We also explore the sensitivity of these estimates to errors in the building estimation process. Our results indicate that information limited to the size and shape of buildings, provides substantial predictive potential for the energy consumption of buildings.

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KW - Building detection

KW - Energy consumption

KW - Machine learning

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M3 - Conference contribution

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BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings

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ER -

Streltsov A, Bradbury K, Malof J. Automated building energy consumption estimation from aerial imagery. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1676-1679. 8517624. (International Geoscience and Remote Sensing Symposium (IGARSS)). doi: 10.1109/IGARSS.2018.8517624

Automated building energy consumption estimation from aerial imagery

Abstract

Publication series

Conference

Funding

UN SDGs

Keywords

Access to Document

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