Kyle Bradbury
Assistant Research Professor in the Department of Electrical and Computer Engineering
Kyle Bradbury is the Managing Director of the Energy Data Analytics Lab at the Duke University Energy Initiative. He brings experience in machine learning and statistical modeling to energy problems. He completed his Ph.D. at Duke University, with research focused on modeling the reliability and cost trade-offs of energy storage systems for integrating wind and solar power into the grid. Kyle holds a M.S. in Electrical Engineering from Duke University where he specialized in statistical signal processing and machine learning, and a B.S. in Electrical Engineering from Tufts University. He has worked for ISO New England, MIT Lincoln Laboratories, and Dominion.
Current Appointments & Affiliations
- Assistant Research Professor in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2020
- Managing Director, Energy Data Analytics Lab of the Duke University Energy Initiative, Nicholas Institute-Energy Initiative, Initiatives 2020
- Faculty Fellow in The Energy Initiative, Nicholas Institute-Energy Initiative, Initiatives 2020
Contact Information
- 140 Science Drive (Gross Hall), Box 90467, Durham, NC 27708
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kyle.bradbury@duke.edu
(919) 613-2411
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LinkedIn
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Professional Homepage
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ResearchGate
- Background
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Education, Training, & Certifications
- Ph.D., Duke University 2013
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Duke Appointment History
- Lecturing fellow in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2016 - 2020
- Lecturing Fellow in the Duke University Energy Initiative, Nicholas Institute-Energy Initiative, Initiatives 2018 - 2020
- Lecturing Fellow in the Duke University Energy Initiative, Nicholas Institute-Energy Initiative, Initiatives 2016 - 2018
- Lecturing Fellow in the Department of Electrical and Computer Engineering, Electrical and Computer Engineering, Pratt School of Engineering 2016 - 2018
- Expertise
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Subject Headings
- Research
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Selected Grants
- Estimating solar PV locations and capacity in San Diego, CA awarded by Ernest Orlando Lawrence Berkeley National Laboratory 2019 - 2021
- NSF - IGE: Enhancing Data Skills and Professional Readiness through Vertically-Integrated Interdisciplinary Data Science Capstone Projects awarded by National Science Foundation 2018 - 2021
- RAISE: C-Accel Pilot - Track A1: Open Knowledge Network for the Global Energy Data Commons awarded by National Science Foundation 2019 - 2021
- Solar array identification in Adelaide, Australia awarded by Australia Energy Market Operator 2018
- Energy Data Analytics Lab awarded by Alfred P. Sloan Foundation 2015 - 2017
- Publications & Artistic Works
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Selected Publications
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Academic Articles
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Streltsov, A., J. M. Malof, B. Huang, and K. Bradbury. “Estimating residential building energy consumption using overhead imagery.” Applied Energy 280 (December 15, 2020). https://doi.org/10.1016/j.apenergy.2020.116018.Full Text
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Bradbury, K., R. Saboo, T. L. Johnson, J. M. Malof, A. Devarajan, W. Zhang, L. M. Collins, and R. G. Newell. “Distributed solar photovoltaic array location and extent dataset for remote sensing object identification.” Scientific Data 3 (December 6, 2016). https://doi.org/10.1038/sdata.2016.106.Full Text
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Malof, J. M., K. Bradbury, L. M. Collins, and R. G. Newell. “Automatic detection of solar photovoltaic arrays in high resolution aerial imagery.” Applied Energy 183 (December 1, 2016): 229–40. https://doi.org/10.1016/j.apenergy.2016.08.191.Full Text Open Access Copy
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Conference Papers
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Hu, W., B. Alexander, W. Cathcart, A. Hu, V. Nair, L. Zuo, J. Malof, L. Collins, and K. Bradbury. “Mapping Electric Transmission Line Infrastructure from Aerial Imagery with Deep Learning.” In International Geoscience and Remote Sensing Symposium (Igarss), 2229–32, 2020. https://doi.org/10.1109/IGARSS39084.2020.9323851.Full Text
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Huang, B., K. Bradbury, L. M. Collins, and J. M. Malof. “Do Deep Learning Models Generalize to Overhead Imagery from Novel Geographic Domains? the xGD Benchmark Problem.” In International Geoscience and Remote Sensing Symposium (Igarss), 1476–79, 2020. https://doi.org/10.1109/IGARSS39084.2020.9323080.Full Text
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Nair, V., P. Rhee, J. Yang, B. Huang, K. Bradbury, and J. M. Malof. “Designing Synthetic Overhead Imagery to Match a Target Geographic Region: Preliminary Results Training Deep Learning Models.” In International Geoscience and Remote Sensing Symposium (Igarss), 948–51, 2020. https://doi.org/10.1109/IGARSS39084.2020.9324180.Full Text
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Kong, F., B. Huang, K. Bradbury, and J. M. Malof. “The synthinel-1 dataset: A collection of high resolution synthetic overhead imagery for building segmentation.” In Proceedings 2020 Ieee Winter Conference on Applications of Computer Vision, Wacv 2020, 1803–12, 2020. https://doi.org/10.1109/WACV45572.2020.9093339.Full Text
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Kong, F., C. Chen, B. Huang, L. M. Collins, K. Bradbury, and J. M. Malof. “Training a single multi-class convolutional segmentation network using multiple datasets with heterogeneous labels: Preliminary results.” In International Geoscience and Remote Sensing Symposium (Igarss), 3903–6, 2019. https://doi.org/10.1109/IGARSS.2019.8898617.Full Text
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Lin, K., B. Huang, L. M. Collins, K. Bradbury, and J. M. Malof. “A simple rotational equivariance loss for generic convolutional segmentation networks: Preliminary results.” In International Geoscience and Remote Sensing Symposium (Igarss), 3876–79, 2019. https://doi.org/10.1109/IGARSS.2019.8898722.Full Text
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Huang, B., D. Reichman, L. M. Collins, K. Bradbury, and J. M. Malof. “On the extraction of training imagery from very large remote sensing datasets for deep convolutional segmenatation networks.” In International Geoscience and Remote Sensing Symposium (Igarss), 2018-July:6895–98, 2018. https://doi.org/10.1109/IGARSS.2018.8519523.Full Text
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Huang, B., K. Lu, N. Audebert, A. Khalel, Y. Tarabalka, J. Malof, A. Boulch, et al. “Large-scale semantic classification: Outcome of the first year of inria aerial image labeling benchmark.” In International Geoscience and Remote Sensing Symposium (Igarss), 2018-July:6947–50, 2018. https://doi.org/10.1109/IGARSS.2018.8518525.Full Text
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Huang, B., L. M. Collins, K. Bradbury, and J. M. Malof. “Deep convolutional segmentation of remote sensing imagery: A simple and efficient alternative to stitching output labels.” In International Geoscience and Remote Sensing Symposium (Igarss), 2018-July:6899–6902, 2018. https://doi.org/10.1109/IGARSS.2018.8518701.Full Text
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Streltsov, A., K. Bradbury, and J. Malof. “Automated building energy consumption estimation from aerial imagery.” In International Geoscience and Remote Sensing Symposium (Igarss), 2018-July:1676–79, 2018. https://doi.org/10.1109/IGARSS.2018.8517624.Full Text
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Wang, R., L. M. Collins, K. Bradbury, and J. M. Malof. “Semisupervised adversarial discriminative domain adaptation, with application to remote sensing data.” In International Geoscience and Remote Sensing Symposium (Igarss), 2018-July:3611–14, 2018. https://doi.org/10.1109/IGARSS.2018.8518096.Full Text
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Malof, J. M., S. Chelikani, L. M. Collins, and K. Bradbury. “Trading spatial resolution for improved accuracy in remote sensing imagery: An empirical study using synthetic data.” In Proceedings Applied Imagery Pattern Recognition Workshop, Vol. 2017-October, 2018. https://doi.org/10.1109/AIPR.2017.8457961.Full Text
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Wang, R., J. Camilo, L. M. Collins, K. Bradbury, and J. M. Malof. “The poor generalization of deep convolutional networks to aerial imagery from new geographic locations: An empirical study with solar array detection.” In Proceedings Applied Imagery Pattern Recognition Workshop, Vol. 2017-October, 2018. https://doi.org/10.1109/AIPR.2017.8457965.Full Text
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Wang, R., J. Camilo, L. M. Collins, K. Bradbury, and J. M. Malof. “The poor generalization of deep convolutional networks to aerial imagery from new geographic locations: An empirical study with solar array detection.” In Proceedings Applied Imagery Pattern Recognition Workshop, Vol. 2017-October, 2018. https://doi.org/10.1109/AIPR.2017.8457960.Full Text
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Huang, B., M. Knox, K. Bradbury, L. M. Collins, and R. G. Newell. “Non-intrusive load monitoring system performance over a range of low frequency sampling rates.” In 2017 6th International Conference on Renewable Energy Research and Applications, Icrera 2017, 2017-January:505–9, 2017. https://doi.org/10.1109/ICRERA.2017.8191111.Full Text
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Malof, J. M., L. M. Collins, and K. Bradbury. “A deep convolutional neural network, with pre-training, for solar photovoltaic array detection in aerial imagery.” In International Geoscience and Remote Sensing Symposium (Igarss), 2017-July:874–77, 2017. https://doi.org/10.1109/IGARSS.2017.8127092.Full Text
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Qian, S., S. Chelikani, P. Wang, L. M. Collins, K. Bradbury, and J. M. Malof. “Trading spatial resolution for improved accuracy when using detection algorithms on remote sensing imagery.” In International Geoscience and Remote Sensing Symposium (Igarss), 2017-July:3716–19, 2017. https://doi.org/10.1109/IGARSS.2017.8127806.Full Text
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So, B., C. Nezin, V. Kaimal, S. Keene, L. Collins, K. Bradbury, and J. M. Malof. “Estimating the electricity generation capacity of solar photovoltaic arrays using only color aerial imagery.” In International Geoscience and Remote Sensing Symposium (Igarss), 2017-July:1603–6, 2017. https://doi.org/10.1109/IGARSS.2017.8127279.Full Text
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Malof, J. M., K. Bradbury, L. M. Collins, R. G. Newell, A. Serrano, H. Wu, and S. Keene. “Image features for pixel-wise detection of solar photovoltaic arrays in aerial imagery using a random forest classifier.” In 2016 Ieee International Conference on Renewable Energy Research and Applications, Icrera 2016, 799–803, 2017. https://doi.org/10.1109/ICRERA.2016.7884446.Full Text
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Czarnek, N., K. Morton, L. Collins, R. Newell, and K. Bradbury. “Performance comparison framework for energy disaggregation systems.” In 2015 Ieee International Conference on Smart Grid Communications, Smartgridcomm 2015, 446–52, 2016. https://doi.org/10.1109/SmartGridComm.2015.7436341.Full Text
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Malof, J. M., L. M. Collins, K. Bradbury, and R. G. Newell. “A deep convolutional neural network and a random forest classifier for solar photovoltaic array detection in aerial imagery.” In 2016 Ieee International Conference on Renewable Energy Research and Applications, Icrera 2016, 650–54, 2016. https://doi.org/10.1109/ICRERA.2016.7884415.Full Text
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Malof, J. M., R. Hou, L. M. Collins, K. Bradbury, and R. Newell. “Automatic solar photovoltaic panel detection in satellite imagery.” In 2015 International Conference on Renewable Energy Research and Applications, Icrera 2015, 1428–31, 2015. https://doi.org/10.1109/ICRERA.2015.7418643.Full Text
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Bradbury, K. J., and J. P. Noonan. “Covert binary communications through the application of chaos theory: Three novel approaches.” In Citsa 2007 Int. Conference on Cybernetics and Information Technologies, Systems and Applications and Ccct 2007 Int. Conference on Computing, Communications and Control Technologies, Proceedings, 2:60–65, 2007.
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Datasets
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Hu, Wei, Bohao Huang, Kyle Bradbury, Jordan Malof, Varun Nair, Tamasha Pathirathna, Xiaolan You, et al. “Electric Transmission Infrastructure Satellite Imagery Dataset for Computer Vision,” July 21, 2021. https://doi.org/10.6084/m9.figshare.14935434.v2.Data Access Link to Item
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Bradbury, Kyle, Raghav Saboo, Jordan Malof, Timothy Johnson, Arjun Devarajan, Wuming Zhang, Leslie Collins, Richard Newell, Artem Streltsov, and Wei Hu. “Distributed Solar Photovoltaic Array Location and Extent Data Set for Remote Sensing Object Identification,” July 30, 2020. https://doi.org/10.6084/m9.figshare.3385780.Data Access Link to Item
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Bradbury, Kyle, Qiwei Han, Varun Nair, Tamasha Pathirathna, and Xiaolan You. “Electric Transmission and Distribution Infrastructure Imagery Dataset,” August 3, 2018. https://doi.org/10.6084/m9.figshare.6931088.v1.Data Access Link to Item
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Bradbury, Kyle, Boning Li, Benjamin Brigman, Gouttham Chandrasekar, Shamikh Hossain, Trishul Nagenalli, Leslie Collins, Timothy Johnson, and Marc Jeuland. “Indian Village Satellite Imagery and Energy Access Dataset,” November 7, 2017. https://doi.org/10.6084/m9.figshare.5552743.Data Access Link to Item
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Bradbury, Kyle, Benjamin Brigman, Gouttham Chandrasekar, Leslie Collins, Shamikh Hossain, Marc Jeuland, Timothy Johnson, Boning Li, and Trishul Nagenalli. “Power Plant Satellite Imagery Dataset,” August 16, 2017. https://doi.org/10.6084/m9.figshare.5307364.v1.Data Access Link to Item
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Bradbury, Kyle, Benjamin Brigman, Leslie Collins, Timothy Johnson, Sebastian Lin, Richard Newell, Sophia Park, Sunith Suresh, Hoel Wiesner, and Yue Xi. “San Francisco, California - Aerial imagery object identification dataset for building and road detection, and building height estimation,” July 29, 2016. https://doi.org/10.6084/m9.figshare.3504350.v1.Data Access Link to Item
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Bradbury, Kyle, Benjamin Brigman, Leslie Collins, Timothy Johnson, Sebastian Lin, Richard Newell, Sophia Park, Sunith Suresh, Hoel Wiesner, and Yue Xi. “Seekonk, Massachusetts - Aerial imagery object identification dataset for building and road detection, and building height estimation,” July 29, 2016. https://doi.org/10.6084/m9.figshare.3504359.v1.Data Access Link to Item
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Bradbury, Kyle, Benjamin Brigman, Leslie Collins, Timothy Johnson, Sebastian Lin, Richard Newell, Sophia Park, Sunith Suresh, Hoel Wiesner, and Yue Xi. “Metadata - Aerial imagery object identification dataset for building and road detection, and building height estimation,” July 29, 2016. https://doi.org/10.6084/m9.figshare.3504413.v1.Data Access
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Bradbury, Kyle, Benjamin Brigman, Leslie Collins, Timothy Johnson, Sebastian Lin, Richard Newell, Sophia Park, Sunith Suresh, Hoel Wiesner, and Yue Xi. “Arlington, Massachusetts - Aerial imagery object identification dataset for building and road detection, and building height estimation,” July 29, 2016. https://doi.org/10.6084/m9.figshare.3485204.v1.Data Access Link to Item
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Bradbury, Kyle, Benjamin Brigman, Leslie Collins, Timothy Johnson, Sebastian Lin, Richard Newell, Sophia Park, Sunith Suresh, Hoel Wiesner, and Yue Xi. “Atlanta, Georgia - Aerial imagery object identification dataset for building and road detection, and building height estimation,” July 29, 2016. https://doi.org/10.6084/m9.figshare.3504308.v1.Data Access Link to Item
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Bradbury, Kyle, Benjamin Brigman, Leslie Collins, Timothy Johnson, Sebastian Lin, Richard Newell, Sophia Park, Sunith Suresh, Hoel Wiesner, and Yue Xi. “Austin, Texas - Aerial imagery object identification dataset for building and road detection, and building height estimation,” July 29, 2016. https://doi.org/10.6084/m9.figshare.3504317.v1.Data Access Link to Item
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Bradbury, Kyle, Benjamin Brigman, Leslie Collins, Timothy Johnson, Sebastian Lin, Richard Newell, Sophia Park, Sunith Suresh, Hoel Wiesner, and Yue Xi. “Washington, DC - Aerial imagery object identification dataset for building and road detection, and building height estimation,” July 29, 2016. https://doi.org/10.6084/m9.figshare.3504320.v1.Data Access Link to Item
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Bradbury, Kyle, Benjamin Brigman, Leslie Collins, Timothy Johnson, Sebastian Lin, Richard Newell, Sophia Park, Sunith Suresh, Hoel Wiesner, and Yue Xi. “New Haven, Connecticut - Aerial imagery object identification dataset for building and road detection, and building height estimation,” July 29, 2016. https://doi.org/10.6084/m9.figshare.3504323.v1.Data Access Link to Item
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Bradbury, Kyle, Benjamin Brigman, Leslie Collins, Timothy Johnson, Sebastian Lin, Richard Newell, Sophia Park, Sunith Suresh, Hoel Wiesner, and Yue Xi. “New York City, New York - Aerial imagery object identification dataset for building and road detection, and building height estimation,” July 29, 2016. https://doi.org/10.6084/m9.figshare.3504326.v1.Data Access Link to Item
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Bradbury, Kyle, Benjamin Brigman, Leslie Collins, Timothy Johnson, Sebastian Lin, Richard Newell, Sophia Park, Sunith Suresh, Hoel Wiesner, and Yue Xi. “Norfolk, Virginia - Aerial imagery object identification dataset for building and road detection, and building height estimation,” July 29, 2016. https://doi.org/10.6084/m9.figshare.3504347.v1.Data Access Link to Item
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Bradbury, Kyle, Raghav Saboo, Timothy Johnson, Jordan Malof, Arjun Devarajan, Wuming Zhang, Leslie Collins, and Richard Newell. “Stockton Aerial USGS Imagery from the Distributed Solar Photovoltaic Array Location and Extent Data Set,” May 27, 2016. https://doi.org/10.6084/m9.figshare.3385804.v1.Data Access Link to Item
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Bradbury, Kyle, Raghav Saboo, Timothy Johnson, Jordan Malof, Arjun Devarajan, Wuming Zhang, Leslie Collins, and Richard Newell. “Oxnard Aerial USGS Imagery from the Distributed Solar Photovoltaic Array Location and Extent Data Set,” May 27, 2016. https://doi.org/10.6084/m9.figshare.3385807.v1.Data Access Link to Item
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Bradbury, Kyle, Raghav Saboo, Timothy Johnson, Jordan Malof, Arjun Devarajan, Wuming Zhang, Leslie Collins, and Richard Newell. “Fresno Aerial USGS Imagery from the Distributed Solar Photovoltaic Array Location and Extent Data Set,” May 27, 2016. https://doi.org/10.6084/m9.figshare.3385828.v1.Data Access Link to Item
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Bradbury, Kyle, Raghav Saboo, Timothy Johnson, Jordan Malof, Arjun Devarajan, Wuming Zhang, Leslie Collins, and Richard Newell. “Modesto Aerial USGS Imagery from the Distributed Solar Photovoltaic Array Location and Extent Data Set,” May 27, 2016. https://doi.org/10.6084/m9.figshare.3385789.v1.Data Access Link to Item
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- Teaching & Mentoring
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Recent Courses
- ENERGY 395T: Bass Connections Energy & Environment Research Team 2021
- ENERGY 396: Connections in Energy: Interdisciplinary Team Projects 2021
- ENERGY 795T: Bass Connections Energy & Environment Research Team 2021
- ENERGY 796: Connections in Energy: Interdisciplinary Team Projects 2021
- IDS 705: Principles of Machine Learning 2021
- ENERGY 395: Connections in Energy: Interdisciplinary Team Projects 2020
- ENERGY 396: Connections in Energy: Interdisciplinary Team Projects 2020
- ENERGY 795: Connections in Energy: Interdisciplinary Team Projects 2020
- ENERGY 796: Connections in Energy: Interdisciplinary Team Projects 2020
- IDS 705: Principles of Machine Learning 2020
- ECE 391: Projects in Electrical and Computer Engineering 2019
- ENERGY 395: Connections in Energy: Interdisciplinary Team Projects 2019
- ENERGY 396: Connections in Energy: Interdisciplinary Team Projects 2019
- ENERGY 795: Connections in Energy: Interdisciplinary Team Projects 2019
- ENERGY 796: Connections in Energy: Interdisciplinary Team Projects 2019
- IDS 705: Principles of Machine Learning 2019
- Scholarly, Clinical, & Service Activities
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Outreach & Engaged Scholarship
- Bass Connections Team Leader. Deep Learning for Rare Energy Infrastructure in Satellite Imagery. 2020 - 2021 2020 - 2021
- Data+ Project Leader. Data+. 2020 2020
- Bass Connections Team Leader. A Wider Lens on Energy: Adapting Deep Learning Techniques to Inform Energy Access Decisions. 2019 - 2020 2019 - 2020
- Data+ Project Leader. Data+. 2019 2019
- Bass Connections Faculty Team Member . Visualizing Energy through Live Performance . August 2017 - May 2018 2017 - 2018
- Bass Connections Faculty Team Member. Energy Data Analytics Lab: Electricity Access in Developing Countries from Aerial Imagery . August 2017 - May 2018 2017 - 2018
- Bass Connections Faculty Team Member . Developing Departmental Energy Reports and a Carbon Pricing Program for Duke University. August 2016 - May 2017 2016 - 2017
- Bass Connections Faculty Team Member. Data+. June 2016 - July 2016 2016
- Bass Connections Faculty Team Member . Resident Engagement and Energy Behavior Assessment through Mobile Phone Technology . August 2015 - May 2016 2015 - 2016
- Bass Connections Faculty Team Member. Energy Data Analytics Lab. August 2015 - May 2016 2015 - 2016
- Bass Connections Faculty Team Member. The University as an Energy Laboratory: Design and Implementation of an Energy Disaggregation System. August 2013 - July 2014 2013 - 2014
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