Cited by 196Solar Radiation and PhotovoltaicsRemote-Sensing Image ClassificationPhotovoltaic Systems and Sustainability

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Automatic detection of solar photovoltaic arrays in high resolution aerial imagery

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Malof, Jordan M., Bradbury, Kyle, Collins, Leslie M., Newell, Richard G.·2016·Applied Energy

DOI 10.1016/j.apenergy.2016.08.191

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Random Forests
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Classification and Regression Trees.
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Robust Real-Time Face Detection
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Classification and regression trees
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Pattern Recognition and Machine Learning
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“The dataset has since been used to create object detection models to identify solar PV panels with varying degrees of success [96],”
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“31,32 To balance the computational cost and detection performance of the RF method, we set the number of trees in the forest to 100 and the maximum depth of the forest to 20.”
Method
Enhancing visual feature constraints in segmentation models for photovoltaic panel recognition
Zhao, Z., Li, K., Chen, Y., & Wang, J. (2025) · Energy and AI · doi:10.1016/j.egyai.2025.100544 · Cited by 5
Diversifying training data does not improve generalizability of neural network models for PV identification
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Zhang, J., Jia, X., Zhou, J. W., Zhang, J., & Hu, J. (2023) · IEEE Transactions on Image Processing · doi:10.1109/tip.2023.3336170 · Cited by 5
“Pioneer works [3], [4], [5], [28] draw inspirations from classical object detection algorithms, which first generate hand-made features and then utilize classifiers including support vector machine and random forest to implement classification.”
Background
Detection of Photovoltaic Solar Panels With Longwave-Infrared Spectral Imaging
Tratt, D. M., Buckland, K. N., & Keim, E. R. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3416113 · Cited by 5
“The advent of high spatial resolution hyperspectral [6] and even multispectral [2], [16] VSWIR satellite imagery holds promise for progress in this area.”
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DSFA-SwinNet: A Multi-Scale Attention Fusion Network for Photovoltaic Areas Detection
Lin, S., Yang, Y., Liu, X., & Tian, L. (2025) · Remote Sensing · doi:10.3390/rs17020332 · Cited by 5
“Malof et al. are the ﬁ rst to deploy automated remote sensing to assess distributed solar PV [17].”
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Giussani, F., Wilczynski, E., Callegher, C. Z., Nogare, G. D., Pozza, C., Novelli, A., & Pezzutto, S. (2024) · Sustainability · doi:10.3390/su16052020 · Cited by 5
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Trading spatial resolution for improved accuracy when using detection algorithms on remote sensing imagery
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“Detection of solar panels from aerial imagery is often limited to satellite images [27], [28], [29], [30] and analysis of solar farms with the aid of thermal cameras [31].”
Background
Weakly Supervised Solar Panel Mapping Using Residual Aggregated Network for Aerial Images
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“The most popular input data for the detection model is high-resolution aerial images or high-resolution satellite images [5,6,7,8,9,10].”
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Automated Change Detection Methods for Satellite Data that can Improve Conservation ImplementationPreprint
Evans, M. J., & Malcom, J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/611459 · Cited by 4
“Object oriented, or computer vision-based approaches may be helpful for distinguishing among these (Malof et al. 2016; Ghorbanzadeh et al. 2019).”
Background
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“For example, A CNN-based solar PV panel identiﬁcation method is proposed in [18], however the proposed method still exhibits relatively low accuracy and recall due to its simple network structure.”
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“3 meters per pixel) has also been tested [2].”
Method
Polygon-based mapping of photovoltaic systems and estimation of energy generation potential
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“After that, they achieved the extraction of the PV systems contour by the random forest algorithm conﬁdence map [4].”
Method
Investigating the viability of detecting rooftop solar photovoltaics via convolutional neural networks using remote sensing images
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“Many articles, however, lacked complete datasets that included images and labels [2].”
BackgroundMethod
Discriminative features based comprehensive detector for defective insulators
Li, Y., Zhu, X., Li, B., Zeng, J., & Wang, S. (2024) · Energy and AI · doi:10.1016/j.egyai.2024.100387 · Cited by 2
Machine learning enables global solar-panel detection
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A deep learning approach for detecting distributed generation in residential customers
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Back in time: A novel time series and deep learning framework for mapping solar installations
Cullerton, M., Zhu, Z., Qiu, S., Rittenhouse, C. D., & Suh, J. W. (2025) · Science of Remote Sensing · doi:10.1016/j.srs.2025.100322 · Cited by 1
Deeplab V3+ based segmentation method for PV panels with aerial orthoimages
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Emerging Technology for Distributed Energy Resources
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Trading spatial resolution for improved accuracy in remote sensing imagery: an empirical study using synthetic data
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“Object detection in aerial or satellite imagery has been studied extensively, with examples that include the detection of buildings [1], roads [2], vehicles [3] and solar photovoltaic panels [4] in color or hyperspectral imagery.”
BackgroundSelf-citation
An Approach to Monitoring Solar Farms in Vietnam Using GEE and Satellite Imagery
Nguyen, D. A., Dinh, B. N., & Le, H. A. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-15063-0_25 · Cited by 1
Integrated Solar Panel Detection and Energy Production Estimation Using Unet and High-Resolution ImageryPreprint
Bouaziz, M. C., Koundi, M. E., & Ennine, G. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4827678 · Cited by 1
Geometry-Based Automated Recognition of Objects on Satellite Images of Sub-meter Resolution
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Comment on essd-2022-16
(2022) · Earth system science data · doi:10.5194/essd-2022-16-rc1
Comment on essd-2022-16
(2022) · Earth system science data · doi:10.5194/essd-2022-16-rc2
Reply on RC2
Zhang, X. (2022) · Earth system science data · doi:10.5194/essd-2022-16-ac2
Reply on RC1
Zhang, X. (2022) · Earth system science data · doi:10.5194/essd-2022-16-ac1
Model-Free Detection of Distributed Solar Generation in Distribution Grids Based on Minimal Exogenous Information
Petrusev, A., Rigo‐Mariani, R., Debusschere, V., Reignier, P., & Hadjsaïd, N. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-3-031-55696-8_14
Identiftcation Method of Photovoltaic Roofs Based on High Resolution Images
Zhu, P., Yang, X., Shen, K., Wu, X., Fan, H., & Tian, X. (2022) · IEEE International Electrical and Energy Conference (CIEEC) · doi:10.1109/cieec54735.2022.9845942
Learning Uncertainty-Aware Label Transition for Weakly Supervised Solar Panel Mapping with Aerial Images
Zhang, J., Jia, X., Zhou, J. W., & Hu, J. (2022) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss46834.2022.9884698
“Classifiers adopted in the early works include support vector machines [2] and random forests [3].”
Method
A Classification and Recognition Model of Distributed Resources Based on Feature Extraction
Zhu, Y., Yang, L., Yang, X., Lu, J., Wang, Y., & Wang, F. (2023) · IEEE International Conference on Energy Technologies for Future Grids (ETFG) · doi:10.1109/etfg55873.2023.10407147
“In [15], a DPVS identification method based on aerial images is proposed, which combines the labeling results with the household load data for DPVS identification.”
Method
Extraction of Solar Photovoltaic Panels Based on High-Resolution Remote Sensing Images
Ru, S., Wang, N., Shen, Y., Huang, S., Yu, C., Jia, P., Wang, H., Zeng, Z., Chen, Z., & Yu, J. (2023) · International Conference on Geoscience and Remote Sensing Mapping (GRSM) · doi:10.1109/grsm60169.2023.10425362
“Random forest methods [1, 2] are commonly used with machine learning methods for extracting solar photovoltaic panels.”
Method
Deeppvmap: Deep Photovoltaic Map for Efficient Segmentation of Solar Panels from Low-Resolution Aerial ImageryPreprint
Wang, Y.-H., Bo-Xiang, Y. C., Liao, H.-M., Chang, M., & Richard, T.-H. T. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4779346
Transfer Learning and Convolutional Neural Networks for Solar Photovoltaic Systems Detection in Distributed Power GridsPreprint
Chou, Y.-H., Faria, V., & de Queiroz, A. R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5174461
Solar Panel Segmentation on Aerial Images using Color and Elevation Information
Luimstra, G., & Bunte, K. (2025) · European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN) · doi:10.14428/esann/2025.es2025-81
Generation of Rooftop Solar Energy Potential Map Using Machine Learning and Deep Learning Based Building Footprint Extraction
Mundargi, Z., Pandit, M., Kshirsagar, S., Kulkarni, M., Kulkarni, S., & Korhalkar, T. (2025) · International Conference on Computing for Sustainability and Intelligent Future (COMP-SIF) · doi:10.1109/comp-sif65618.2025.10969963
SolarFormer++: Multi-scale Transformer for Solar PV Profiling and Obstruction Localization for Degradation Mitigation
Durán, E., Tran, M. Q., Massey, M., Gracia, A., Hanyu, T., Tran, A. T., McCann, R., Liao, H., Cothren, J. D., Adkins, M., et al. (2025) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2025.3574675
“In recent years, high-ﬁdelity solar mapping has gained signiﬁcant importance due to the widespread adoption of photovoltaic (PV) energy, improved aerial imagery resolution, and advancements in deep learning (DL) techniques [17], [18].”
Optimizing PV Panel Segmentation in Complex Environments Using Pre-Training and Simulated Annealing Algorithm: The JSWPVI
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Authors: Malof, J. M., Bradbury, K., Collins, L. M., & Newell, R. G.
Affiliations: Duke University (US)
Corresponding author: Malof, Jordan M.
ORCID: Malof, Jordan M.
Bradbury, Kyle
Collins, Leslie M.
Newell, Richard G.
Venue: Applied Energy
Publisher: Elsevier BV
Published: 2016-12
Volume: 183
Pages: 229-240
ISSN: 0306-2619 (ISSN-L)
DOI: 10.1016/j.apenergy.2016.08.191
OpenAlex: W2494424066
Cited by: 196
References: 76
Citations by year: 2026: 17 · 2025: 29 · 2024: 23 · 2023: 32 · 2022: 28 · 2021: 23 · 2020: 19 · 2019: 8 · 2018: 8 · 2017: 7 · 2016: 2
FWCI: 6.86
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Applied Energy (publishedVersion)
arXiv (Cornell University) (submittedVersion)
RePEc: Research Papers in Economics (submittedVersion)
arXiv (Cornell University)
Funders: Wells Fargo
Alfred P. Sloan Foundation
Topics: Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Solar Radiation and Photovoltaics
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Photovoltaic Systems and Sustainability
Keywords: solar photovoltaic array, very high resolution satellite imagery, aerial imagery, automated detection algorithm, solar energy mapping, solar resource assessment

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