Automatic detection of solar photovoltaic arrays in high resolution aerial imagery
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Since the locations and sizes of rooftop solar panels are often unknown to electricity system operators, previous work has used computer vision techniques on satellite imagery to generate size and location data for rooftop solar panels.
“Since the locations and sizes of rooftop solar panels are often unknown to electricity system operators, previous work has used computer vision techniques on satellite imagery to generate size and location data for rooftop solar panels [165,514,868].”
From Tackling climate change with machine learning (2023) · cites this paper as [514]
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“Here, we focus on buildings and the corresponding roof shapes that are important for instance for solar panels [7] and 5G deployment [8] (see Section 2.”
BackgroundRemote Sensing for Monitoring Photovoltaic Solar Plants in Brazil Using Deep Semantic Segmentation
da Costa, M. V. C. V., de Carvalho, O. L. F., Orlandi, A. G., Hirata, I., de Albuquerque, A. O., Silva, F. V. E., Guimarães, R. F., Gomes, R. A. T., & Júnior, O. A. D. C. (2021) · Energies · doi:10.3390/en14102960 · Cited by 89 · see citing works
“ing images, such as solar energy estimates [53–56], solar power plant site selection [57–62], PV potential on building rooftops [63–66], and area estimation [67,68].”
BackgroundA review of behind-the-meter solar forecasting
Erdener, B. C., Feng, C., Doubleday, K., Florita, A. R., & Hodge, B. S. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112224 · Cited by 90 · see citing works
Liu, X., Liu, X., Jiang, Y., Zhang, T., & Hao, B. (2023) · CSEE Journal of Power and Energy Systems · doi:10.17775/cseejpes.2022.04850 · Cited by 86 · see citing works
“The detection process applies deep learning algorithms at each step, including labeling aerial images with PV [44], [45], estimating their sizes [46], [47], estimating their 3D orientations using time-series data of power generation [48], [49].”
Wang, F., Li, K., Wang, X., Jiang, L., Ren, J., Mi, Z., Shafie‐khah, M., & Catalào, J. P. S. (2018) · Energies · doi:10.3390/en11071750 · Cited by 84 · see citing works
Ren, S., Luzi, F., Lahrichi, S., Kassaw, K., Collins, L. M., Bradbury, K., & Malof, J. M. (2024) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv57701.2024.00817 · Cited by 88 · see citing works
“Our work represents the first comprehensive evaluation of SAM on overhead imagery (e.g., including a variety of target classes, resolutions, and geographic locations), and the first to include both model composition and interactive annotation scenarios.”
Self-citationMalof, J. M., Collins, L. M., & Bradbury, K. (2017) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2017.8127092 · Cited by 81 · see citing works
“, city-level instead of state-level) [1], [2], and with greater scalability since the annotations are computed automatically rather than manually.”
BackgroundMethodInfluentialSelf-citationMüller, J., & Trutnevyte, E. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.114747 · Cited by 74 · see citing works
Feng, C., Liu, Y., & Zhang, J. (2021) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2021.107176 · Cited by 73 · see citing works
“Then, the pixel means and variances of different sizes of windows were used as input to RF models for PV pixel detection in [206].”
MethodKasmi, G., Saint‐Drenan, Y., Trebosc, D., Jolivet, R., Leloux, J., Sarr, B., & Dubus, L. (2023) · Scientific Data · doi:10.1038/s41597-023-01951-4 · Cited by 71 · see citing works
“Remote sensing-based methods 13,15–17 recently emerged as a promising solution to quickly and cheaply acquire detailed information on PV installations 18 .”
BackgroundLi, Z., Zhang, Z., & Wang, L. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106283 · Cited by 70 · see citing works
Enhancing PV panel segmentation in remote sensing images with constraint refinement modules
Tan, H., Guo, Z., Zhang, H., Chen, Q., Lin, Z., Chen, Y., & Yan, J. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121757 · Cited by 67 · see citing works
Wang, M., Cui, Q., Sun, Y., & Wang, Q. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.04.010 · Cited by 66 · see citing works
Wang, J., Chen, X., Jiang, W., Li, H., Liu, J., & Sui, H. (2023) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2023.103309 · Cited by 64 · see citing works
Malof, J. M., Collins, L. M., Bradbury, K., & Newell, R. G. (2016) · International Conference on Renewable Energy Research and Applications (ICRERA) · doi:10.1109/icrera.2016.7884415 · Cited by 64 · see citing works
Self-citationJie, Y., Ji, X., Yue, A., Chen, J., Deng, Y., Chen, J., & Zhang, Y. (2020) · Energies · doi:10.3390/en13246742 · Cited by 67 · see citing works
“Malof [15] pioneered the use of manual features for extracting distributed photovoltaic power stations and proposed a method that first obtains all the maximally stable extreme regions (MSERs) [16] from an image and then filters out the areas with low confidence.”
MethodInfluentialThormeyer, C., Sasse, J.-P., & Trutnevyte, E. (2020) · Renewable Energy · doi:10.1016/j.renene.2019.06.017 · Cited by 61 · see citing works
Yagli, G. M., Yang, D., Gandhi, O., & Srinivasan, D. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.114122 · Cited by 62 · see citing works
Zheng, T., Bergin, M., Hu, S., Miller, J., & Carlson, D. (2020) · Atmospheric Environment · doi:10.1016/j.atmosenv.2020.117451 · Cited by 62 · see citing works
Effective solar prosumer identification using net smart meter data
Donaldson, D. L., & Jayaweera, D. (2020) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2020.105823 · Cited by 61 · see citing works
Mao, H., Xie, C., Luo, Y., Deng, J., Tian, Z., Yu, J., Xiao, Y., & Fan, J. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.113276 · Cited by 60 · see citing works
3D-PV-Locator: Large-scale detection of rooftop-mounted photovoltaic systems in 3D
Mayer, K., Rausch, B., Arlt, M.-L., Gust, G., Wang, Z., Neumann, D., & Rajagopal, R. (2022) · Applied Energy · doi:10.1016/j.apenergy.2021.118469 · Cited by 59 · see citing works
Zhang, X., Zeraatpisheh, M., Rahman, M. M., Wang, S., & Xu, M. (2021) · Remote Sensing · doi:10.3390/rs13193909 · Cited by 58 · see citing works
GIScience can facilitate the development of solar cities for energy transition
Zhu, R., Kwan, M., Perera, A. T. D., Fan, H., Yang, B., Chen, B. Y., Chen, M., Qian, Z., Zhang, H., Zhang, X., et al. (2023) · Advances in Applied Energy · doi:10.1016/j.adapen.2023.100129 · Cited by 58 · see citing works
“[57] Malof JM, Bradbury K, Collins LM, Newell RG.”
BackgroundMethodAn Artificial Intelligence Dataset for Solar Energy Locations in India
Ortiz, A., Negandhi, D., Mysorekar, S. R., Nagaraju, S. K., Kiesecker, J. M., Robinson, C., Bhatia, P., Khurana, A., Wang, J., Oviedo, F., et al. (2022) · Scientific Data · doi:10.1038/s41597-022-01499-9 · Cited by 57 · see citing works
Camilo, J. A., Wang, R., Collins, L. M., Bradbury, K., & Malof, J. M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.04018 · Cited by 52 · see citing works
“The data used in these splits is the same as the data used for PV algorithm evaluation in several previous studies [2], [5].”
BackgroundMethodInfluentialSelf-citationFischhendler, I., Herman, L., & David, L. (2021) · New Political Economy · doi:10.1080/13563467.2021.1903850 · Cited by 50 · see citing works
“Malof et al. (2016) and Bradbury et al. (2016) used solar panels identification as a replacement for household surveys; once the machine-learning algorithm can identify solar panels, the algorithm can learn how to identify solar panels also unfamiliar area.”
BackgroundKaroui, M. S., Benhalouche, F. Z., Deville, Y., Djerriri, K., Briottet, X., Houet, T., Bris, A. L., & Weber, C. (2019) · Remote Sensing · doi:10.3390/rs11182164 · Cited by 50 · see citing works
“For that reason, and to avoid spiteful frauds with these substitute energies, several organizations are becoming ever more attracted to detailed information concerning these solar systems, as well as their localization and energy production [1].”
BackgroundAutomated Extraction of Energy Systems Information from Remotely Sensed Data: A Review and Analysis
Ren, S., Hu, W., Bradbury, K., Harrison‐Atlas, D., Valeri, L. M., Murray, B. C., & Malof, J. M. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119876 · Cited by 47 · see citing works
“Electricity Generation Infrastructure identification and characterization [4], [109] - [108], [110] [5], [97], [99], [101]– [103], [105]– [107], [253] - - [105], [111]”
Self-citationSolar photovoltaic module detection using laboratory and airborne imaging spectroscopy data
Ji, C., Bachmann, M., Esch, T., Feilhauer, H., Heiden, U., Heldens, W., Hueni, A., Lakes, T., Metz-Marconcini, A., Schroedter‐Homscheidt, M., et al. (2021) · Remote Sensing of Environment · doi:10.1016/j.rse.2021.112692 · Cited by 47 · see citing works
Impacts of agrisolar co-location on the food–energy–water nexus and economic security
Stid, J. T., Shukla, S., Kendall, A. D., Anctil, A., Hyndman, D. W., Rapp, J., & Anex, R. P. (2025) · Nature Sustainability · doi:10.1038/s41893-025-01546-4 · Cited by 44 · see citing works
DeepSolar for Germany: A deep learning framework for PV system mapping from aerial imagery
Mayer, K., Wang, Z., Arlt, M.-L., Neumann, D., & Rajagopal, R. (2020) · International Conference on Smart Energy Systems and Technologies (SEST) · doi:10.1109/sest48500.2020.9203258 · Cited by 43 · see citing works
“The first contributions in the field have applied support vector machines (SVMs) [9] and random forests [10].”
BackgroundGeoAI for detection of solar photovoltaic installations in the Netherlands
Kausika, B. B., Nijmeijer, D., Reimerink, I., Brouwer, P., & Liem, V. (2021) · Energy and AI · doi:10.1016/j.egyai.2021.100111 · Cited by 43 · see citing works
Malof, J. M., Bradbury, K., Collins, L. M., Newell, R. G., Serrano, A., Wu, H., & Keene, S. M. (2016) · International Conference on Renewable Energy Research and Applications (ICRERA) · doi:10.1109/icrera.2016.7884446 · Cited by 42 · see citing works
Self-citationDetection of Solar Photovoltaic Power Plants Using Satellite and Airborne Hyperspectral Imaging
Jörges, C., Vidal, H. S., Hank, T., & Bach, H. (2023) · Remote Sensing · doi:10.3390/rs15133403 · Cited by 34 · see citing works
de Hoog, J., Maetschke, S., Ilfrich, P., & Kolluri, R. R. (2020) · Proceedings of the Eleventh ACM International Conference on Future Energy Systems · doi:10.1145/3396851.3397681 · Cited by 32 · see citing works
“Approaches that attempt object detection have used standard feature extraction (using some form of machine learning) or convolutional neural networks (CNNs) [19, 21, 22, 28].”
MethodWang, J., Chen, X., Shi, W., Jiang, W., Zhang, X., Li, H., Liu, J., & Sui, H. (2023) · Remote Sensing · doi:10.3390/rs15215232 · Cited by 33 · see citing works
Tiling and Stitching Segmentation Output for Remote Sensing: Basic Challenges and RecommendationsPreprint
Huang, B., Reichman, D., Collins, L. M., Bradbury, K., & Malof, J. M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.12219 · Cited by 33 · see citing works
Self-citationHu, M., Ge, D., Telford, R., Stephen, B., & Wallom, D. C. H. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103380 · Cited by 31 · see citing works
Wang, R., Camilo, J. A., Collins, L. M., Bradbury, K., & Malof, J. M. (2017) · IEEE Applied Imagery Pattern Recognition Workshop · doi:10.1109/aipr.2017.8457960 · Cited by 31 · see citing works
“IMAGERY Automatic mapping of solar photovoltaic (PV) arrays in high resolution imagery was first investigated in [14] (on a smallscale dataset) and [13] (on a large scale dataset).”
BackgroundMethodSelf-citationA framework to estimate generating capacities of PV systems using satellite imagery segmentation
Jurakuziev, D., Jumaboev, S., & Lee, M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106186 · Cited by 29 · see citing works
Predicting PV Areas in Aerial Images with Deep Learning
Zech, M., & Ranalli, J. A. (2020) · Conference record of the IEEE Photovoltaic Specialists Conference · doi:10.1109/pvsc45281.2020.9300636 · Cited by 29 · see citing works
Haroun, F. M. E., Deros, S. N. M., & Din, N. M. (2021) · IEEE Access · doi:10.1109/access.2021.3106550 · Cited by 29 · see citing works
“[48] used random forest ML classifier to detect small-scale solar photovoltaic panels from highresolutions satellite images.”
MethodCapacity Estimation of Solar Farms Using Deep Learning on High-Resolution Satellite Imagery
Ravishankar, R., Almahmoud, E., Habib, A. H., & Weck, O. L. D. (2022) · Remote Sensing · doi:10.3390/rs15010210 · Cited by 29 · see citing works
Lodhi, M. K., Tan, Y., Wang, X., Masum, S. M., Khan, M. N., & Ullah, N. (2024) · Energy · doi:10.1016/j.energy.2024.132256 · Cited by 28 · see citing works
Wang, J., Liu, J., & Li, L. (2022) · Remote Sensing · doi:10.3390/rs14246296 · Cited by 27 · see citing works
“Conventional methods, including household surveys and utility interconnection filings, are limited in their completeness and spatial resolution in collecting the distribution of PV plants [8].”
MethodClark, C. N., & Pacifici, F. (2023) · Scientific Data · doi:10.1038/s41597-023-02539-8 · Cited by 27 · see citing works
Ren, S., Malof, J. M., Fetter, T. R., Beach, R., Rineer, J. I., & Bradbury, K. (2022) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11040222 · Cited by 25 · see citing works
“The earliest work on solar panel segmentation used traditional machine learning methods like support vector machines (SVM), decision trees and random forests, with manuallyengineered features like mean, variance, textons, and local color statistics of pixels [7,10,19].”
BackgroundSelf-citationSizing ramping reserve using probabilistic solar forecasts: A data-driven method
Li, B., Feng, C., Siebenschuh, C., Zhang, R., Spyrou, E., Krishnan, V. K., Hobbs, B. F., & Zhang, J. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.118812 · Cited by 24 · see citing works
“CNNs have been widely used in the power and energy domain, such as forecasting [54], infrastructure detection [55], and individual behavior understanding [56].”
MethodLu, N., Li, L., & Qin, J. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123311 · Cited by 23 · see citing works
What you get is not always what you see—pitfalls in solar array assessment using overhead imagery
Hu, W. G., Bradbury, K., Malof, J. M., Li, B., Huang, B., Streltsov, A., Fujita, K. S., & Hoen, B. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.120143 · Cited by 23 · see citing works
Self-citationEdun, A. S., Perry, K., Harley, J. B., & Deline, C. A. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117273 · Cited by 22 · see citing works
Automatic Detection of Photovoltaic Farms Using Satellite Imagery and Convolutional Neural Networks
Ιωάννου, Κ., & Myronidis, D. (2021) · Sustainability · doi:10.3390/su13095323 · Cited by 21 · see citing works
“[37], used high resolution satellite imagery and deep forest algorithm in order to detect roof top installed photovoltaic arrays.”
MethodAerial image analysis based on improved adaptive clustering for photovoltaic module inspection
Zhang, D., Wu, F., Li, X., Luo, X., Wang, J., Yan, W., Chen, Z., & Yang, Q. (2017) · IEEE International Smart Cities Conference (ISC2) · doi:10.1109/isc2.2017.8090798 · Cited by 21 · see citing works
“The solutions in [10, 17] adopted supervised Random Forest classifier to identify the locations, shapes, or sizes of PV array and achieve good result.”
MethodGergeľová, M. B., Labant, S., Kuzevič, Š., Kuzevičová, Ž., & Pavolová, H. (2020) · Sustainability · doi:10.3390/su12176847 · Cited by 21 · see citing works
“Aerial photogrammetry [16], high resolution aerial satellite images [17], interferometric synthetic aperture radar (InSAR) [18], and the LiDAR point cloud [19,20] can be used as inputs for object detection.”
MethodZheng, T., Bergin, M., Wang, G., & Carlson, D. (2021) · Remote Sensing · doi:10.3390/rs13071356 · Cited by 22 · see citing works
Mapping solar array location, size, and capacity using deep learning and overhead imageryPreprint
Malof, J. M., Li, B., Huang, B., Bradbury, K., & Stretslov, A. (2019) · arXiv (Cornell University) · Cited by 19 · see citing works
Self-citationGasparyan, H. A., Davtyan, T., & Agaian, S. С. (2024) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2024.3386402 · Cited by 18 · see citing works
Supporting habitat conservation with automated change detection in Google Earth Engine
Evans, M. J., & Malcom, J. (2020) · Conservation Biology · doi:10.1111/cobi.13680 · Cited by 18 · see citing works
“Object oriented or computer-vision-based approaches may be helpful for distinguishing among these (Malof et al. 2016; Ghorbanzadeh et al. 2019).”
BackgroundEnhance Unobservable Solar Generation Estimation via Constructive Generative Adversarial Networks
Yuan, J., & Weng, Y. (2023) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2023.3262773 · Cited by 17 · see citing works
“While the primitive information in distribution grids is limited, it canbeinferredfrom extrapublicdatasources that areeasy to access, e.g., satellite images of PVs. Previous efforts lead to (GIS),whichisanimageclassification task[24],[25],[26],[27].”
BackgroundDaxini, R., Wilson, R., & Wu, Y. (2023) · Energy · doi:10.1016/j.energy.2023.129046 · Cited by 17 · see citing works
So, B., Nezin, C., Kaimal, V., Keene, S. M., Collins, L. M., Bradbury, K., & Malof, J. M. (2017) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2017.8127279 · Cited by 17 · see citing works
“, neighborhood- or city-level instead of state- or national-level) [1], [2].”
BackgroundSelf-citationGridTracer: Automatic Mapping of Power Grids Using Deep Learning and Overhead Imagery
Huang, B., Yang, J., Streltsov, A., Bradbury, K., Collins, L. M., & Malof, J. M. (2021) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2021.3124519 · Cited by 16 · see citing works
“Recently deep learning models - namely deep neural networks (DNNs) - have been shown to be capable of accurately mapping a variety of objects in color overhead imagery, such as buildings [7], [8] roads [8], [9], and solar arrays [10], [11], [12].”
BackgroundSelf-citationLiu, W., Huo, H., Ji, L., Zhao, Y., Liu, X., & Li, J. (2023) · Remote Sensing · doi:10.3390/rs16010009 · Cited by 16 · see citing works
Li, L., Lu, N., & Qin, J. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124521 · Cited by 16 · see citing works
Lodhi, M. K., Tan, Y., Wang, X., Suprijanto, A., & Imran, M. C. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115717 · Cited by 16 · see citing works
Karoui, M. S., Benhalouche, F. Z., Deville, Y., Djerriri, K., Briottet, X., & Bris, A. L. (2018) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2018.8518204 · Cited by 16 · see citing works
“Therefore, and to prevent malicious frauds with these alternative energies, many organizations are increasingly interested in detailed information about these solar installations including their localization and energy production [1].”
BackgroundChen, D., Peng, Q., Lu, J., Huang, P., Song, Y., & Peng, F. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124296 · Cited by 15 · see citing works
Zhang, J., Jia, X., & Hu, J. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3123268 · Cited by 14 · see citing works
Perry, K., & Campos, C. (2022) · IEEE Journal of Photovoltaics · doi:10.1109/jphotov.2022.3230565 · Cited by 14 · see citing works
“Our previous efforts build upon a body of published research, which focuses on solar array detection in satellite imagery [6], [7], [8], as well as array characterization following detection [9], [10], [11].”
MethodAdvancing solar and wind penetration in China through energy complementarity
Hu, Y., Jiang, H., Zhang, C., Yuan, J., Zhang, M., Yao, L., Chen, Q., Wu, J., Zhang, H., Ma, S., et al. (2026) · Nature · doi:10.1038/s41586-026-10570-z · Cited by 13 · see citing works
“Although spatiotemporal complementarity between these resources is widely recognized as a pathway to enhance renewable integration and reduce balancing requirements 3–16 , existing assessments are largely based on hypothetical deployments 17–24 .”
Kasmi, G., Touron, A., Blanc, P., Saint‐Drenan, Y., Fortin, M., & Dubus, L. (2024) · Energies · doi:10.3390/en17174353 · Cited by 13 · see citing works
Jie, Y., Yue, A., Liu, S., Huang, Q., Chen, J., Meng, Y., Deng, Y., & Yu, Z. (2020) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.14.016506 · Cited by 13 · see citing works
“Random forest is utilized to extract the photovoltaic panels on the rooftops.(2) Random forest and convolutional neural network (CNN) are cascaded to extract the panels.”
MethodYin, H., & Zhou, K. (2022) · Utilities Policy · doi:10.1016/j.jup.2022.101378 · Cited by 13 · see citing works
Semantic Segmentation of Solar PV Panels and Wind Turbines in Satellite Images Using U-Net
Darapaneni, N., Jagannathan, A., Natarajan, V., Swaminathan, G. V., Subramanian, S., & Paduri, A. R. (2020) · IEEE International Conference on Industrial and Information Systems (ICIIS) · doi:10.1109/iciis51140.2020.9342701 · Cited by 11 · see citing works
Zhang, J., Jia, X., & Hu, J. (2020) · International Conference on Digital Image Computing: Techniques and Applications (DICTA) · doi:10.1109/dicta51227.2020.9363379 · Cited by 11 · see citing works
“For feature extraction, local color statistic (LCS) feature, texture feature and shape feature are frequently investigated, and support vector machine (SVM) [5], [7] and random forest (RF) [6] classifiers are popular choices in the classification phase.”
BackgroundMethodAdvanced machine learning applications to modern power systems
Feng, C., Sun, M., Dabbaghjamanesh, M., Liu, Y., & Zhang, J. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-820168-8.00007-9 · Cited by 12 · see citing works
Water photovoltaic plant contaminant identification using visible light images
Zhou, Y., & Sun, H.-R. (2022) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2022.102476 · Cited by 10 · see citing works
Object Detection in Satellite Imagery Using 2-Step Convolutional Neural Networks
Miyamoto, H., Uehara, K., Murakawa, M., Sakanashi, H., Nasato, H., Kouyama, T., & Nakamura, R. (2018) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2018.8518587 · Cited by 10 · see citing works
“Previous works employed higher-order local auto correlation [1], random forests [2], and deep learning [3][4][5][6].”
MethodDeep Convolutional Neural Network for Detection of Solar Panels
Golovko, V. A., Kroshchanka, A., Mikhno, E., Komar, M., & Sachenko, A. (2020) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-43070-2_17 · Cited by 10 · see citing works
Estimation of PV Location in Distribution Systems based on Voltage Sensitivities
Grijalva, S., Khan, A. U., Mbeleg, J., Gomez-Peces, C., Reno, M. J., & Blakely, L. (2021) · North American Power Symposium (NAPS) · doi:10.1109/naps50074.2021.9449639 · Cited by 10 · see citing works
Ordered directional monotonicity in the construction of edge detectors
Marco-Detchart, C., Bustince, H., Fernández, J., Mesiar, R., Lafuente, J. P., Barrenechea, E., & Pintor, J. M. (2020) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2020.07.002 · Cited by 9 · see citing works
An observational method for determining daily and regional photovoltaic solar energy statistics
Laevens, B. P. M., ten Bosch, O., Pijpers, F. P., & van Sark, W. G. J. H. M. (2021) · Solar Energy · doi:10.1016/j.solener.2021.08.077 · Cited by 9 · see citing works
Wang, J., Zheng, W., & Li, Z. (2022) · The Electricity Journal · doi:10.1016/j.tej.2022.107132 · Cited by 9 · see citing works
de Carvalho, O. L. F., Junior, O. A. D. C., de Albuquerque, A. O., Orlandi, A. G., Hirata, I., Borges, D. L., Gomes, R. A. T., & Guimarães, R. F. (2023) · Remote Sensing · doi:10.3390/rs15051240 · Cited by 8 · see citing works
Detecting Photovoltaic Panels in Aerial Images by Means of Characterising Colours
Marletta, D., Midolo, A., & Tramontana, E. (2023) · Technologies · doi:10.3390/technologies11060174 · Cited by 8 · see citing works
SolarFormer: Multi-scale Transformer for Solar PV Profiling
Luis, A. D., Tran, M. T., Hanyu, T., Tran, A. T., Haitao, L., McCann, R., Mantooth, A., Huang, Y., & Le, N. (2024) · International Conference on Smart Grid Synchronized Measurements and Analytics (SGSMA) · doi:10.1109/sgsma58694.2024.10571461 · Cited by 8 · see citing works
“The preliminary efforts in this field focused on binary classification of images, determining the presence or absence of PV installations [15] [16].”
BackgroundYang, J., Zhao, L., Xu, C., Sun, Y., Ren, H., & Nie, Z. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126548 · Cited by 7 · see citing works
- Authors
- Malof, J. M., Bradbury, K., Collins, L. M., & Newell, R. G.
- Affiliations
- Duke University (US)
- Corresponding author
- Malof, Jordan M.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2016-12
- Volume
- 183
- Pages
- 229-240
- ISSN
- 0306-2619 (ISSN-L)
- 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
- 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 › Computer Science › Artificial Intelligence › Solar Radiation and Photovoltaics
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Environmental Science › Environmental Engineering › Photovoltaic Systems and Sustainability
- Keywords
- solar photovoltaic array, very high resolution satellite imagery, aerial imagery, automated detection algorithm, solar energy mapping, solar resource assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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