92 citations as recorded by crossref.

1. Enhancing PV panel segmentation in remote sensing images with constraint refinement modules H. Tan et al. https://doi.org/10.1016/j.apenergy.2023.121757
2. Mapping the Spatial Distribution of Photovoltaic Power Plants in Northwest China Using Remote Sensing and Machine Learning X. Shi et al. https://doi.org/10.3390/su18020820
3. Deep Learning Framework for Multiclass Segmentation of Photovoltaic Systems S. Krikau & S. Keller https://doi.org/10.1109/TGRS.2025.3640268
4. TerraSegNet: Bilateral Axial Attention Network for Remote Sensing Image Segmentation in Diverse Environmental Monitoring Applications B. Setyawan Wijaya et al. https://doi.org/10.1109/ACCESS.2025.3633712
5. Fourier Multispectral Transformer for Robust Hyperspectral Reconstruction and Remote Sensing Segmentation S. Grigoryan et al. https://doi.org/10.1134/S1054661825701317
6. High-resolution solar panel detection in Sfax, Tunisia: A UNet-Based approach M. Bouaziz et al. https://doi.org/10.1016/j.renene.2024.121171
7. SPHERE: Benchmarking YOLO vs. CNN on a Novel Dataset for High-Accuracy Solar Panel Defect Detection in Renewable Energy Systems K. Ayturan et al. https://doi.org/10.3390/app15094880
8. DBSF: dual-branch boundary-supplemented framework for photovoltaic power station extraction B. Yu et al. https://doi.org/10.1080/17538947.2025.2500531
9. GIScience can facilitate the development of solar cities for energy transition R. Zhu et al. https://doi.org/10.1016/j.adapen.2023.100129
10. Optimizing PV Panel Segmentation in Complex Environments Using Pre-Training and Simulated Annealing Algorithm: The JSWPVI R. Zhang et al. https://doi.org/10.3390/land14061245
11. CSPPNet: A Convolution and State-Space-Based Photovoltaic Panel Extraction Network Using Gaofen-2 High-Resolution Imagery W. Liu et al. https://doi.org/10.1109/JSTARS.2025.3543490
12. Detailed PV Monitor: A Highly Generalized Photovoltaic Panels Segmentation Network Integrating Context-Aware and Deep Feature Reconstruction X. Zhang et al. https://doi.org/10.1109/JSTARS.2025.3558471
13. Assessment of the large-scale extraction of photovoltaic (PV) panels with a workflow based on artificial neural networks and algorithmic postprocessing of vectorization results M. Manso-Callejo et al. https://doi.org/10.1016/j.jag.2023.103563
14. Enhancing visual feature constraints in segmentation models for photovoltaic panel recognition Z. Zhao et al. https://doi.org/10.1016/j.egyai.2025.100544
15. Combined Hybrid Neural Networks and Swarm Intelligence Optimization Algorithms for Photovoltaic Panel Segmentation From Remote Sensing Images X. Zhang et al. https://doi.org/10.1109/ACCESS.2024.3406551
16. Joint-task learning framework with scale adaptive and position guidance modules for improved household rooftop photovoltaic segmentation in remote sensing image L. Li et al. https://doi.org/10.1016/j.apenergy.2024.124521
17. High-Fidelity Remote Sensing-Based Solar Panel Mapping Using a Parameter-Efficient UNet With Dilated Multi-Scale Convolutions and Lightweight Attention K. Nazir et al. https://doi.org/10.1109/ACCESS.2026.3657280
18. Deep learning for photovoltaic panels segmentation K. Bouzaachane et al. https://doi.org/10.23939/mmc2023.03.638
19. Data-Model Complexity Trade-Off in UAV-Acquired Ultra-High-Resolution Remote Sensing: Empirical Study on Photovoltaic Panel Segmentation Z. Zou et al. https://doi.org/10.3390/drones9090619
20. PYS: A classification and extraction model of photovoltaics for providing more detailed data to support photovoltaic sustainable development D. Chen et al. https://doi.org/10.1016/j.seta.2023.103578
21. Characterization and mapping of photovoltaic solar power plants by Landsat imagery and random forest: A case study in Gansu Province, China X. Wang et al. https://doi.org/10.1016/j.jclepro.2023.138015
22. Labeled photovoltaic installations for orthographic aerial imagery in Queens, New York T. Furedi et al. https://doi.org/10.1038/s41597-025-06523-2
23. A Downscaling Methodology for Extracting Photovoltaic Plants with Remote Sensing Data: From Feature Optimized Random Forest to Improved HRNet Y. Wang et al. https://doi.org/10.3390/rs15204931
24. Spatial and Temporal Expansion of Photovoltaic Sites and Thermal Environmental Effects in Ningxia Based on Remote Sensing and Deep Learning H. Xie et al. https://doi.org/10.3390/rs17142440
25. Remote-sensing extraction and carbon emission reduction benefit assessment for centralized photovoltaic power plants in Agrivoltaic systems C. Huang et al. https://doi.org/10.1016/j.apenergy.2024.123585
26. Deformable Transformer and Spectral U-Net for Large-Scale Hyperspectral Image Semantic Segmentation T. Zhang et al. https://doi.org/10.1109/JSTARS.2024.3485239
27. Mapping of Utility-Scale Solar Panel Areas From 2000 to 2022 in China Using Google Earth Engine X. Lyu et al. https://doi.org/10.1109/JSTARS.2024.3468627
28. High-resolution analysis of rooftop photovoltaic potential based on hourly generation simulations and load profiles H. Jiang et al. https://doi.org/10.1016/j.apenergy.2023.121553
29. Exploration of determinants underlying regional disparity in rooftop photovoltaic adoption: A case study in Nagoya, Japan L. Tao et al. https://doi.org/10.1016/j.apenergy.2024.123469
30. Comparative Performance Evaluation of YOLOv5, YOLOv8, and YOLOv11 for Solar Panel Defect Detection R. Khanam et al. https://doi.org/10.3390/solar5010006
31. Unraveling the effects of lake-surface environment on floating photovoltaic electricity generation in Southeast Asia D. Hu et al. https://doi.org/10.1080/20964471.2025.2607852
32. A Bottom-Up Approach Integrating Computer Vision with Material Flow Analysis to Estimate the Recycling Potential of Distributed Solar Panels Using Satellite Imagery P. Jiang et al. https://doi.org/10.1021/acs.est.5c01600
33. Synthesizing images with aligned masks using text-to-image based generative AI for robust PV segmentation H. Tan et al. https://doi.org/10.1016/j.renene.2026.125217
34. Deep Learning-Based Health Monitoring for Photovoltaic Systems K. Alnuaimi et al. https://doi.org/10.1109/JPHOTOV.2025.3563887
35. Comparative Performance of Machine Learning Classifiers for Photovoltaic Mapping in Arid Regions Using Google Earth Engine L. Zhang et al. https://doi.org/10.3390/en18174464
36. Deep solar PV refiner: A detail-oriented deep learning network for refined segmentation of photovoltaic areas from satellite imagery R. Zhu et al. https://doi.org/10.1016/j.jag.2022.103134
37. An Information Extraction Method for Industrial and Commercial Rooftop Photovoltaics Based on GaoFen-7 Remote Sensing Images H. Tao et al. https://doi.org/10.3390/rs15245744
38. Fine-Grained Hashing for High Spatial Resolution Optical-SAR Remote Sensing Image Retrieval G. Lingyun et al. https://doi.org/10.1109/JSTARS.2026.3653578
39. Impact of Synthetic Data on Deep Learning Models for Earth Observation: Photovoltaic Panel Detection Case Study E. Hisam et al. https://doi.org/10.3390/ijgi14120481
40. Globally interconnected solar-wind system addresses future electricity demands H. Jiang et al. https://doi.org/10.1038/s41467-025-59879-9
41. Classification and segmentation of five photovoltaic types based on instance segmentation for generating more refined photovoltaic data D. Chen et al. https://doi.org/10.1016/j.apenergy.2024.124296
42. Extracting Photovoltaic Panels From Heterogeneous Remote Sensing Images With Spatial and Spectral Differences Z. Zhao et al. https://doi.org/10.1109/JSTARS.2024.3369660
43. Detailed Aerial Mapping of Photovoltaic Power Plants Through Semantically Significant Keypoints V. Kozák et al. https://doi.org/10.26833/ijeg.1737764
44. A Novel Framework for Solar Panel Segmentation From Remote Sensing Images: Utilizing Chebyshev Transformer and Hyperspectral Decomposition H. Gasparyan et al. https://doi.org/10.1109/TGRS.2024.3386402
45. Artificial Vision in Renewable Photovoltaic Systems: A Review and Vision of Specific Applications and Technologies T. Amaral et al. https://doi.org/10.3390/app152413285
46. A CLIP-Based Zero-Shot Photovoltaic Segmentation Framework for Remote Sensing Imagery H. Li et al. https://doi.org/10.3390/rs18060865
47. Generalized deep learning model for photovoltaic module segmentation from satellite and aerial imagery G. García et al. https://doi.org/10.1016/j.solener.2024.112539
48. A harmonized dataset of ground-mounted solar energy in the US with enhanced metadata J. Stid et al. https://doi.org/10.1038/s41597-025-05862-4
49. Extraction of Photovoltaic Plants Using Machine Learning Methods: A Case Study of the Pilot Energy City of Golmud, China Z. Chen et al. https://doi.org/10.3390/rs14112697
50. PV Identifier: Extraction of small-scale distributed photovoltaics in complex environments from high spatial resolution remote sensing images N. Lu et al. https://doi.org/10.1016/j.apenergy.2024.123311
51. PMDS-UNet: A novel approach for precise segmentation of distributed photovoltaics in high-resolution remote sensing imagery S. Chen et al. https://doi.org/10.1016/j.apenergy.2026.128229
52. Photovoltaic Panel Identification and Potential Assessment Based on Remote Sensing and Deep Learning: A Case Study of Yancheng City J. Wu et al. https://doi.org/10.3390/rs18152542
53. The Use of Drone Photo Material to Classify the Purity of Photovoltaic Panels Based on Statistical Classifiers T. Czarnecki & K. Bloch https://doi.org/10.3390/s22020483
54. Edge-preserving and uncertainty-guided domain adaptation for weakly supervised rooftop photovoltaic mapping S. Wang et al. https://doi.org/10.1016/j.isprsjprs.2026.07.008
55. Deep-Learning-Based Evaluation of Rooftop Photovoltaic Deployment in Tianjin, China M. Shan et al. https://doi.org/10.3390/ijgi14030101
56. Advances and prospects on estimating solar photovoltaic installation capacity and potential based on satellite and aerial images H. Mao et al. https://doi.org/10.1016/j.rser.2023.113276
57. Automatic Rooftop Solar Panel Recognition from UAV LiDAR Data Using Deep Learning and Geometric Feature Analysis J. Coglan et al. https://doi.org/10.3390/rs17193389
58. Application of photovoltaics on different types of land in China: Opportunities, status and challenges C. Song et al. https://doi.org/10.1016/j.rser.2023.114146
59. GeoAI-Driven mapping of urban rooftop Photovoltaics: A sustainable energy framework for Karachi, Pakistan M. Lodhi et al. https://doi.org/10.1016/j.jag.2025.104786
60. PV-JSSR: Joint super-resolution and segmentation with cross-task interaction for photovoltaic panel mapping from multi-resolution imagery A. Batool et al. https://doi.org/10.1016/j.apenergy.2026.128627
61. Impact of Deep Convolutional Neural Network Structure on Photovoltaic Array Extraction from High Spatial Resolution Remote Sensing Images L. Li et al. https://doi.org/10.3390/rs15184554
62. Edge-enhanced SAM for extracting photovoltaic power plants from remote sensing imagery Y. Chen et al. https://doi.org/10.1016/j.jag.2025.104580
63. SPFNet2: A lightweight solar panel fault detection framework using parallel U-Net and MobileNetV3Large R. Rudro et al. https://doi.org/10.1016/j.renene.2026.125235
64. Spectral-Feature-Driven photovoltaic Detection: A universal Physics-Based index for rapid Localization S. He et al. https://doi.org/10.1016/j.jag.2026.105164
65. Multi-Resolution Segmentation of Solar Photovoltaic Systems Using Deep Learning M. Kleebauer et al. https://doi.org/10.3390/rs15245687
66. Mapping national-scale photovoltaic power stations using a novel enhanced photovoltaic index and evaluating carbon reduction benefits J. Wang et al. https://doi.org/10.1016/j.enconman.2024.118894
67. A deep learning based framework for solar panel segmentation and fault classification enhanced with explainable AI A. Adib et al. https://doi.org/10.1016/j.solener.2025.114058
68. Comprehensive analysis of tropical rooftop PV project: A case study in nanning X. Wang et al. https://doi.org/10.1016/j.heliyon.2023.e14131
69. PVNet: A novel semantic segmentation model for extracting high-quality photovoltaic panels in large-scale systems from high-resolution remote sensing imagery J. Wang et al. https://doi.org/10.1016/j.jag.2023.103309
70. Toward global rooftop PV detection with Deep Active Learning M. Zech et al. https://doi.org/10.1016/j.adapen.2024.100191
71. Rooftop PV Segmenter: A Size-Aware Network for Segmenting Rooftop Photovoltaic Systems from High-Resolution Imagery J. Wang et al. https://doi.org/10.3390/rs15215232
72. Query Adaptive Transformer and Multiprototype Rectification for Few-Shot Remote Sensing Image Segmentation T. Gao et al. https://doi.org/10.1109/TGRS.2024.3511622
73. Data Augmentation with Generative Adversarial Network for Solar Panel Segmentation from Remote Sensing Images J. Lekavičius & V. Gružauskas https://doi.org/10.3390/en17133204
74. DBGENet: a dual-branch global-context and edge-aware encoder network for photovoltaic panel segmentation under complex remote sensing scenes C. Xie et al. https://doi.org/10.1088/2631-8695/ae8cbf
75. Geospatial assessment of rooftop solar photovoltaic potential using multi-source remote sensing data H. Jiang et al. https://doi.org/10.1016/j.egyai.2022.100185
76. Automated Rooftop Solar Panel Detection Through Convolutional Neural Networks S. Pena Pereira et al. https://doi.org/10.1080/07038992.2024.2363236
77. SR-Mamba: A Structure-Retentive State-Space Network for Geometry-Aligned Photovoltaic Segmentation From Remote Sensing Imagery K. Nazir et al. https://doi.org/10.1109/JSTARS.2026.3702612
78. FEPVNet: A Network with Adaptive Strategies for Cross-Scale Mapping of Photovoltaic Panels from Multi-Source Images B. Su et al. https://doi.org/10.3390/rs15092469
79. A solar panel dataset of very high resolution satellite imagery to support the Sustainable Development Goals C. Clark & F. Pacifici https://doi.org/10.1038/s41597-023-02539-8
80. A Lightweight Self Attention Based Multi-Task Deep Learning Model for Industrial Solar Panel and Environmental Monitoring T. Gangopadhyay et al. https://doi.org/10.1109/TETCI.2024.3444590
81. Assessment of offshore wind-solar energy potentials and spatial layout optimization in mainland China H. Jiang et al. https://doi.org/10.1016/j.oceaneng.2023.115914
82. The spatiotemporal patterns of national-subsidized PV power projects in China: Evolution and influencing factors C. Xiao et al. https://doi.org/10.1016/j.infgeo.2025.100036
83. Mapping Photovoltaic Panels in Coastal China Using Sentinel-1 and Sentinel-2 Images and Google Earth Engine H. Zhang et al. https://doi.org/10.3390/rs15153712
84. Remote sensing of photovoltaic scenarios: Techniques, applications and future directions Q. Chen et al. https://doi.org/10.1016/j.apenergy.2022.120579
85. Multi-sourced data modelling of spatially heterogenous life-cycle carbon mitigation from installed rooftop photovoltaics: A case study in Singapore R. Zhu et al. https://doi.org/10.1016/j.apenergy.2024.122957
86. Detecting Photovoltaic Panels in Aerial Images by Means of Characterising Colours D. Marletta et al. https://doi.org/10.3390/technologies11060174
87. Uncovering the rapid expansion of photovoltaic power plants in China from 2010 to 2022 using satellite data and deep learning Y. Chen et al. https://doi.org/10.1016/j.rse.2024.114100
88. Vectorization Method for Remote Sensing Object Segmentation Based on Frame Field Learning: A Case Study of Greenhouses L. Yao et al. https://doi.org/10.1109/TGRS.2024.3403425
89. DSFA-SwinNet: A Multi-Scale Attention Fusion Network for Photovoltaic Areas Detection S. Lin et al. https://doi.org/10.3390/rs17020332
90. Optimization Method for Remote Sensing Image-Based Photovoltaic Panel Segmentation via Perception-Driven Enhancement in Nonideal Environments X. Chao et al. https://doi.org/10.1109/JSTARS.2025.3602477
91. General generative AI-based image augmentation method for robust rooftop PV segmentation H. Tan et al. https://doi.org/10.1016/j.apenergy.2024.123554
92. ESG reporting in the insurance industry: assessing the quantity, quality and utility of the reported KPIs A. Marti et al. https://doi.org/10.1108/JFRA-02-2025-0115