Articles | Volume 13, issue 11
https://doi.org/10.5194/essd-13-5389-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-5389-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery
Hou Jiang
State Key Laboratory of Resources and Environmental Information
System, Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, China
State Key Laboratory of Resources and Environmental Information
System, Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, China
Southern Marine Science and Engineering Guangdong Laboratory,
Guangzhou 511458, China
Jiangsu Center for Collaborative Innovation in Geographical
Information Resource Development and Application, Nanjing Normal University,
Nanjing 210023, China
State Key Laboratory of Resources and Environmental Information
System, Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, China
Southern Marine Science and Engineering Guangdong Laboratory,
Guangzhou 511458, China
Jiangsu Center for Collaborative Innovation in Geographical
Information Resource Development and Application, Nanjing Normal University,
Nanjing 210023, China
Jun Qin
State Key Laboratory of Resources and Environmental Information
System, Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, China
Southern Marine Science and Engineering Guangdong Laboratory,
Guangzhou 511458, China
Tang Liu
School of Information Engineering, China University of Geosciences
(Beijing), Beijing 100083, China
Yujun Liu
State Key Laboratory of Resources and Environmental Information
System, Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, China
Provincial Geomatics Center of Jiangsu, Nanjing 210013, China
Chenghu Zhou
State Key Laboratory of Resources and Environmental Information
System, Institute of Geographic Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing 100101, China
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- Enhancing PV panel segmentation in remote sensing images with constraint refinement modules H. Tan et al. 10.1016/j.apenergy.2023.121757
- A Downscaling Methodology for Extracting Photovoltaic Plants with Remote Sensing Data: From Feature Optimized Random Forest to Improved HRNet Y. Wang et al. 10.3390/rs15204931
- Extraction of Photovoltaic Plants Using Machine Learning Methods: A Case Study of the Pilot Energy City of Golmud, China Z. Chen et al. 10.3390/rs14112697
- The Use of Drone Photo Material to Classify the Purity of Photovoltaic Panels Based on Statistical Classifiers T. Czarnecki & K. Bloch 10.3390/s22020483
- High-resolution analysis of rooftop photovoltaic potential based on hourly generation simulations and load profiles H. Jiang et al. 10.1016/j.apenergy.2023.121553
- Geospatial assessment of rooftop solar photovoltaic potential using multi-source remote sensing data H. Jiang et al. 10.1016/j.egyai.2022.100185
- Advances and prospects on estimating solar photovoltaic installation capacity and potential based on satellite and aerial images H. Mao et al. 10.1016/j.rser.2023.113276
- Application of photovoltaics on different types of land in China: Opportunities, status and challenges C. Song et al. 10.1016/j.rser.2023.114146
- FEPVNet: A Network with Adaptive Strategies for Cross-Scale Mapping of Photovoltaic Panels from Multi-Source Images B. Su et al. 10.3390/rs15092469
- GIScience can facilitate the development of solar cities for energy transition R. Zhu et al. 10.1016/j.adapen.2023.100129
- Deep solar PV refiner: A detail-oriented deep learning network for refined segmentation of photovoltaic areas from satellite imagery R. Zhu et al. 10.1016/j.jag.2022.103134
- A solar panel dataset of very high resolution satellite imagery to support the Sustainable Development Goals C. Clark & F. Pacifici 10.1038/s41597-023-02539-8
- An Information Extraction Method for Industrial and Commercial Rooftop Photovoltaics Based on GaoFen-7 Remote Sensing Images H. Tao et al. 10.3390/rs15245744
- Assessment of offshore wind-solar energy potentials and spatial layout optimization in mainland China H. Jiang et al. 10.1016/j.oceaneng.2023.115914
- 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. 10.1016/j.jag.2023.103563
- Impact of Deep Convolutional Neural Network Structure on Photovoltaic Array Extraction from High Spatial Resolution Remote Sensing Images L. Li et al. 10.3390/rs15184554
- Mapping Photovoltaic Panels in Coastal China Using Sentinel-1 and Sentinel-2 Images and Google Earth Engine H. Zhang et al. 10.3390/rs15153712
- Multi-Resolution Segmentation of Solar Photovoltaic Systems Using Deep Learning M. Kleebauer et al. 10.3390/rs15245687
- Remote sensing of photovoltaic scenarios: Techniques, applications and future directions Q. Chen et al. 10.1016/j.apenergy.2022.120579
- Multi-sourced data modelling of spatially heterogenous life-cycle carbon mitigation from installed rooftop photovoltaics: A case study in Singapore R. Zhu et al. 10.1016/j.apenergy.2024.122957
- Deep learning for photovoltaic panels segmentation K. Bouzaachane et al. 10.23939/mmc2023.03.638
- Detecting Photovoltaic Panels in Aerial Images by Means of Characterising Colours D. Marletta et al. 10.3390/technologies11060174
- Comprehensive analysis of tropical rooftop PV project: A case study in nanning X. Wang et al. 10.1016/j.heliyon.2023.e14131
- PYS: A classification and extraction model of photovoltaics for providing more detailed data to support photovoltaic sustainable development D. Chen et al. 10.1016/j.seta.2023.103578
- 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. 10.1016/j.jclepro.2023.138015
- 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. 10.1016/j.jag.2023.103309
- Uncovering the rapid expansion of photovoltaic power plants in China from 2010 to 2022 using satellite data and deep learning Y. Chen et al. 10.1016/j.rse.2024.114100
- Rooftop PV Segmenter: A Size-Aware Network for Segmenting Rooftop Photovoltaic Systems from High-Resolution Imagery J. Wang et al. 10.3390/rs15215232
- Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery H. Jiang et al. 10.5194/essd-13-5389-2021
28 citations as recorded by crossref.
- Enhancing PV panel segmentation in remote sensing images with constraint refinement modules H. Tan et al. 10.1016/j.apenergy.2023.121757
- A Downscaling Methodology for Extracting Photovoltaic Plants with Remote Sensing Data: From Feature Optimized Random Forest to Improved HRNet Y. Wang et al. 10.3390/rs15204931
- Extraction of Photovoltaic Plants Using Machine Learning Methods: A Case Study of the Pilot Energy City of Golmud, China Z. Chen et al. 10.3390/rs14112697
- The Use of Drone Photo Material to Classify the Purity of Photovoltaic Panels Based on Statistical Classifiers T. Czarnecki & K. Bloch 10.3390/s22020483
- High-resolution analysis of rooftop photovoltaic potential based on hourly generation simulations and load profiles H. Jiang et al. 10.1016/j.apenergy.2023.121553
- Geospatial assessment of rooftop solar photovoltaic potential using multi-source remote sensing data H. Jiang et al. 10.1016/j.egyai.2022.100185
- Advances and prospects on estimating solar photovoltaic installation capacity and potential based on satellite and aerial images H. Mao et al. 10.1016/j.rser.2023.113276
- Application of photovoltaics on different types of land in China: Opportunities, status and challenges C. Song et al. 10.1016/j.rser.2023.114146
- FEPVNet: A Network with Adaptive Strategies for Cross-Scale Mapping of Photovoltaic Panels from Multi-Source Images B. Su et al. 10.3390/rs15092469
- GIScience can facilitate the development of solar cities for energy transition R. Zhu et al. 10.1016/j.adapen.2023.100129
- Deep solar PV refiner: A detail-oriented deep learning network for refined segmentation of photovoltaic areas from satellite imagery R. Zhu et al. 10.1016/j.jag.2022.103134
- A solar panel dataset of very high resolution satellite imagery to support the Sustainable Development Goals C. Clark & F. Pacifici 10.1038/s41597-023-02539-8
- An Information Extraction Method for Industrial and Commercial Rooftop Photovoltaics Based on GaoFen-7 Remote Sensing Images H. Tao et al. 10.3390/rs15245744
- Assessment of offshore wind-solar energy potentials and spatial layout optimization in mainland China H. Jiang et al. 10.1016/j.oceaneng.2023.115914
- 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. 10.1016/j.jag.2023.103563
- Impact of Deep Convolutional Neural Network Structure on Photovoltaic Array Extraction from High Spatial Resolution Remote Sensing Images L. Li et al. 10.3390/rs15184554
- Mapping Photovoltaic Panels in Coastal China Using Sentinel-1 and Sentinel-2 Images and Google Earth Engine H. Zhang et al. 10.3390/rs15153712
- Multi-Resolution Segmentation of Solar Photovoltaic Systems Using Deep Learning M. Kleebauer et al. 10.3390/rs15245687
- Remote sensing of photovoltaic scenarios: Techniques, applications and future directions Q. Chen et al. 10.1016/j.apenergy.2022.120579
- Multi-sourced data modelling of spatially heterogenous life-cycle carbon mitigation from installed rooftop photovoltaics: A case study in Singapore R. Zhu et al. 10.1016/j.apenergy.2024.122957
- Deep learning for photovoltaic panels segmentation K. Bouzaachane et al. 10.23939/mmc2023.03.638
- Detecting Photovoltaic Panels in Aerial Images by Means of Characterising Colours D. Marletta et al. 10.3390/technologies11060174
- Comprehensive analysis of tropical rooftop PV project: A case study in nanning X. Wang et al. 10.1016/j.heliyon.2023.e14131
- PYS: A classification and extraction model of photovoltaics for providing more detailed data to support photovoltaic sustainable development D. Chen et al. 10.1016/j.seta.2023.103578
- 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. 10.1016/j.jclepro.2023.138015
- 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. 10.1016/j.jag.2023.103309
- Uncovering the rapid expansion of photovoltaic power plants in China from 2010 to 2022 using satellite data and deep learning Y. Chen et al. 10.1016/j.rse.2024.114100
- Rooftop PV Segmenter: A Size-Aware Network for Segmenting Rooftop Photovoltaic Systems from High-Resolution Imagery J. Wang et al. 10.3390/rs15215232
1 citations as recorded by crossref.
Latest update: 28 Mar 2024
Short summary
A multi-resolution (0.8, 0.3, and 0.1 m) photovoltaic (PV) dataset is established using satellite and aerial images. The dataset contains 3716 samples of PVs installed on various land and rooftop types. The dataset can support multi-scale PV segmentation (e.g., concentrated PVs, distributed ground PVs, and fine-grained rooftop PVs) and cross applications between different resolutions (e.g., from satellite to aerial samples and vice versa), as well as other research related to PVs.
A multi-resolution (0.8, 0.3, and 0.1 m) photovoltaic (PV) dataset is established using...
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