Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery
文献类型：期刊论文
| 作者 | Jiang, Hou4; Yao, Ling2,3,4; Lu, Ning2,3,4; Qin, Jun3,4; Liu, Tang5; Liu, Yujun1,4; Zhou, Chenghu4 |
| 刊名 | EARTH SYSTEM SCIENCE DATA
 |
| 出版日期 | 2021-11-19 |
| 卷号 | 13期号:11页码:5389-5401 |
| ISSN号 | 1866-3508 |
| DOI | 10.5194/essd-13-5389-2021 |
| 通讯作者 | Yao, Ling(yaoling@lreis.ac.cn) |
| 英文摘要 | In the context of global carbon emission reduction, solar photovoltaic (PV) technology is experiencing rapid development. Accurate localized PV information, including location and size, is the basis for PV regulation and potential assessment of the energy sector. Automatic information extraction based on deep learning requires high-quality labeled samples that should be collected at multiple spatial resolutions and under different backgrounds due to the diversity and variable scale of PVs. We established a PV dataset using satellite and aerial images with spatial resolutions of 0.8, 0.3, and 0.1 m, which focus on concentrated PVs, distributed ground PVs, and fine-grained rooftop PVs, respectively. The dataset contains 3716 samples of PVs installed on shrub land, grassland, cropland, saline-alkali land, and water surfaces, as well as flat concrete, steel tile, and brick roofs. The dataset is used to examine the model performance of different deep networks on PV segmentation. On average, an intersection over union (IoU) greater than 85% is achieved. In addition, our experiments show that direct cross application between samples with different resolutions is not feasible and that fine-tuning of the pre-trained deep networks using target samples is necessary. The dataset can support more work on PV technology for greater value, such as developing a PV detection algorithm, simulating PV conversion efficiency, and estimating regional PV potential. The dataset is available from Zenodo on the following website: https://doi.org/10.5281/zenodo.5171712 (Jiang et al., 2021). |
| WOS关键词 | SOLAR ; CLASSIFICATION ; IMPACTS ; PLANTS ; PV |
| 资助项目 | National Natural Science Foundation of China[41771380] ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory[GML2019ZD0301] |
| WOS研究方向 | Geology ; Meteorology & Atmospheric Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:000721699100001 |
| 出版者 | COPERNICUS GESELLSCHAFT MBH |
| 资助机构 | National Natural Science Foundation of China ; Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/167886]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Yao, Ling |
| 作者单位 | 1.Prov Geomat Ctr Jiangsu, Nanjing 210013, Peoples R China 2.Nanjing Normal Univ, Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Peoples R China 3.Southern Marine Sci & Engn Guangdong Lab, Guangzhou 511458, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 5.China Univ Geosci Beijing, Sch Informat Engn, Beijing 100083, Peoples R China |
| 推荐引用方式 GB/T 7714 | Jiang, Hou,Yao, Ling,Lu, Ning,et al. Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery[J]. EARTH SYSTEM SCIENCE DATA,2021,13(11):5389-5401. |
| APA | Jiang, Hou.,Yao, Ling.,Lu, Ning.,Qin, Jun.,Liu, Tang.,...&Zhou, Chenghu.(2021).Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery.EARTH SYSTEM SCIENCE DATA,13(11),5389-5401. |
| MLA | Jiang, Hou,et al."Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery".EARTH SYSTEM SCIENCE DATA 13.11(2021):5389-5401. |
入库方式： OAI收割
来源：地理科学与资源研究所
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