Articles | Volume 13, issue 11
Earth Syst. Sci. Data, 13, 5389–5401, 2021
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.
Earth Syst. Sci. Data, 13, 5389–5401, 2021
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.
Data description paper 19 Nov 2021
Data description paper | 19 Nov 2021
Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery
Hou Jiang et al.
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Hou Jiang, Ning Lu, Jun Qin, and Ling Yao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-209, https://doi.org/10.5194/essd-2019-209, 2019
Revised manuscript not accepted
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This study produces a 12-year (2007–2018) hourly surface global and diffuse solar radiation dataset with 0.05° grids over China based on geostationary satellite data using deep learning technique. The produced data have much higher accuracy than results of traditional methods and current widely-used products due to integration of spatial pattern through convolutional neural network, enlightening studies involving spatial features, and reveal the long-term regional variations in fine scales.
Chongyang Wang, Li Wang, Danni Wang, Dan Li, Chenghu Zhou, Hao Jiang, Qiong Zheng, Shuisen Chen, Kai Jia, Yangxiaoyue Liu, Ji Yang, Xia Zhou, and Yong Li
Geosci. Model Dev., 14, 6833–6846, https://doi.org/10.5194/gmd-14-6833-2021, https://doi.org/10.5194/gmd-14-6833-2021, 2021
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The turbidity maximum zone (TMZ) is a special phenomenon in estuaries worldwide. However, the extraction methods and criteria used to describe the TMZ vary significantly both spatially and temporally. This study proposes an new index, the turbidity maximum zone index, based on the corresponding relationship of total suspended solid concentration and Chl a concentration, which could better extract TMZs in different estuaries and on different dates.
Hou Jiang, Ning Lu, Jun Qin, and Ling Yao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-209, https://doi.org/10.5194/essd-2019-209, 2019
Revised manuscript not accepted
Short summary
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This study produces a 12-year (2007–2018) hourly surface global and diffuse solar radiation dataset with 0.05° grids over China based on geostationary satellite data using deep learning technique. The produced data have much higher accuracy than results of traditional methods and current widely-used products due to integration of spatial pattern through convolutional neural network, enlightening studies involving spatial features, and reveal the long-term regional variations in fine scales.
Jiawei Yi, Yunyan Du, Fuyuan Liang, Tao Pei, Ting Ma, and Chenghu Zhou
Nat. Hazards Earth Syst. Sci., 19, 2169–2182, https://doi.org/10.5194/nhess-19-2169-2019, https://doi.org/10.5194/nhess-19-2169-2019, 2019
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This paper utilized the advantages of smartphone location data to study human responses to rainstorm disasters. Intense rainstorms disrupt city residents' behaviors as reflected in anomalies of location-based service requests. Anomaly identification from fine-scale smartphone location data facilitates the monitoring of social responses to rainstorms. Residents' collective geotagged behaviors in different cities show different sensitivities to rainstorms.
Changchun Huang, Quanliang Jiang, Ling Yao, Hao Yang, Chen Lin, Tao Huang, A-Xing Zhu, and Yimin Zhang
Biogeosciences, 15, 1827–1841, https://doi.org/10.5194/bg-15-1827-2018, https://doi.org/10.5194/bg-15-1827-2018, 2018
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The latitudinal dependency of POC / PON in ocean and inland water is significant, regulated by trophic state and climate, etc. factors. POC / PON significantly increased from coastal water (6.89 ± 2.38) to open ocean (7.59 ± 4.22) with the increasing rate of 0.0024 / km. The re-examination of the global relationship between, and variations in, POC and PON could be important for the global and regional coupling between the carbon and nitrogen cycles in the ocean and freshwater.
J. Yi, Y. Du, Z. He, and C. Zhou
Ocean Sci., 10, 39–48, https://doi.org/10.5194/os-10-39-2014, https://doi.org/10.5194/os-10-39-2014, 2014
J. Yi, Y. Du, X. Wang, Z. He, and C. Zhou
Ocean Sci., 9, 171–182, https://doi.org/10.5194/os-9-171-2013, https://doi.org/10.5194/os-9-171-2013, 2013
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African anthropogenic emissions inventory for gases and particles from 1990 to 2015
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Margarita Choulga, Greet Janssens-Maenhout, Ingrid Super, Efisio Solazzo, Anna Agusti-Panareda, Gianpaolo Balsamo, Nicolas Bousserez, Monica Crippa, Hugo Denier van der Gon, Richard Engelen, Diego Guizzardi, Jeroen Kuenen, Joe McNorton, Gabriel Oreggioni, and Antoon Visschedijk
Earth Syst. Sci. Data, 13, 5311–5335, https://doi.org/10.5194/essd-13-5311-2021, https://doi.org/10.5194/essd-13-5311-2021, 2021
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People worry that growing man-made carbon dioxide (CO2) concentrations lead to climate change. Global models, use of observations, and datasets can help us better understand behaviour of CO2. Here a tool to compute uncertainty in man-made CO2 sources per country per year and month is presented. An example of all sources separated into seven groups (intensive and average energy, industry, humans, ground and air transport, others) is presented. Results will be used to predict CO2 concentrations.
Jan C. Minx, William F. Lamb, Robbie M. Andrew, Josep G. Canadell, Monica Crippa, Niklas Döbbeling, Piers M. Forster, Diego Guizzardi, Jos Olivier, Glen P. Peters, Julia Pongratz, Andy Reisinger, Matthew Rigby, Marielle Saunois, Steven J. Smith, Efisio Solazzo, and Hanqin Tian
Earth Syst. Sci. Data, 13, 5213–5252, https://doi.org/10.5194/essd-13-5213-2021, https://doi.org/10.5194/essd-13-5213-2021, 2021
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We provide a synthetic dataset on anthropogenic greenhouse gas (GHG) emissions for 1970–2018 with a fast-track extension to 2019. We show that GHG emissions continued to rise across all gases and sectors. Annual average GHG emissions growth slowed, but absolute decadal increases have never been higher in human history. We identify a number of data gaps and data quality issues in global inventories and highlight their importance for monitoring progress towards international climate goals.
S. Enrique Puliafito, Tomás R. Bolaño-Ortiz, Rafael P. Fernandez, Lucas L. Berná, Romina M. Pascual-Flores, Josefina Urquiza, Ana I. López-Noreña, and María F. Tames
Earth Syst. Sci. Data, 13, 5027–5069, https://doi.org/10.5194/essd-13-5027-2021, https://doi.org/10.5194/essd-13-5027-2021, 2021
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GEAA-AEIv3.0M atmospheric emissions inventory is the first high-spatial-resolution inventory (approx. 2.5 km × 2.5 km) with monthly variability from 1995 to 2020, including greenhouse gases, ozone precursors, acidifying gases, and particulate matter, from all Argentine productive activities. The main benefit of GEAA-AEIv3.0M is to map emissions with better temporal resolution to support air quality and climate modeling, to evaluate pollutant mitigation strategies by Argentine decision makers.
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Earth Syst. Sci. Data, 13, 3691–3705, https://doi.org/10.5194/essd-13-3691-2021, https://doi.org/10.5194/essd-13-3691-2021, 2021
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This inventory fills the gap in African regional inventories, providing biofuel and fossil fuel emissions that take into account African specificities. It could be used for air quality modeling. We show that all pollutant emissions are globally increasing during the period 1990–2015. Also, West Africa and East Africa emissions are largely due to domestic fire and traffic activities, while southern Africa and northern Africa emissions are largely due to industrial and power plant sources.
Albana Kona, Fabio Monforti-Ferrario, Paolo Bertoldi, Marta Giulia Baldi, Georgia Kakoulaki, Nadja Vetters, Christian Thiel, Giulia Melica, Eleonora Lo Vullo, Alessandra Sgobbi, Christofer Ahlgren, and Brieuc Posnic
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The Global Covenant of Mayors for Climate & Energy (GCoM), the largest international initiative to promote climate action at the city level, has collected a large amount of information on urban greenhouse gas emissions.
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A catalog of point sources of nitrogen oxides was created using satellite observations of NO2. Key for the identification of point sources was the divergence, i.e., the difference between upwind and downwind levels of NO2.
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Earth Syst. Sci. Data, 13, 1667–1680, https://doi.org/10.5194/essd-13-1667-2021, https://doi.org/10.5194/essd-13-1667-2021, 2021
Alison R. Marklein, Deanne Meyer, Marc L. Fischer, Seongeun Jeong, Talha Rafiq, Michelle Carr, and Francesca M. Hopkins
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Dairy cow farms produce half of California's (CA) methane (CH4) emissions. Current CH4 emission inventories lack regional variation in management and are inadequate to assess CH4 mitigation measures. We develop a spatial database of CH4 emissions for CA dairy farms including farm-scale herd demographics and management data. This database is useful to predict CH4 emission reductions from mitigation efforts, to compare with atmospheric CH4 observations and to attribute emissions to specific farms.
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CH4 is a potent greenhouse gas, and China’s anthropogenic CH4 emissions account for a large proportion of global total emissions. However, the existing estimates either focus on a specific sector or lag behind real time by several years. We collected and analyzed 12 datasets and compared them to reveal the spatiotemporal changes and their uncertainties. We further estimated the emissions from 1990–2019, and the estimates showed a robust trend in recent years when compared to top-down results.
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The temporal variability of atmospheric emissions is linked to changes in activity patterns, emission processes and meteorology. Accounting for the change in temporal emission characteristics is a key aspect for modelling the trends of air pollutants. This work presents a dataset of global and European emission temporal profiles to be used for air quality modelling purposes. The profiles were constructed considering the influences of local sociodemographic factors and climatological conditions.
Erin E. McDuffie, Steven J. Smith, Patrick O'Rourke, Kushal Tibrewal, Chandra Venkataraman, Eloise A. Marais, Bo Zheng, Monica Crippa, Michael Brauer, and Randall V. Martin
Earth Syst. Sci. Data, 12, 3413–3442, https://doi.org/10.5194/essd-12-3413-2020, https://doi.org/10.5194/essd-12-3413-2020, 2020
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Global emission inventories are vital to understanding the impacts of air pollution on the environment, human health, and society. We update the open-source Community Emissions Data System (CEDS) to provide global gridded emissions of seven key air pollutants from 1970–2017 for 11 source sectors and multiple fuel types, including coal, solid biofuel, and liquid oil and natural gas. This dataset includes both monthly global gridded emissions and annual national totals.
Robbie M. Andrew
Earth Syst. Sci. Data, 12, 2411–2421, https://doi.org/10.5194/essd-12-2411-2020, https://doi.org/10.5194/essd-12-2411-2020, 2020
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India is the world's third-largest emitter of carbon dioxide and is developing rapidly. While India has pledged an emissions-intensity reduction as its contribution to the Paris Agreement, the country does not regularly report emissions statistics, making tracking progress difficult. Here I compile monthly energy and industrial activity data, allowing for the production of estimates of India's CO2 emissions by month and calendar year.
Robbie M. Andrew
Earth Syst. Sci. Data, 12, 1437–1465, https://doi.org/10.5194/essd-12-1437-2020, https://doi.org/10.5194/essd-12-1437-2020, 2020
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There are now several global datasets with estimates of global CO2 emissions from fossil sources, but the totals from these differ. Sometimes the range of these estimates has been used to indicate uncertainty in global emissions. In this paper I discuss the reasons why these datasets differ, particularly their different system boundaries: which emissions sources are included and which are omitted. Analysis is both qualitative and quantitative.
André-Marie Dendievel, Brice Mourier, Alexandra Coynel, Olivier Evrard, Pierre Labadie, Sophie Ayrault, Maxime Debret, Florence Koltalo, Yoann Copard, Quentin Faivre, Thomas Gardes, Sophia Vauclin, Hélène Budzinski, Cécile Grosbois, Thierry Winiarski, and Marc Desmet
Earth Syst. Sci. Data, 12, 1153–1170, https://doi.org/10.5194/essd-12-1153-2020, https://doi.org/10.5194/essd-12-1153-2020, 2020
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Polychlorinated biphenyl indicators (ΣPCBi) from sediment cores, bed and flood deposits, suspended particulate matter, and dredged sediments along the major French rivers (1945–2018) are compared with socio-hydrological drivers. ΣPCBi increased from 1945 to the 1990s due to urban and industrial emissions. It gradually decreased with the implementation of regulations. Specific ΣPCBi fluxes reveal the amount of PCB-polluted sediment transported by French rivers to European seas over 40 years.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Judith Hauck, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Dorothee C. E. Bakker, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Peter Anthoni, Leticia Barbero, Ana Bastos, Vladislav Bastrikov, Meike Becker, Laurent Bopp, Erik Buitenhuis, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Kim I. Currie, Richard A. Feely, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Daniel S. Goll, Nicolas Gruber, Sören Gutekunst, Ian Harris, Vanessa Haverd, Richard A. Houghton, George Hurtt, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Jed O. Kaplan, Etsushi Kato, Kees Klein Goldewijk, Jan Ivar Korsbakken, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Danica Lombardozzi, Gregg Marland, Patrick C. McGuire, Joe R. Melton, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Craig Neill, Abdirahman M. Omar, Tsuneo Ono, Anna Peregon, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Roland Séférian, Jörg Schwinger, Naomi Smith, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco N. Tubiello, Guido R. van der Werf, Andrew J. Wiltshire, and Sönke Zaehle
Earth Syst. Sci. Data, 11, 1783–1838, https://doi.org/10.5194/essd-11-1783-2019, https://doi.org/10.5194/essd-11-1783-2019, 2019
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The Global Carbon Budget 2019 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Robbie M. Andrew
Earth Syst. Sci. Data, 11, 1675–1710, https://doi.org/10.5194/essd-11-1675-2019, https://doi.org/10.5194/essd-11-1675-2019, 2019
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Global production of cement has grown very rapidly in recent years, and, after fossil fuels and land-use change, it is the third-largest source of society's emissions of carbon dioxide. This paper draws on a large variety of available datasets, prioritising official data and emission factors, to produce both global and country-level estimates of these
processemissions from cement production.
Cited articles
Ball, J. E., Anderson, D. T., and Chan, C. S.: Comprehensive survey of deep
learning in remote sensing: theories, tools, and challenges for the
community, J. Appl. Remote Sens., 11, 042609, https://doi.org/10.1117/1.JRS.11.042609,
2017.
Bódis, K., Kougias, I., Jäger-Waldau, A., Taylor, N., and
Szabó, S.: A high-resolution geospatial assessment of the rooftop solar
photovoltaic potential in the European Union, Renew. Sust. Energ. Rev., 114,
109309, https://doi.org/10.1016/j.rser.2019.109309, 2019.
Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H.: Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation, in: Computer Vision – ECCV 2018, edited by: Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y., Springer, Cham, Germany, 833–851, https://doi.org/10.1007/978-3-030-01234-2_49, 2018.
Chu, S. and Majumdar, A.: Opportunities and challenges for a sustainable
energy future, Nature, 488, 294–303, https://doi.org/10.1038/nature11475, 2012.
Golovko, V., Bezobrazov, S., Kroshchanka, A., Sachenko, A., Komar, M., and
Karachka, A.: Convolutional neural network based solar photovoltaic panel
detection in satellite photos, 2017 9th IEEE International Conference on
Intelligent Data Acquisition and Advanced Computing Systems: Technology and
Applications (IDAACS), Bucharest, Romania, 21–23 September 2017, 14–19, https://doi.org/10.1109/IDAACS.2017.8094501, 2017.
Hernandez, R. R., Hoffacker, M. K., Murphy-Mariscal, M. L., Wu, G. C., and
Allen, M. F.: Solar energy development impacts on land cover change and
protected areas, P. Natl. Acad. Sci. USA, 112, 13579,
https://doi.org/10.1073/pnas.1517656112, 2015.
House, D., Lech, M., and Stolar, M.: Using deep learning to identify
potential roof spaces for solar panels, 2018 12th International
Conference on Signal Processing and Communication Systems (ICSPCS), Cairns, Australia, 17–19 December 2018, 1–6,
https://doi.org/10.1109/ICSPCS.2018.8631725, 2018.
IRENA: Renewable capacity statistics 2021, International Renewable Energy
Agency (IRENA), Abu Dhabi, 2021.
Ji, S., Wei, S., and Lu, M.: Fully convolutional networks for multisource
building extraction from an open aerial and satellite imagery data set, IEEE
T. Geosci. Remote, 57, 574–586, https://doi.org/10.1109/TGRS.2018.2858817, 2019.
Ji, S., Zhang, Z., Zhang, C., Wei, S., Lu, M., and Duan, Y.: Learning
discriminative spatiotemporal features for precise crop classification from
multi-temporal satellite images, Int. J. Remote Sens., 41, 3162–3174,
https://doi.org/10.1080/01431161.2019.1699973, 2020.
Jiang, H., Yao, L., and Liu, Y.: Multi-resolution dataset for photovoltaic
panel segmentation from satellite and aerial imagery, Zenodo [data set],
https://doi.org/10.5281/zenodo.5171712, 2021.
Kabir, E., Kumar, P., Kumar, S., Adelodun, A. A., and Kim, K.-H.: Solar
energy: Potential and future prospects, Renew. Sust. Energ. Rev., 82,
894–900, https://doi.org/10.1016/j.rser.2017.09.094, 2018.
La Monaca, S. and Ryan, L.: Solar PV where the sun doesn't shine:
Estimating the economic impacts of support schemes for residential PV with
detailed net demand profiling, Energ. Policy, 108, 731–741,
https://doi.org/10.1016/j.enpol.2017.05.052, 2017.
Li, K., Wan, G., Cheng, G., Meng, L., and Han, J.: Object detection in
optical remote sensing images: A survey and a new benchmark, ISPRS J.
Photogramm., 159, 296–307, https://doi.org/10.1016/j.isprsjprs.2019.11.023, 2020.
Liang, S., Qi, F., Ding, Y., Cao, R., Yang, Q., and Yan, W.: Mask R-CNN
based segmentation method for satellite imagery of photovoltaics generation
systems, 2020 39th Chinese Control Conference (CCC), Shenyang, China, 27–29 July 2020, 5343–5348,
https://doi.org/10.23919/CCC50068.2020.9189474, 2020.
Lin, G., Milan, A., Shen, C., and Reid, I.: RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, USA, 21–26 July 2017, 5168–5177, https://doi.org/10.1109/CVPR.2017.549, 2017.
Liu, L., Sun, Q., Li, H., Yin, H., Ren, X., and Wennersten, R.: Evaluating
the benefits of Integrating Floating Photovoltaic and Pumped Storage Power
System, Energ. Convers. Manage., 194, 173–185,
https://doi.org/10.1016/j.enconman.2019.04.071, 2019.
Majumdar, D. and Pasqualetti, M. J.: Analysis of land availability for
utility-scale power plants and assessment of solar photovoltaic development
in the state of Arizona, USA, Renew. Energ., 134, 1213–1231,
https://doi.org/10.1016/j.renene.2018.08.064, 2019.
Malof, J. M., Rui, H., Collins, L. M., Bradbury, K., and Newell, R.: Automatic
solar photovoltaic panel detection in satellite imagery, 2015
International Conference on Renewable Energy Research and Applications
(ICRERA), 1428–1431, Palermo, Italy, 22–25 November 2015, https://doi.org/10.1109/ICRERA.2015.7418643, 2015.
Martins, F. R., Pereira, E. B., and Abreu, S. L.: Satellite-derived solar
resource maps for Brazil under SWERA project, Sol. Energy, 81, 517–528,
https://doi.org/10.1016/j.solener.2006.07.009, 2007.
Moutinho, V. and Robaina, M.: Is the share of renewable energy sources
determining the CO2 kWh and income relation in electricity generation?,
Renew. Sust. Energ. Rev., 65, 902–914, https://doi.org/10.1016/j.rser.2016.07.007,
2016.
Perez, R., Kmiecik, M., Herig, C., and Renné, D.: Remote monitoring of
PV performance using geostationary satellites, Sol. Energy, 71, 255–261,
https://doi.org/10.1016/S0038-092X(01)00050-0, 2001.
Peters, I. M., Liu, H., Reindl, T., and Buonassisi, T.: Global prediction of
photovoltaic field performance differences using open-source satellite data,
Joule, 2, 307–322, https://doi.org/10.1016/j.joule.2017.11.012, 2018.
Rabaia, M. K. H., Abdelkareem, M. A., Sayed, E. T., Elsaid, K., Chae, K.-J.,
Wilberforce, T., and Olabi, A. G.: Environmental impacts of solar energy
systems: A review, Sci. Total Environ., 754, 141989,
https://doi.org/10.1016/j.scitotenv.2020.141989, 2021.
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J.,
Carvalhais, N., and Prabhat: Deep learning and process understanding for
data-driven Earth system science, Nature, 566, 195–204,
https://doi.org/10.1038/s41586-019-0912-1, 2019.
Rico Espinosa, A., Bressan, M., and Giraldo, L. F.: Failure signature
classification in solar photovoltaic plants using RGB images and
convolutional neural networks, Renew. Energ., 162, 249–256,
https://doi.org/10.1016/j.renene.2020.07.154, 2020.
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, edited by: Navab N., Hornegger J., Wells W., and Frangi A., Springer, Cham, Germany, 234–241, https://doi.org/10.1007/978-3-319-24574-4_28, 2015.
Sacchelli, S., Garegnani, G., Geri, F., Grilli, G., Paletto, A., Zambelli,
P., Ciolli, M., and Vettorato, D.: Trade-off between photovoltaic systems
installation and agricultural practices on arable lands: An environmental
and socio-economic impact analysis for Italy, Land Use Policy, 56, 90–99,
https://doi.org/10.1016/j.landusepol.2016.04.024, 2016.
Shin, H., Hansen, K. U., and Jiao, F.: Techno-economic assessment of
low-temperature carbon dioxide electrolysis, Nat. Sustain., 4, 911–919,
https://doi.org/10.1038/s41893-021-00739-x, 2021.
Song, Y., Wu, W., Liu, Z., Yang, X., Liu, K., and Lu, W.: An Adaptive Pansharpening Method by Using Weighted Least Squares Filter, IEEE Geosci. Remote. Sens. Lett., 13, 18–22, https://doi.org/10.1109/LGRS.2015.2492569, 2016.
Wang, M., Cui, Q., Sun, Y., and Wang, Q.: Photovoltaic panel extraction from
very high-resolution aerial imagery using region–line primitive association
analysis and template matching, ISPRS J. Photogramm., 141, 100–111,
https://doi.org/10.1016/j.isprsjprs.2018.04.010, 2018.
Xia, G., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M.,
Pelillo, M., and Zhang, L.: DOTA: A large-scale dataset for object detection
in aerial images, 2018 IEEE/CVF Conference on Computer Vision and
Pattern Recognition, Salt Lake City, USA, 18–23 June 2018, 3974–3983, https://doi.org/10.1109/CVPR.2018.00418, 2018.
Yan, J. Y., Yang, Y., Campana, P. E., and He, J. J.: City-level analysis of
subsidy-free solar photovoltaic electricity price, profits and grid parity
in China, Nat. Energy, 4, 709–717, https://doi.org/10.1038/s41560-019-0441-z, 2019.
Yao, Y. and Hu, Y.: Recognition and location of solar panels based on
machine vision, 2017 2nd Asia-Pacific Conference on Intelligent Robot
Systems (ACIRS), Wuhan, China, 16–19 June 2017, 7–12, https://doi.org/10.1109/ACIRS.2017.7986055, 2017.
Yu, J., Wang, Z., Majumdar, A., and Rajagopal, R.: DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States, Joule, 2, 2605–2617, https://doi.org/10.1016/j.joule.2018.11.021, 2018.
Zambrano-Asanza, S., Quiros-Tortos, J., and Franco, J. F.: Optimal site
selection for photovoltaic power plants using a GIS-based multi-criteria
decision making and spatial overlay with electric load, Renew. Sust. Energ.
Rev., 143, 110853, https://doi.org/10.1016/j.rser.2021.110853, 2021.
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...
