Articles | Volume 14, issue 9
https://doi.org/10.5194/essd-14-4057-2022
© Author(s) 2022. 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-14-4057-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
06 Sep 2022
Data description paper |
| 06 Sep 2022
| 06 Sep 2022
Vectorized dataset of roadside noise barriers in China using street view imagery
Zhen Qian
Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing 210023, China
State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing 210023, China
State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Jiangsu Provincial Key Laboratory for NSLSCS, School of Mathematical Science, Nanjing Normal University, Nanjing 210023, China
Yue Yang
Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing 210023, China
State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Teng Zhong
Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing 210023, China
State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Fan Zhang
Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Rui Zhu
Department of Land Surveying and Geo-Informatics, The Hong Kong
Polytechnic University, Kowloon, Hong Kong, China
Kai Zhang
Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing 210023, China
State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Zhixin Zhang
College of Geography & Marine, Nanjing University, Nanjing, P.O. Box 2100913, China
Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing 210023, China
Zhuo Sun
Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing 210023, China
State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Peilong Ma
Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing 210023, China
State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Guonian Lü
Key Laboratory of Virtual Geographic Environment (Ministry of Education of PRC), Nanjing Normal University, Nanjing 210023, China
State Key Laboratory Cultivation Base of Geographical Environment Evolution, Nanjing 210023, China
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
Yu Ye
Department of Architecture, College of Architecture and Urban Planning, Tongji University, China
Jinyue Yan
Future Energy Center, Malardalen University, 72123 Vasteras, Sweden
Department of Chemical Engineering, KTH Royal Institute of
Technology, Stockholm 10044, Sweden
Related subject area
Domain: ESSD – Land | Subject: Land Cover and Land Use
ChinaWheatYield30m: a 30 m annual winter wheat yield dataset from 2016 to 2021 in China
Refined fine-scale mapping of tree cover using time series of Planet-NICFI and Sentinel-1 imagery for Southeast Asia (2016–2021)
High-resolution global map of closed-canopy coconut palm
High-resolution land use and land cover dataset for regional climate modelling: historical and future changes in Europe
Global urban fractional changes at a 1 km resolution throughout 2100 under eight scenarios of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs)
China Building Rooftop Area: the first multi-annual (2016–2021) and high-resolution (2.5 m) building rooftop area dataset in China derived with super-resolution segmentation from Sentinel-2 imagery
High-resolution distribution maps of single-season rice in China from 2017 to 2022
Mapping global non-floodplain wetlands
A new cropland area database by country circa 2020
FORMS: Forest Multiple Source height, wood volume, and biomass maps in France at 10 to 30 m resolution based on Sentinel-1, Sentinel-2, and GEDI data with a deep learning approach
An improved global land cover mapping in 2015 with 30 m resolution (GLC-2015) based on a multisource product-fusion approach
Annual emissions of carbon from land use, land-use change, and forestry from 1850 to 2020
LCM2021 – The UK Land Cover Map 2021
An open-source automatic survey of green roofs in London using segmentation of aerial imagery
Twenty-meter annual paddy rice area map for mainland Southeast Asia using Sentinel-1 synthetic-aperture-radar data
A 29-year time series of annual 300 m resolution plant-functional-type maps for climate models
SinoLC-1: the first 1-meter resolution national-scale land-cover map of China created with the deep learning framework and open-access data
Estimating local agricultural gross domestic product (AgGDP) across the world
Classification and mapping of European fuels using a hierarchical, multipurpose fuel classification system
Harmonising the land-use flux estimates of global models and national inventories for 2000–2020
Four-century history of land transformation by humans in the United States (1630–2020): annual and 1 km grid data for the HIStory of LAND changes (HISLAND-US)
HISDAC-ES: Historical Settlement Data Compilation for Spain (1900–2020)
A 250 m annual alpine grassland AGB dataset over the Qinghai–Tibet Plateau (2000–2019) in China based on in situ measurements, UAV photos, and MODIS data
AsiaRiceYield4km: seasonal rice yield in Asia from 1995 to 2015
TreeSatAI Benchmark Archive: a multi-sensor, multi-label dataset for tree species classification in remote sensing
UGS-1m: fine-grained urban green space mapping of 31 major cities in China based on the deep learning framework
AI4Boundaries: an open AI-ready dataset to map field boundaries with Sentinel-2 and aerial photography
GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020
CALC-2020: a new baseline land cover map at 10 m resolution for the circumpolar Arctic
MDAS: a new multimodal benchmark dataset for remote sensing
Gridded pollen-based Holocene regional plant cover in temperate and northern subtropical China suitable for climate modelling
Location, biophysical and agronomic parameters for croplands in northern Ghana
Historical nitrogen fertilizer use in China from 1952 to 2018
SiDroForest: a comprehensive forest inventory of Siberian boreal forest investigations including drone-based point clouds, individually labeled trees, synthetically generated tree crowns, and Sentinel-2 labeled image patches
History of anthropogenic Nitrogen inputs (HaNi) to the terrestrial biosphere: a 5 arcmin resolution annual dataset from 1860 to 2019
LUCAS cover photos 2006–2018 over the EU: 874 646 spatially distributed geo-tagged close-up photos with land cover and plant species label
Gridded 5 arcmin datasets for simultaneously farm-size-specific and crop-specific harvested areas in 56 countries
A global map of local climate zones to support earth system modelling and urban-scale environmental science
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Yu Zhao, Shaoyu Han, Jie Zheng, Hanyu Xue, Zhenhai Li, Yang Meng, Xuguang Li, Xiaodong Yang, Zhenhong Li, Shuhong Cai, and Guijun Yang
Earth Syst. Sci. Data, 15, 4047–4063, https://doi.org/10.5194/essd-15-4047-2023, https://doi.org/10.5194/essd-15-4047-2023, 2023
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In the present study, we generated a 30 m Chinese winter wheat yield dataset from 2016 to 2021, called ChinaWheatYield30m. The dataset has high spatial resolution and great accuracy. It is the highest-resolution yield dataset known. Such a dataset will provide basic knowledge of detailed wheat yield distribution, which can be applied for many purposes including crop production modeling or regional climate evaluation.
Feng Yang and Zhenzhong Zeng
Earth Syst. Sci. Data, 15, 4011–4021, https://doi.org/10.5194/essd-15-4011-2023, https://doi.org/10.5194/essd-15-4011-2023, 2023
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We generated a 4.77 m resolution annual tree cover map product for Southeast Asia (SEA) for 2016–2021 using Planet-NICFI and Sentinel-1 imagery. Maps were created with good accuracy and high consistency during 2016–2021. The baseline maps at 4.77 m can be converted to forest cover maps for SEA at various resolutions to meet different users’ needs. Our products can help resolve rounding errors in forest cover mapping by counting isolated trees and monitoring long, narrow forest cover removal.
Adrià Descals, Serge Wich, Zoltan Szantoi, Matthew J. Struebig, Rona Dennis, Zoe Hatton, Thina Ariffin, Nabillah Unus, David L. A. Gaveau, and Erik Meijaard
Earth Syst. Sci. Data, 15, 3991–4010, https://doi.org/10.5194/essd-15-3991-2023, https://doi.org/10.5194/essd-15-3991-2023, 2023
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The spatial extent of coconut palm is understudied despite its increasing demand and associated impacts. We present the first global coconut palm layer at 20 m resolution. The layer was produced using deep learning and remotely sensed data. The global coconut area estimate is 12.31 Mha for dense coconut palm, but the estimate is 3 times larger when sparse coconut palm is considered. This means that coconut production can likely increase on the lands currently allocated to coconut palm.
Peter Hoffmann, Vanessa Reinhart, Diana Rechid, Nathalie de Noblet-Ducoudré, Edouard L. Davin, Christina Asmus, Benjamin Bechtel, Jürgen Böhner, Eleni Katragkou, and Sebastiaan Luyssaert
Earth Syst. Sci. Data, 15, 3819–3852, https://doi.org/10.5194/essd-15-3819-2023, https://doi.org/10.5194/essd-15-3819-2023, 2023
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This paper introduces the new high-resolution land use and land cover change dataset LUCAS LUC for Europe (version 1.1), tailored for use in regional climate models. Historical and projected future land use change information from the Land-Use Harmonization 2 (LUH2) dataset is translated into annual plant functional type changes from 1950 to 2015 and 2016 to 2100, respectively, by employing a newly developed land use translator.
Wanru He, Xuecao Li, Yuyu Zhou, Zitong Shi, Guojiang Yu, Tengyun Hu, Yixuan Wang, Jianxi Huang, Tiecheng Bai, Zhongchang Sun, Xiaoping Liu, and Peng Gong
Earth Syst. Sci. Data, 15, 3623–3639, https://doi.org/10.5194/essd-15-3623-2023, https://doi.org/10.5194/essd-15-3623-2023, 2023
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Most existing global urban products with future projections were developed in urban and non-urban categories, which ignores the gradual change of urban development at the local scale. Using annual global urban extent data from 1985 to 2015, we forecasted global urban fractional changes under eight scenarios throughout 2100. The developed dataset can provide spatially explicit information on urban fractions at 1 km resolution, which helps support various urban studies (e.g., urban heat island).
Zeping Liu, Hong Tang, Lin Feng, and Siqing Lyu
Earth Syst. Sci. Data, 15, 3547–3572, https://doi.org/10.5194/essd-15-3547-2023, https://doi.org/10.5194/essd-15-3547-2023, 2023
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Large-scale maps of building rooftop area (BRA) are crucial for addressing policy decisions and sustainable development. In this paper, we propose a deep-learning method for high-resolution BRA mapping (2.5 m) from Sentinel-2 imagery (10 m). The resulting China building rooftop area dataset (CBRA) is the first multi-annual (2016–2021) and high-resolution (2.5 m) BRA dataset in China. Cross-comparisons show that the CBRA achieves the best performance in capturing the spatiotemporal information.
Ruoque Shen, Baihong Pan, Qiongyan Peng, Jie Dong, Xuebing Chen, Xi Zhang, Tao Ye, Jianxi Huang, and Wenping Yuan
Earth Syst. Sci. Data, 15, 3203–3222, https://doi.org/10.5194/essd-15-3203-2023, https://doi.org/10.5194/essd-15-3203-2023, 2023
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Paddy rice is the second-largest grain crop in China and plays an important role in ensuring global food security. This study developed a new rice-mapping method and produced distribution maps of single-season rice in 21 provincial administrative regions of China from 2017 to 2022 at a 10 or 20 m resolution. The accuracy was examined using 108 195 survey samples and county-level statistical data, and we found that the distribution maps have good accuracy.
Charles R. Lane, Ellen D'Amico, Jay R. Christensen, Heather E. Golden, Qiusheng Wu, and Adnan Rajib
Earth Syst. Sci. Data, 15, 2927–2955, https://doi.org/10.5194/essd-15-2927-2023, https://doi.org/10.5194/essd-15-2927-2023, 2023
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Non-floodplain wetlands (NFWs) – wetlands located outside floodplains – confer watershed-scale resilience to hydrological, biogeochemical, and biotic disturbances. Although they are frequently unmapped, we identified ~ 33 million NFWs covering > 16 × 10 km2 across the globe. NFWs constitute the majority of the world's wetlands (53 %). Despite their small size (median 0.039 km2), these imperiled systems have an outsized impact on watershed functions and sustainability and require protection.
Francesco Nicola Tubiello, Giulia Conchedda, Leon Casse, Pengyu Hao, Giorgia De Santis, and Zhongxin Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-211, https://doi.org/10.5194/essd-2023-211, 2023
Revised manuscript accepted for ESSD
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We describe a new dataset of cropland area circa the year 2020, with global coverageand country detial. Data are generated from geospatial information on the agreement characteristics of six high-resolution cropland maps. By helping to highlight features of cropland characteristics and underlying causes for agreement across land cover products, the dataset can be used as a tool to help guide future mapping efforts towards improved agricultural monitoring.
Martin Schwartz, Philippe Ciais, Aurélien De Truchis, Jérôme Chave, Catherine Ottlé, Cedric Vega, Jean-Pierre Wigneron, Manuel Nicolas, Sami Jouaber, Siyu Liu, Martin Brandt, and Ibrahim Fayad
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-196, https://doi.org/10.5194/essd-2023-196, 2023
Revised manuscript accepted for ESSD
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As forests play a key role in climate-related issues, their accurate monitoring is critical to reduce global carbon emissions effectively. Based on open-access remote sensing technologies and artificial intelligence methods, we created high-resolution tree height and biomass maps of Metropolitan France that outperform previous products available. This study, based on freely available data, brings essential information to support climate-efficient forest management policies at low-cost.
Bingjie Li, Xiaocong Xu, Xiaoping Liu, Qian Shi, Haoming Zhuang, Yaotong Cai, and Da He
Earth Syst. Sci. Data, 15, 2347–2373, https://doi.org/10.5194/essd-15-2347-2023, https://doi.org/10.5194/essd-15-2347-2023, 2023
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A global land cover map with fine spatial resolution is important for climate and environmental studies, food security, or biodiversity conservation. In this study, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing the existing land cover products based on the Dempster–Shafer theory of evidence on the Google Earth Engine platform. The GLC-2015 performed well, with an OA of 79.5 % (83.6 %) assessed with the global point-based (patch-based) samples.
Richard A. Houghton and Andrea Castanho
Earth Syst. Sci. Data, 15, 2025–2054, https://doi.org/10.5194/essd-15-2025-2023, https://doi.org/10.5194/essd-15-2025-2023, 2023
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We update a previous analysis of carbon emissions (annual and national) from land use, land-use change, and forestry from 1850 to 2020. We use data from the latest (2020) Global Forest Resources Assessment, incorporate shifting cultivation, and include improvements to the bookkeeping model and recent estimates of emissions from peatlands. Net global emissions declined steadily over the decade from 2011 to 2020 (mean of 0.96 Pg C yr−1), falling below 1.0 Pg C yr−1 for the first time in 30 years.
Christopher G. Marston, Aneurin W. O'Neil, R. Daniel Morton, Claire M. Wood, and Clare S. Rowland
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-78, https://doi.org/10.5194/essd-2023-78, 2023
Revised manuscript accepted for ESSD
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The UK Land Cover Map 2021 (LCM2021) is a UK-wide land cover data set, with 21- and 10-class versions. It is intended to support a broad range of UK environmental research, including ecological and hydrological research. LCM2021 was produced by classifying Sentinel-2 satellite imagery. LCM2021 is distributed as a suite of products to facilitate easy-use for a range of applications. To support research at different spatial scales it includes 10 m, 25 m and 1 km resolution products.
Charles H. Simpson, Oscar Brousse, Nahid Mohajeri, Michael Davies, and Clare Heaviside
Earth Syst. Sci. Data, 15, 1521–1541, https://doi.org/10.5194/essd-15-1521-2023, https://doi.org/10.5194/essd-15-1521-2023, 2023
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Adding plants to roofs of buildings can reduce indoor and outdoor temperatures and so can reduce urban overheating, which is expected to increase due to climate change and urban growth. To better understand the effect this has on the urban environment, we need data on how many buildings have green roofs already.
We used a computer vision model to find green roofs in aerial imagery in London, producing a dataset identifying what buildings have green roofs and improving on previous methods.
Chunling Sun, Hong Zhang, Lu Xu, Ji Ge, Jingling Jiang, Lijun Zuo, and Chao Wang
Earth Syst. Sci. Data, 15, 1501–1520, https://doi.org/10.5194/essd-15-1501-2023, https://doi.org/10.5194/essd-15-1501-2023, 2023
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Over 90 % of the world’s rice is produced in the Asia–Pacific region. In this study, a rice-mapping method based on Sentinel-1 data for mainland Southeast Asia is proposed. A combination of spatiotemporal features with strong generalization is selected and input into the U-Net model to obtain a 20 m resolution rice area map of mainland Southeast Asia in 2019. The accuracy of the proposed method is 92.20 %. The rice area map is concordant with statistics and other rice area maps.
Kandice L. Harper, Céline Lamarche, Andrew Hartley, Philippe Peylin, Catherine Ottlé, Vladislav Bastrikov, Rodrigo San Martín, Sylvia I. Bohnenstengel, Grit Kirches, Martin Boettcher, Roman Shevchuk, Carsten Brockmann, and Pierre Defourny
Earth Syst. Sci. Data, 15, 1465–1499, https://doi.org/10.5194/essd-15-1465-2023, https://doi.org/10.5194/essd-15-1465-2023, 2023
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We built a spatially explicit annual plant-functional-type (PFT) dataset for 1992–2020 exhibiting intra-class spatial variability in PFT fractional cover at 300 m. For each year, 14 maps of percentage cover are produced: bare soil, water, permanent snow/ice, built, managed grasses, natural grasses, and trees and shrubs, each split into leaf type and seasonality. Model simulations indicate significant differences in simulated carbon, water, and energy fluxes in some regions using this new set.
Zhuohong Li, Wei He, Mofan Cheng, Jingxin Hu, Guangyi Yang, and Hongyan Zhang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-87, https://doi.org/10.5194/essd-2023-87, 2023
Revised manuscript accepted for ESSD
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Nowadays, the very-high-resolution land-cover (LC) map with national coverage is still unavailable in China, hindering efficient resource allocation. To fill this gap, the first 1-meter resolution LC map of China, SinoLC-1, was built. The results showed SinoLC-1 had an overall accuracy of 73.61 % and conformed closely to the official survey reports. The comparison with other datasets suggests SinoLC-1 can be a better support for downstream applications and provide more accurate info to users.
Yating Ru, Brian Blankespoor, Ulrike Wood-Sichra, Timothy S. Thomas, Liangzhi You, and Erwin Kalvelagen
Earth Syst. Sci. Data, 15, 1357–1387, https://doi.org/10.5194/essd-15-1357-2023, https://doi.org/10.5194/essd-15-1357-2023, 2023
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Economic statistics are frequently produced at an administrative level that lacks detail to examine development patterns and the exposure to natural hazards. This paper disaggregates national and subnational administrative statistics of agricultural GDP into a global dataset at the local level using satellite-derived indicators. As an illustration, the paper estimates that the exposure of areas with extreme drought to agricultural GDP is USD 432 billion, where nearly 1.2 billion people live.
Elena Aragoneses, Mariano García, Michele Salis, Luís M. Ribeiro, and Emilio Chuvieco
Earth Syst. Sci. Data, 15, 1287–1315, https://doi.org/10.5194/essd-15-1287-2023, https://doi.org/10.5194/essd-15-1287-2023, 2023
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We present a new hierarchical fuel classification system with a total of 85 fuels that is useful for preventing fire risk at different spatial scales. Based on this, we developed a European fuel map (1 km resolution) using land cover datasets, biogeographic datasets, and bioclimatic modelling. We validated the map by comparing it to high-resolution data, obtaining high overall accuracy. Finally, we developed a crosswalk for standard fuel models as a first assignment of fuel parameters.
Giacomo Grassi, Clemens Schwingshackl, Thomas Gasser, Richard A. Houghton, Stephen Sitch, Josep G. Canadell, Alessandro Cescatti, Philippe Ciais, Sandro Federici, Pierre Friedlingstein, Werner A. Kurz, Maria J. Sanz Sanchez, Raúl Abad Viñas, Ramdane Alkama, Selma Bultan, Guido Ceccherini, Stefanie Falk, Etsushi Kato, Daniel Kennedy, Jürgen Knauer, Anu Korosuo, Joana Melo, Matthew J. McGrath, Julia E. M. S. Nabel, Benjamin Poulter, Anna A. Romanovskaya, Simone Rossi, Hanqin Tian, Anthony P. Walker, Wenping Yuan, Xu Yue, and Julia Pongratz
Earth Syst. Sci. Data, 15, 1093–1114, https://doi.org/10.5194/essd-15-1093-2023, https://doi.org/10.5194/essd-15-1093-2023, 2023
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Striking differences exist in estimates of land-use CO2 fluxes between the national greenhouse gas inventories and the IPCC assessment reports. These differences hamper an accurate assessment of the collective progress under the Paris Agreement. By implementing an approach that conceptually reconciles land-use CO2 flux from national inventories and the global models used by the IPCC, our study is an important step forward for increasing confidence in land-use CO2 flux estimates.
Xiaoyong Li, Hanqin Tian, Chaoqun Lu, and Shufen Pan
Earth Syst. Sci. Data, 15, 1005–1035, https://doi.org/10.5194/essd-15-1005-2023, https://doi.org/10.5194/essd-15-1005-2023, 2023
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We reconstructed land use and land cover (LULC) history for the conterminous United States during 1630–2020 by integrating multi-source data. The results show the widespread expansion of cropland and urban land and the shrinking of natural vegetation in the past four centuries. Forest planting and regeneration accelerated forest recovery since the 1920s. The datasets can be used to assess the LULC impacts on the ecosystem's carbon, nitrogen, and water cycles.
Johannes H. Uhl, Dominic Royé, Keith Burghardt, José Antonio Aldrey Vázquez, Manuel Borobio Sanchiz, and Stefan Leyk
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-53, https://doi.org/10.5194/essd-2023-53, 2023
Revised manuscript accepted for ESSD
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Historical, fine-grained geospatial datasets on built-up areas are rarely available, constraining studies of urbanization, settlement evolution, or the dynamics of human-environment interactions to recent decades. In order to provide such historical data, we used publicly available cadastral building data for Spain and created a series of gridded surfaces, measuring age, physical, and land use related features of the built environment in Spain, and the evolution of settlements from 1900 to 2020.
Huifang Zhang, Zhonggang Tang, Binyao Wang, Hongcheng Kan, Yi Sun, Yu Qin, Baoping Meng, Meng Li, Jianjun Chen, Yanyan Lv, Jianguo Zhang, Shuli Niu, and Shuhua Yi
Earth Syst. Sci. Data, 15, 821–846, https://doi.org/10.5194/essd-15-821-2023, https://doi.org/10.5194/essd-15-821-2023, 2023
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The accuracy of regional grassland aboveground biomass (AGB) is always limited by insufficient ground measurements and large spatial gaps with satellite pixels. This paper used more than 37 000 UAV images as bridges to successfully obtain AGB values matching MODIS pixels. The new AGB estimation model had good robustness, with an average R2 of 0.83 and RMSE of 34.13 g m2. Our new dataset provides important input parameters for understanding the Qinghai–Tibet Plateau during global climate change.
Huaqing Wu, Jing Zhang, Zhao Zhang, Jichong Han, Juan Cao, Liangliang Zhang, Yuchuan Luo, Qinghang Mei, Jialu Xu, and Fulu Tao
Earth Syst. Sci. Data, 15, 791–808, https://doi.org/10.5194/essd-15-791-2023, https://doi.org/10.5194/essd-15-791-2023, 2023
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High-spatiotemporal-resolution rice yield datasets are limited over a large region. We proposed an explicit method to predict rice yield based on machine learning methods and generated a seasonal 4 km resolution rice yield dataset across Asia (AsiaRiceYield4km) for 1995–2015. The seasonal rice yield accuracy of AsiaRiceYield4km is high and much improved compared with previous datasets. AsiaRiceYield4km will fill the current data gap and better support agricultural monitoring systems.
Steve Ahlswede, Christian Schulz, Christiano Gava, Patrick Helber, Benjamin Bischke, Michael Förster, Florencia Arias, Jörn Hees, Begüm Demir, and Birgit Kleinschmit
Earth Syst. Sci. Data, 15, 681–695, https://doi.org/10.5194/essd-15-681-2023, https://doi.org/10.5194/essd-15-681-2023, 2023
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Imagery from air and space is the primary source of large-scale forest mapping. Our study introduces a new dataset with over 50000 image patches prepared for deep learning tasks. We show how the information for 20 European tree species can be extracted from different remote sensing sensors. Our algorithms can detect single species with precision scores up to 88 %. With a pixel size of 20×20 cm, forestry administration can now derive large-scale tree species maps at a very high resolution.
Qian Shi, Mengxi Liu, Andrea Marinoni, and Xiaoping Liu
Earth Syst. Sci. Data, 15, 555–577, https://doi.org/10.5194/essd-15-555-2023, https://doi.org/10.5194/essd-15-555-2023, 2023
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A large-scale and high-resolution urban green space (UGS) product with 1 m of 31 major cities in China (UGS-1m) is generated based on a deep learning framework to provide basic UGS information for relevant UGS research, such as distribution, area, and UGS rate. Moreover, an urban green space dataset (UGSet) with a total of 4454 samples of 512 × 512 in size are also supplied as the benchmark to support model training and algorithm comparison.
Raphaël d'Andrimont, Martin Claverie, Pieter Kempeneers, Davide Muraro, Momchil Yordanov, Devis Peressutti, Matej Batič, and François Waldner
Earth Syst. Sci. Data, 15, 317–329, https://doi.org/10.5194/essd-15-317-2023, https://doi.org/10.5194/essd-15-317-2023, 2023
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AI4boundaries is an open AI-ready data set to map field boundaries with Sentinel-2 and aerial photography provided with harmonised labels covering seven countries and 2.5 M parcels in Europe.
Xiao Zhang, Liangyun Liu, Tingting Zhao, Xidong Chen, Shangrong Lin, Jinqing Wang, Jun Mi, and Wendi Liu
Earth Syst. Sci. Data, 15, 265–293, https://doi.org/10.5194/essd-15-265-2023, https://doi.org/10.5194/essd-15-265-2023, 2023
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An accurate global 30 m wetland dataset that can simultaneously cover inland and coastal zones is lacking. This study proposes a novel method for wetland mapping and generates the first global 30 m wetland map with a fine classification system (GWL_FCS30), including five inland wetland sub-categories (permanent water, swamp, marsh, flooded flat and saline) and three coastal wetland sub-categories (mangrove, salt marsh and tidal flats).
Chong Liu, Xiaoqing Xu, Xuejie Feng, Xiao Cheng, Caixia Liu, and Huabing Huang
Earth Syst. Sci. Data, 15, 133–153, https://doi.org/10.5194/essd-15-133-2023, https://doi.org/10.5194/essd-15-133-2023, 2023
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Rapid Arctic changes are increasingly influencing human society, both locally and globally. Land cover offers a basis for characterizing the terrestrial world, yet spatially detailed information on Arctic land cover is lacking. We employ multi-source data to develop a new land cover map for the circumpolar Arctic. Our product reveals regionally contrasting biome distributions not fully documented in existing studies and thus enhances our understanding of the Arctic’s terrestrial system.
Jingliang Hu, Rong Liu, Danfeng Hong, Andrés Camero, Jing Yao, Mathias Schneider, Franz Kurz, Karl Segl, and Xiao Xiang Zhu
Earth Syst. Sci. Data, 15, 113–131, https://doi.org/10.5194/essd-15-113-2023, https://doi.org/10.5194/essd-15-113-2023, 2023
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Multimodal data fusion is an intuitive strategy to break the limitation of individual data in Earth observation. Here, we present a multimodal data set, named MDAS, consisting of synthetic aperture radar (SAR), multispectral, hyperspectral, digital surface model (DSM), and geographic information system (GIS) data for the city of Augsburg, Germany, along with baseline models for resolution enhancement, spectral unmixing, and land cover classification, three typical remote sensing applications.
Furong Li, Marie-José Gaillard, Xianyong Cao, Ulrike Herzschuh, Shinya Sugita, Jian Ni, Yan Zhao, Chengbang An, Xiaozhong Huang, Yu Li, Hongyan Liu, Aizhi Sun, and Yifeng Yao
Earth Syst. Sci. Data, 15, 95–112, https://doi.org/10.5194/essd-15-95-2023, https://doi.org/10.5194/essd-15-95-2023, 2023
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The objective of this study is present the first gridded and temporally continuous quantitative plant-cover reconstruction for temperate and northern subtropical China over the last 12 millennia. The reconstructions are based on 94 pollen records and include estimates for 27 plant taxa, 10 plant functional types, and 3 land-cover types. The dataset is suitable for palaeoclimate modelling and the evaluation of simulated past vegetation cover and anthropogenic land-cover change from models.
Jose Luis Gómez-Dans, Philip Edward Lewis, Feng Yin, Kofi Asare, Patrick Lamptey, Kenneth Kobina Yedu Aidoo, Dilys Sefakor MacCarthy, Hongyuan Ma, Qingling Wu, Martin Addi, Stephen Aboagye-Ntow, Caroline Edinam Doe, Rahaman Alhassan, Isaac Kankam-Boadu, Jianxi Huang, and Xuecao Li
Earth Syst. Sci. Data, 14, 5387–5410, https://doi.org/10.5194/essd-14-5387-2022, https://doi.org/10.5194/essd-14-5387-2022, 2022
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We provide a data set to support mapping croplands in smallholder landscapes in Ghana. The data set contains information on crop location on three agroecological zones for 2 years, temporal series of measurements of leaf area index and leaf chlorophyll concentration for maize canopies and yield. We demonstrate the use of these data to validate cropland masks, create a maize mask using satellite data and explore the relationship between satellite measurements and yield.
Zhen Yu, Jing Liu, and Giri Kattel
Earth Syst. Sci. Data, 14, 5179–5194, https://doi.org/10.5194/essd-14-5179-2022, https://doi.org/10.5194/essd-14-5179-2022, 2022
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We developed a 5 km annual nitrogen (N) fertilizer use dataset in China, covering the period from 1952 to 2018. We found that previous FAO-data-based N fertilizer products overestimated the N use in low, but underestimated in high, cropland coverage areas in China. The new dataset has improved the spatial distribution and corrected the existing biases, which is beneficial for biogeochemical cycle simulations in China, such as the assessment of greenhouse gas emissions and food production.
Femke van Geffen, Birgit Heim, Frederic Brieger, Rongwei Geng, Iuliia A. Shevtsova, Luise Schulte, Simone M. Stuenzi, Nadine Bernhardt, Elena I. Troeva, Luidmila A. Pestryakova, Evgenii S. Zakharov, Bringfried Pflug, Ulrike Herzschuh, and Stefan Kruse
Earth Syst. Sci. Data, 14, 4967–4994, https://doi.org/10.5194/essd-14-4967-2022, https://doi.org/10.5194/essd-14-4967-2022, 2022
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SiDroForest is an attempt to remedy data scarcity regarding vegetation data in the circumpolar region, whilst providing adjusted and labeled data for machine learning and upscaling practices. SiDroForest contains four datasets that include SfM point clouds, individually labeled trees, synthetic tree crowns and labeled Sentinel-2 patches that provide insights into the vegetation composition and forest structure of two important vegetation transition zones in Siberia, Russia.
Hanqin Tian, Zihao Bian, Hao Shi, Xiaoyu Qin, Naiqing Pan, Chaoqun Lu, Shufen Pan, Francesco N. Tubiello, Jinfeng Chang, Giulia Conchedda, Junguo Liu, Nathaniel Mueller, Kazuya Nishina, Rongting Xu, Jia Yang, Liangzhi You, and Bowen Zhang
Earth Syst. Sci. Data, 14, 4551–4568, https://doi.org/10.5194/essd-14-4551-2022, https://doi.org/10.5194/essd-14-4551-2022, 2022
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Nitrogen is one of the critical nutrients for growth. Evaluating the change in nitrogen inputs due to human activity is necessary for nutrient management and pollution control. In this study, we generated a historical dataset of nitrogen input to land at the global scale. This dataset consists of nitrogen fertilizer, manure, and atmospheric deposition inputs to cropland, pasture, and rangeland at high resolution from 1860 to 2019.
Raphaël d'Andrimont, Momchil Yordanov, Laura Martinez-Sanchez, Peter Haub, Oliver Buck, Carsten Haub, Beatrice Eiselt, and Marijn van der Velde
Earth Syst. Sci. Data, 14, 4463–4472, https://doi.org/10.5194/essd-14-4463-2022, https://doi.org/10.5194/essd-14-4463-2022, 2022
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Between 2006 and 2018, 875 661 LUCAS cover (i.e. close-up) photos were taken over a systematic sample of the European Union. This geo-located photo dataset has been curated and is being made available along with the surveyed label data, including land cover and plant species.
Han Su, Bárbara Willaarts, Diana Luna-Gonzalez, Maarten S. Krol, and Rick J. Hogeboom
Earth Syst. Sci. Data, 14, 4397–4418, https://doi.org/10.5194/essd-14-4397-2022, https://doi.org/10.5194/essd-14-4397-2022, 2022
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There are over 608 million farms around the world but they are not the same. We developed high spatial resolution maps showing where small and large farms were located and which crops were planted for 56 countries. We checked the reliability and have the confidence to use them for the country level and global studies. Our maps will help more studies to easily measure how agriculture policies, water availability, and climate change affect small and large farms.
Matthias Demuzere, Jonas Kittner, Alberto Martilli, Gerald Mills, Christian Moede, Iain D. Stewart, Jasper van Vliet, and Benjamin Bechtel
Earth Syst. Sci. Data, 14, 3835–3873, https://doi.org/10.5194/essd-14-3835-2022, https://doi.org/10.5194/essd-14-3835-2022, 2022
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Because urban areas are key contributors to climate change but are also susceptible to multiple hazards, one needs spatially detailed information on urban landscapes to support environmental services. This global local climate zone map describes this much-needed intra-urban heterogeneity across the whole surface of the earth in a universal language and can serve as a basic infrastructure to study e.g. environmental hazards, energy demand, and climate adaptation and mitigation solutions.
Xin Huang, Jie Yang, Wenrui Wang, and Zhengrong Liu
Earth Syst. Sci. Data, 14, 3649–3672, https://doi.org/10.5194/essd-14-3649-2022, https://doi.org/10.5194/essd-14-3649-2022, 2022
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Using more than 2.7 million Sentinel images, we proposed a global ISA mapping method and produced the 10-m global ISA dataset (GISA-10m), with overall accuracy exceeding 86 %. The inter-comparison between different global ISA datasets showed the superiority of our results. The ISA distribution at urban and rural was discussed and compared. For the first time, courtesy of the high spatial resolution, the global road ISA was further identified, and its distribution was discussed.
Cited articles
Abdulkareem, M., Havukainen, J., Nuortila-Jokinen, J., and Horttanainen, M.:
Life cycle assessment of a low-height noise barrier for railway traffic
noise, J. Clean. Prod., 323, 129169,
https://doi.org/10.1016/j.jclepro.2021.129169, 2021.
Apparicio, P., Carrier, M., Gelb, J., Séguin, A.-M., and Kingham, S.:
Cyclists' exposure to air pollution and road traffic noise in central city
neighbourhoods of Montreal, J. Transp. Geogr., 57, 63–69,
https://doi.org/10.1016/j.jtrangeo.2016.09.014, 2016.
Arenas, J. P.: Potential problems with environmental sound barriers when
used in mitigating surface transportation noise, Sci. Total
Environ., 405, 173–179, https://doi.org/10.1016/j.scitotenv.2008.06.049,
2008.
Au, C.-C. and Henderson, J. V.: Are Chinese cities too small?, Rev.
Econ. Stud., 73, 549–576,
https://doi.org/10.1111/j.1467-937X.2006.00387.x, 2006.
Baidu Maps: Baidu Street View, https://api.map.baidu.com/panorama/v2?key=parameters,
last access: 13 June 2022.
Begou, P., Kassomenos, P., and Kelessis, A.: Effects of road traffic noise
on the prevalence of cardiovascular diseases: The case of Thessaloniki,
Greece, Sci. Total Environ., 703, 134477,
https://doi.org/10.1016/j.scitotenv.2019.134477, 2020.
Bhattacharyya, A., Chatterjee, S., Sen, S., Sinitca, A., Kaplun, D., and Sarkar, R.: A deep learning model for classifying human facial expressions from infrared thermal images, Scientific Reports, 11, 20696, https://doi.org/10.1038/s41598-021-99998-z, 2021.
Den Boer, L. and Schroten, A.: Traffic noise reduction in Europe, CE Delft, 14, 2057–2068, https://inquinamentoacustico.it/_dowload/traffic_noise_reduction-CE-Delft.pdf (last access: 12 December 2021), 2007.
Cao, Y., Geddes, T. A., Yang, J. Y. H., and Yang, P.: Ensemble deep learning
in bioinformatics, Nature Machine Intelligence, 2, 500–508,
https://doi.org/10.1038/s42256-020-0217-y, 2020.
Chen, M.: Vectorized dataset of roadside noise barriers in China, National
Tibetan Plateau/Third Pole Environment Data Center [data set],
https://doi.org/10.11888/Others.tpdc.271914, 2021.
Deng, M., Yang, W., Chen, C., Wu, Z., Liu, Y., and Xiang, C.: Street-level
solar radiation mapping and patterns profiling using Baidu Street View
images, Sustain. Cities Soc., 75, 103289,
https://doi.org/10.1016/j.scs.2021.103289, 2021.
Du, K., Ning, J., and Yan, L.: How long is the sun duration in a street
canyon? – Analysis of the view factors of street canyons, Build.
Environ., 172, 106680, https://doi.org/10.1016/j.buildenv.2020.106680,
2020.
Goodchild, M. F. and Li, W.: Replication across space and time must be weak in the social and environmental sciences, P. Natl. Acad. of Sci. USA, 118, e2015759118, https://doi.org/10.1073/pnas.2015759118, 2021.
Griffiths, D. and Boehm, J.: Improving public data for building segmentation
from Convolutional Neural Networks (CNNs) for fused airborne lidar and image
data using active contours, ISPRS J. Photogramm., 154, 70–83, https://doi.org/10.1016/j.isprsjprs.2019.05.013, 2019.
Gu, M., Liu, Y., Yang, J., Peng, L., Zhao, C., Yang, Z., Yang, J., Fang, W.,
Fang, J., and Zhao, Z.: Estimation of environmental effect of PVNB installed
along a metro line in China, Renew. Energ., 45, 237–244,
https://doi.org/10.1016/j.renene.2012.02.021, 2012.
Guan, C. and Rowe, P. G.: In pursuit of a well-balanced network of cities
and towns: A case study of the Changjiang Delta Region in China, Environ.
Plann. B, 45, 548–566,
https://doi.org/10.1177/2399808317696073, 2018.
Guan, X., Wei, H., Lu, S., Dai, Q., and Su, H.: Assessment on the
urbanization strategy in China: Achievements, challenges and reflections,
Habitat Int., 71, 97–109,
https://doi.org/10.1016/j.habitatint.2017.11.009, 2018.
He, K., Zhang, X., Ren, S., and Sun, J.: Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27–30 June 2016, 770–778, https://doi.org/10.1109/CVPR.2016.90, 2016.
Hu, K., Zhang, Z., Niu, X., Zhang, Y., Cao, C., Xiao, F., and Gao, X.:
Retinal vessel segmentation of color fundus images using multiscale
convolutional neural network with an improved cross-entropy loss function,
Neurocomputing, 309, 179–191, https://doi.org/10.1016/j.neucom.2018.05.011,
2018.
Huang, Y., Lei, C., Liu, C. H., Perez, P., Forehead, H., Kong, S., and Zhou,
J. L.: A review of strategies for mitigating roadside air pollution in urban
street canyons, Environ. Pollut., 280, 116971,
https://doi.org/10.1016/j.envpol.2021.116971, 2021.
Janowicz, K., Gao, S., McKenzie, G., Hu, Y., and Bhaduri, B.: GeoAI:
spatially explicit artificial intelligence techniques for geographic
knowledge discovery and beyond, Int. J. Geogr.
Inf. Sci., 34, 625–636,
https://doi.org/10.1080/13658816.2019.1684500, 2019.
Jia, M., Liu, Y., Lieske, S. N., and Chen, T.: Public policy change and its
impact on urban expansion: An evaluation of 265 cities in China, Land Use
Policy, 97, 104754, https://doi.org/10.1016/j.landusepol.2020.104754, 2020.
Kang, Y., Zhang, F., Gao, S., Lin, H., and Liu, Y.: A review of urban
physical environment sensing using street view imagery in public health
studies, Annals of GIS, 26, 261–275,
https://doi.org/10.1080/19475683.2020.1791954, 2020.
Lafia, S., Turner, A., and Kuhn, W.: Improving Discovery of Open Civic Data, in: 10th International Conference on Geographic Information Science (GIScience 2018), Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, UC Santa Barbara, https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.9, 2018.
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444,
https://doi.org/10.1038/nature14539, 2015.
Li, W.: GeoAI: Where machine learning and big data converge in GIScience,
Journal of Spatial Information Science, 20,
71–77,
https://doi.org/10.5311/josis.2020.20.658, 2020.
Lin, G. C.: The growth and structural change of Chinese cities: a contextual
and geographic analysis, Cities, 19, 299–316,
https://doi.org/10.1016/S0264-2751(02)00039-2, 2002.
Liu, X. and Long, Y.: Automated identification and characterization of
parcels with OpenStreetMap and points of interest, Environ. Plann.
B, 43, 341–360,
https://doi.org/10.1177/0265813515604767, 2015.
Liu, X., Chen, M., Claramunt, C., Batty, M., Kwan, M.-P., Senousi, A. M., Cheng, T., Strobl, J., Arzu, C., and Wilson, J.: Geographic information science in the era of geospatial big data: A cyberspace perspective, The Innovation, 3, 100279, https://doi.org/10.1016/j.xinn.2022.100279, 2022.
Liu, Y., Ma, X., Shu, L., Yang, Q., Zhang, Y., Huo, Z., and Zhou, Z.:
Internet of things for noise mapping in smart cities: state of the art and
future directions, IEEE Network, 34, 112–118,
https://doi.org/10.1109/MNET.011.1900634, 2020.
Loshchilov, I. and Hutter, F.: Decoupled weight decay regularization, arXiv
[preprint], https://doi.org/10.48550/arXiv.1711.05101, 14 November 2017.
Lü, G., Chen, M., Yuan, L., Zhou, L., Wen, Y., Wu, M., Hu, B., Yu, Z.,
Yue, S., and Sheng, Y.: Geographic scenario: a possible foundation for
further development of virtual geographic environments, Int.
J. Digit. Earth, 11, 356–368,
https://doi.org/10.1080/17538947.2017.1374477, 2018.
Ma, L. J.: Urban administrative restructuring, changing scale relations and
local economic development in China, Polit. Geogr., 24, 477–497,
https://doi.org/10.1016/j.polgeo.2004.10.005, 2005.
Map Technical Review Center, Ministry of Natural Resources, China:
Chinese administrative boundary, http://bzdt.ch.mnr.gov.cn/,
last access: 21 April 2021.
Ning, Z., Hudda, N., Daher, N., Kam, W., Herner, J., Kozawa, K., Mara, S.,
and Sioutas, C.: Impact of roadside noise barriers on particle size
distributions and pollutants concentrations near freeways, Atmos.
Environ., 44, 3118–3127, https://doi.org/10.1016/j.atmosenv.2010.05.033,
2010.
Oltean-Dumbrava, C. and Miah, A.: Assessment and relative sustainability of
common types of roadside noise barriers, J. Clean. Prod., 135,
919–931, https://doi.org/10.1016/j.jclepro.2016.06.107, 2016.
OpenStreetMap contributors: Data networks, OSM [data set], https://www.openstreetmap.org/, last access: 16 May 2021.
Perkins, R. M. and Xiang, W.-N.: Building a geographic info-structure for
sustainable development planning on a small island developing state,
Landscape Urban Plan., 78, 353–361,
https://doi.org/10.1016/j.landurbplan.2005.10.005, 2006.
Potvin, S., Apparicio, P., and Séguin, A.-M.: The spatial distribution
of noise barriers in Montreal: A barrier to achieve environmental equity,
Transportation Res. D-T. E., 72, 83–97,
https://doi.org/10.1016/j.trd.2019.04.011, 2019.
Qian, Z., Liu, X., Tao, F., and Zhou, T.: Identification of Urban Functional Areas by Coupling Satellite Images and Taxi GPS Trajectories, Remote Sensing, 12, 2449, https://doi.org/10.3390/rs12152449, 2020.
Qian, Z., Chen, M., Zhong, T., Zhang, F., Zhu, R., Zhang, Z., Zhang, K.,
Sun, Z., and Lü, G.: Deep Roof Refiner: A detail-oriented deep learning
network for refined delineation of roof structure lines using satellite
imagery, Int. J. Appl. Earth Obs., 107, 102680, https://doi.org/10.1016/j.jag.2022.102680, 2022
Qin, K., Xu, Y., Kang, C., and Kwan, M. P.: A graph convolutional network
model for evaluating potential congestion spots based on local urban built
environments, Transactions in GIS, 24, 1382–1401,
https://doi.org/10.1111/tgis.12641, 2020.
Ranasinghe, D., Lee, E. S., Zhu, Y., Frausto-Vicencio, I., Choi, W., Sun,
W., Mara, S., Seibt, U., and Paulson, S. E.: Effectiveness of vegetation and
sound wall-vegetation combination barriers on pollution dispersion from
freeways under early morning conditions, Sci. Total Environ.,
658, 1549–1558, https://doi.org/10.1016/j.scitotenv.2018.12.159, 2019.
Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese,
S.: Generalized intersection over union: A metric and a loss for bounding
box regression, in: Proceedings of the IEEE/CVF Conference on Computer Vision
and Pattern Recognition, Long Beach, CA, USA
15–20 June 2019, https://doi.org/10.1109/CVPR.2019.00075, 658–666, 2019.
Sainju, A. M. and Jiang, Z.: Mapping Road Safety Features from Streetview Imagery: A Deep Learning Approach, ACM/IMS Trans. Data Sci., 1, 15, https://doi.org/10.1145/3362069, 2020.
Song, Y. and Wu, P.: Earth Observation for Sustainable Infrastructure: A
Review, Remote Sensing, 13, 1528, https://doi.org/10.3390/rs13081528, 2021.
Song, Y., Thatcher, D., Li, Q., McHugh, T., and Wu, P.: Developing
sustainable road infrastructure performance indicators using a model-driven
fuzzy spatial multi-criteria decision making method, Renew.
Sust. Energ. Rev., 138, 110538,
https://doi.org/10.1016/j.rser.2020.110538, 2021.
Thomas, K. A., Kidziński, Ł., Halilaj, E., Fleming, S. L.,
Venkataraman, G. R., Oei, E. H., Gold, G. E., and Delp, S. L.: Automated
classification of radiographic knee osteoarthritis severity using deep
neural networks, Radiology: Artificial Intelligence, 2, e190065,
https://doi.org/10.1148/ryai.2020190065, 2020.
Tobler, W.: A computer movie simulating urban growth in the Detroit region,
Econ. Geogr., 46, 234–240, 1970.
Tobler, W.: On the first law of geography: A reply, Ann.
Assoc. Am. Geogr., 94, 304–310,
https://doi.org/10.1111/j.1467-8306.2004.09402009.x, 2004.
Touvron, H., Vedaldi, A., Douze, M., and Jégou, H.: Fixing the
train-test resolution discrepancy, arXiv [preprint], https://doi.org/10.48550/arXiv.1906.06423, 14
June 2019.
Wang, M., Deng, Y., Won, J., and Cheng, J. C.: An integrated underground
utility management and decision support based on BIM and GIS, Automat.
Constr., 107, 102931, https://doi.org/10.1016/j.autcon.2019.102931,
2019.
Wang, S. and Wang, X.: Modeling and analysis of highway emission dispersion
due to noise barrier and automobile wake effects, Atmos. Pollut.
Res., 12, 67–75, https://doi.org/10.1016/j.apr.2020.08.013, 2021.
Wang, Y., Zhu, X., Zhang, T., Bano, S., Pan, H., Qi, L., Zhang, Z., and
Yuan, Y.: A renewable low-frequency acoustic energy harvesting noise barrier
for high-speed railways using a Helmholtz resonator and a PVDF film, Appl.
Energ., 230, 52–61, https://doi.org/10.1016/j.apenergy.2018.08.080, 2018.
Wang, Y.-R. and Li, X.-M.: Arctic sea ice cover data from spaceborne synthetic aperture radar by deep learning, Earth Syst. Sci. Data, 13, 2723–2742, https://doi.org/10.5194/essd-13-2723-2021, 2021.
Wolpert, D. H. and Macready, W. G.: No free lunch theorems for optimization,
IEEE T. Evolut. Comput., 1, 67–82,
https://doi.org/10.1109/4235.585893, 1997.
Zagoruyko, S. and Komodakis, N.: Wide residual networks, arXiv [preprint],
https://doi.org/10.48550/arXiv.1605.07146, 23 May 2016.
Zhang, F., Zhou, B., Liu, L., Liu, Y., Fung, H. H., Lin, H., and Ratti, C.:
Measuring human perceptions of a large-scale urban region using machine
learning, Landscape Urban Plan., 180, 148–160,
https://doi.org/10.1016/j.landurbplan.2018.08.020, 2018.
Zhang, F., Wu, L., Zhu, D., and Liu, Y.: Social sensing from street-level
imagery: A case study in learning spatio-temporal urban mobility patterns,
ISPRS J. Photogramm., 153, 48–58,
https://doi.org/10.1016/j.isprsjprs.2019.04.017, 2019.
Zhang, K., Qian, Z., Yang, Y., Chen, M., Zhong, T., Zhu, R., Lv, G., and Yan, J.: Using street view images to identify road noise barriers with ensemble classification model and geospatial analysis, Sustain. Cities Soc., 78, 103598, https://doi.org/10.1016/j.scs.2021.103598, 2022.
Zhang, Z., Qian, Z., Zhong, T., Chen, M., Zhang, K., Yang, Y., Zhu, R., Zhang, F., Zhang, H., Zhou, F., Yu, J., Zhang, B., Lü, G., and Yan, J.: Vectorized rooftop area data for 90 cities in China, Scientific Data, 9, 66, https://doi.org/10.1038/s41597-022-01168-x, 2022.
Zhao, S. X., Chan, R. C., and Sit, K. T.: Globalization and the dominance of
large cities in contemporary China, Cities, 20, 265–278,
https://doi.org/10.1016/S0264-2751(03)00031-3, 2003.
Zhao, W.-J., Liu, E.-X., Poh, H. J., Wang, B., Gao, S.-P., Png, C. E., Li,
K. W., and Chong, S. H.: 3D traffic noise mapping using unstructured surface
mesh representation of buildings and roads, Appl. Acoust., 127, 297–304,
https://doi.org/10.1016/j.apacoust.2017.06.025, 2017.
Zhao, Y., Li, H., Kubilay, A., and Carmeliet, J.: Buoyancy effects on the
flows around flat and steep street canyons in simplified urban settings
subject to a neutral approaching boundary layer: Wind tunnel PIV
measurements, Sci. Total Environ., 797, 149067,
https://doi.org/10.1016/j.scitotenv.2021.149067, 2021.
Zhong, T., Zhang, K., Chen, M., Wang, Y., Zhu, R., Zhang, Z., Zhou, Z.,
Qian, Z., Lv, G., and Yan, J.: Assessment of solar photovoltaic potentials
on urban noise barriers using street-view imagery, Renew. Energ., 168,
181–194, https://doi.org/10.1016/j.renene.2020.12.044, 2021.
Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A., and
Torralba, A.: Semantic understanding of scenes through the ade20k dataset,
Int. J. Comput. Vision, 127, 302–321,
https://doi.org/10.1007/s11263-018-1140-0, 2019.
Zhou, H., He, S., Cai, Y., Wang, M., and Su, S.: Social inequalities in
neighborhood visual walkability: Using street view imagery and deep learning
technologies to facilitate healthy city planning, Sustain. Cities
Soc., 50, 101605, https://doi.org/10.1016/j.scs.2019.101605, 2019.
Short summary
Roadside noise barriers (RNBs) are important urban infrastructures to ensure a city is liveable. This study provides the first reliable and nationwide vectorized RNB dataset with street view imagery in China. The generated RNB dataset is evaluated in terms of two aspects, i.e., the detection accuracy and the completeness and positional accuracy. The method is based on a developed geospatial artificial intelligence framework.
Roadside noise barriers (RNBs) are important urban infrastructures to ensure a city is liveable....
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