Pub Date : 2021-11-01DOI: 10.1080/20964471.2021.1981197
Adan Wu, Tao Che, Xin Li, Xiaowen Zhu
ABSTRACT Research on Arctic passages has mainly focused on navigation policies, sea ice extraction models, and navigation of Arctic sea routes. It is difficult to quantitatively address the specific problems encountered by ships sailing in the Arctic in real time through traditional manual approaches. Additionally, existing sea ice information service systems focus on data sharing and lack online calculation and analysis capabilities, making it difficult for decision-makers to derive valuable information from massive amounts of data. To improve navigation analysis through intelligent information service, we built an advanced Ship Navigation Information Service System (SNISS) using a 3D geographic information system (GIS) based on big Earth data. The SNISS includes two main features: (1) heuristic algorithms were developed to identify the optimal navigation route of the Arctic Northeast Passage (NEP) from a macroscale perspective for the past 10 years to the next 100 years, and (2) for key sea straits along the NEP, online local sea-ice images can be retrieved to provide a fully automatic sea ice data processing workflow, solving the problems of poor flexibility and low availability of real sea ice remote sensing data extraction. This work can potentially enhance the safety of shipping navigation along the NEP.
{"title":"A ship navigation information service system for the Arctic Northeast Passage using 3D GIS based on big Earth data","authors":"Adan Wu, Tao Che, Xin Li, Xiaowen Zhu","doi":"10.1080/20964471.2021.1981197","DOIUrl":"https://doi.org/10.1080/20964471.2021.1981197","url":null,"abstract":"ABSTRACT Research on Arctic passages has mainly focused on navigation policies, sea ice extraction models, and navigation of Arctic sea routes. It is difficult to quantitatively address the specific problems encountered by ships sailing in the Arctic in real time through traditional manual approaches. Additionally, existing sea ice information service systems focus on data sharing and lack online calculation and analysis capabilities, making it difficult for decision-makers to derive valuable information from massive amounts of data. To improve navigation analysis through intelligent information service, we built an advanced Ship Navigation Information Service System (SNISS) using a 3D geographic information system (GIS) based on big Earth data. The SNISS includes two main features: (1) heuristic algorithms were developed to identify the optimal navigation route of the Arctic Northeast Passage (NEP) from a macroscale perspective for the past 10 years to the next 100 years, and (2) for key sea straits along the NEP, online local sea-ice images can be retrieved to provide a fully automatic sea ice data processing workflow, solving the problems of poor flexibility and low availability of real sea ice remote sensing data extraction. This work can potentially enhance the safety of shipping navigation along the NEP.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87061171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-27DOI: 10.1080/20964471.2021.1981801
Z. Mohamed-Ghouse, C. Desha, A. Rajabifard, Michelle Blicavs, Graeme Martin
ABSTRACT Leaders are increasingly calling for improved decision support to manage human and environmental challenges in the 21st Century. The 17 United Nations Sustainable Development Goals (SDGs) provide a framing of these challenges, wherein 169 targets require significant data to be monitored and pursued effectively. However, many targets are still not connected with big Earth data capabilities. In this conceptual paper, the authors sought to answer the question “How are partnerships influencing progress in using big Earth data to address the SDGs?” Using the Pivotal Principles for Digital Earth, we reflect on the geospatial sector’s partnering efforts and opportunities for enhancing the use of big Earth data. We use Australia as a case study to explore partnering for action towards one or more SDGs. We conclude that partnerships are emerging for big Earth data use in addressing the SDGs, but much can still be done to harness the power of partnerships for transformative SDG outcomes. We propose four key enabling priorities: 1) multiple-stakeholder collaboration, 2) regular enactment of the problem-solving cycle, 3) transparent and reliable georeferenced data, and 4) development and preservation of trust. Five “next steps” are outlined for Australia, which can also benefit practitioners and leaders globally in problem-solving for the SDGs.
{"title":"Evaluating the role of partnerships in increasing the use of big Earth data to support the Sustainable Development Goals: an Australian perspective","authors":"Z. Mohamed-Ghouse, C. Desha, A. Rajabifard, Michelle Blicavs, Graeme Martin","doi":"10.1080/20964471.2021.1981801","DOIUrl":"https://doi.org/10.1080/20964471.2021.1981801","url":null,"abstract":"ABSTRACT Leaders are increasingly calling for improved decision support to manage human and environmental challenges in the 21st Century. The 17 United Nations Sustainable Development Goals (SDGs) provide a framing of these challenges, wherein 169 targets require significant data to be monitored and pursued effectively. However, many targets are still not connected with big Earth data capabilities. In this conceptual paper, the authors sought to answer the question “How are partnerships influencing progress in using big Earth data to address the SDGs?” Using the Pivotal Principles for Digital Earth, we reflect on the geospatial sector’s partnering efforts and opportunities for enhancing the use of big Earth data. We use Australia as a case study to explore partnering for action towards one or more SDGs. We conclude that partnerships are emerging for big Earth data use in addressing the SDGs, but much can still be done to harness the power of partnerships for transformative SDG outcomes. We propose four key enabling priorities: 1) multiple-stakeholder collaboration, 2) regular enactment of the problem-solving cycle, 3) transparent and reliable georeferenced data, and 4) development and preservation of trust. Five “next steps” are outlined for Australia, which can also benefit practitioners and leaders globally in problem-solving for the SDGs.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85300979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-15DOI: 10.1080/20964471.2021.1976706
Dong Liang, Huadong Guo, Lu Zhang, Haipeng Li, Xuezhi Wang
ABSTRACT Antarctica plays an important role in research on global change, and its unique geography, ocean, climate, and environment provide an ideal place for humankind to understand Earth’s evolution. Remote sensing provides an effective means to monitor and observe large-scale processes on the continent. Synthetic aperture radar (SAR) in particular provides the capability for all-weather Earth observation. The Sentinel-1A and Sentinel-1B SAR satellites have ideal ground coverage and imaging frequency for observing Antarctica. This study developed a dataset of 69,586 Sentinel-1 EW mode satellite images of the Antarctic ice sheet from October 2014 to December 2020. The dataset was processed with the European Space Agency Sentinel Application Platform (SNAP) and a Python batch scheduling tool on the Big Earth Data Cloud Service Platform of the Chinese Academy of Sciences Big Earth Data Science Engineering Program (CASEarth). Several data processing operations were implemented to process the raw dataset, including radiometric calibration, invalid edge removal, geocoding, data re-projection to an Antarctic projection, data compression to TIFF format, and construction of image pyramids. The dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00085.
{"title":"Sentinel-1 EW mode dataset for Antarctica from 2014–2020 produced by the CASEarth Cloud Service Platform","authors":"Dong Liang, Huadong Guo, Lu Zhang, Haipeng Li, Xuezhi Wang","doi":"10.1080/20964471.2021.1976706","DOIUrl":"https://doi.org/10.1080/20964471.2021.1976706","url":null,"abstract":"ABSTRACT Antarctica plays an important role in research on global change, and its unique geography, ocean, climate, and environment provide an ideal place for humankind to understand Earth’s evolution. Remote sensing provides an effective means to monitor and observe large-scale processes on the continent. Synthetic aperture radar (SAR) in particular provides the capability for all-weather Earth observation. The Sentinel-1A and Sentinel-1B SAR satellites have ideal ground coverage and imaging frequency for observing Antarctica. This study developed a dataset of 69,586 Sentinel-1 EW mode satellite images of the Antarctic ice sheet from October 2014 to December 2020. The dataset was processed with the European Space Agency Sentinel Application Platform (SNAP) and a Python batch scheduling tool on the Big Earth Data Cloud Service Platform of the Chinese Academy of Sciences Big Earth Data Science Engineering Program (CASEarth). Several data processing operations were implemented to process the raw dataset, including radiometric calibration, invalid edge removal, geocoding, data re-projection to an Antarctic projection, data compression to TIFF format, and construction of image pyramids. The dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00085.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88709680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
ABSTRACT Poverty alleviation is one of the greatest challenges faced by low-income and middle-income countries. China, which had the largest rural poverty-stricken population, has made tremendous efforts in alleviating poverty especially since the implementation of the targeted poverty alleviation (TPA) policy in 2014, and by 2020, all national poverty-stricken counties (NPCs) have been out of poverty. This study combines deep learning with multiple satellite datasets to estimate county-level economic development from 2008 to 2019 and assess the effect of the TPA policy for 592 national poverty-stricken counties (NPCs) at country, provincial and county levels. Per capita gross domestic product (GDP) is used to measure the affluence level. From 2014 through 2019, the 592 NPCs experience an average growth rate of per capita GDP at 7.6%±0.4%, higher than the average growth rate of 310 adjacent non-NPC counties (7.3%±0.4%) and of the whole country (6.3%). We also reveal 42 counties with weak growth recently and that the average affluence level of the NPCs in 2019 is still much lower than the national or provincial averages. The inexpensive, timely and accurate method proposed here can be applied to other low-income and middle-income countries for affluence assessment.
{"title":"Evaluation of county-level poverty alleviation progress by deep learning and satellite observations","authors":"Yanxiao Jiang, Liqiang Zhang, Yang Li, Jintai Lin, Jingwen Li, Guoqing Zhou, Su-hong Liu, Jingxiu Cao, Zhiqiang Xiao","doi":"10.1080/20964471.2021.1967259","DOIUrl":"https://doi.org/10.1080/20964471.2021.1967259","url":null,"abstract":"ABSTRACT Poverty alleviation is one of the greatest challenges faced by low-income and middle-income countries. China, which had the largest rural poverty-stricken population, has made tremendous efforts in alleviating poverty especially since the implementation of the targeted poverty alleviation (TPA) policy in 2014, and by 2020, all national poverty-stricken counties (NPCs) have been out of poverty. This study combines deep learning with multiple satellite datasets to estimate county-level economic development from 2008 to 2019 and assess the effect of the TPA policy for 592 national poverty-stricken counties (NPCs) at country, provincial and county levels. Per capita gross domestic product (GDP) is used to measure the affluence level. From 2014 through 2019, the 592 NPCs experience an average growth rate of per capita GDP at 7.6%±0.4%, higher than the average growth rate of 310 adjacent non-NPC counties (7.3%±0.4%) and of the whole country (6.3%). We also reveal 42 counties with weak growth recently and that the average affluence level of the NPCs in 2019 is still much lower than the national or provincial averages. The inexpensive, timely and accurate method proposed here can be applied to other low-income and middle-income countries for affluence assessment.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74149612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-01DOI: 10.1080/20964471.2021.1974681
Charlotte Poussin, Alexandrine Massot, C. Ginzler, D. Weber, B. Chatenoux, Pierre Lacroix, Thomas Piller, L. Nguyen, G. Giuliani
ABSTRACT Exacerbated by climate change, Europe has experienced series of hot and dry summer since the beginning of the 21st century. The importance of land conditions became an international concern with a dedicated sustainable development goal (SDG), the SDG 15. It calls for developing and finding innovative solutions to follow and evaluate impacts of changing land conditions induced by various driving forces. In Switzerland, drought risk will significantly increase in the coming decades with severe consequences on agriculture, energy production and vegetation. In this paper, we used a 35-year satellite-derived annual and seasonal times-series of normalized difference water index (NDWI) to follow vegetation water content evolution at different spatial and temporal scales across Switzerland and related them to temperature and precipitation to investigate possible responses of changing climatic conditions. Results indicate that there is a small and slow drying tendency at the country scale with a NDWI mean decreasing slope of −0.22%/year for the 23% significant pixels across Switzerland. This tendency is mostly visible below 2000 m above sea level (m.a.s.l.) and in all biogeographical regions. The Southern Alps regions appear to be more responsive to changing drying conditions with a significant and slight negative NDWI trend (−0.39%/year) over the last 35 years. Moreover, NDWI values are mostly a function of temperature at elevations below the tree line rather than precipitation. Findings suggest that multi-annual and seasonal NDWI can be a valuable indicator to monitor vegetation water content at different scales, but other components such as land cover type and evapotranspiration should be considered to better characterize NDWI variability. Satellite Earth Observations data can provide valuable complementary observations for national statistics on the ecological state of vegetation to support SDG 15 to monitor land affected by drying conditions.
{"title":"Drying conditions in Switzerland – indication from a 35-year Landsat time-series analysis of vegetation water content estimates to support SDGs","authors":"Charlotte Poussin, Alexandrine Massot, C. Ginzler, D. Weber, B. Chatenoux, Pierre Lacroix, Thomas Piller, L. Nguyen, G. Giuliani","doi":"10.1080/20964471.2021.1974681","DOIUrl":"https://doi.org/10.1080/20964471.2021.1974681","url":null,"abstract":"ABSTRACT Exacerbated by climate change, Europe has experienced series of hot and dry summer since the beginning of the 21st century. The importance of land conditions became an international concern with a dedicated sustainable development goal (SDG), the SDG 15. It calls for developing and finding innovative solutions to follow and evaluate impacts of changing land conditions induced by various driving forces. In Switzerland, drought risk will significantly increase in the coming decades with severe consequences on agriculture, energy production and vegetation. In this paper, we used a 35-year satellite-derived annual and seasonal times-series of normalized difference water index (NDWI) to follow vegetation water content evolution at different spatial and temporal scales across Switzerland and related them to temperature and precipitation to investigate possible responses of changing climatic conditions. Results indicate that there is a small and slow drying tendency at the country scale with a NDWI mean decreasing slope of −0.22%/year for the 23% significant pixels across Switzerland. This tendency is mostly visible below 2000 m above sea level (m.a.s.l.) and in all biogeographical regions. The Southern Alps regions appear to be more responsive to changing drying conditions with a significant and slight negative NDWI trend (−0.39%/year) over the last 35 years. Moreover, NDWI values are mostly a function of temperature at elevations below the tree line rather than precipitation. Findings suggest that multi-annual and seasonal NDWI can be a valuable indicator to monitor vegetation water content at different scales, but other components such as land cover type and evapotranspiration should be considered to better characterize NDWI variability. Satellite Earth Observations data can provide valuable complementary observations for national statistics on the ecological state of vegetation to support SDG 15 to monitor land affected by drying conditions.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86733537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-20DOI: 10.1080/20964471.2021.1948178
Zahra Assarkhaniki, S. Sabri, A. Rajabifard
ABSTRACT The detection of informal settlements is the first step in planning and upgrading deprived areas in order to leave no one behind in SDGs. Very High-Resolution satellite images (VHR), have been extensively used for this purpose. However, as a cost-prohibitive data source, VHR might not be available to all, particularly nations that are home to many informal settlements. This study examines the application of open and freely available data sources to detect the structure and pattern of informal settlements. Here, in a case study of Jakarta, Indonesia, Medium Resolution satellite imagery (MR) derived from Landsat 8 (2020) was classified to detect these settlements. The classification was done using Random Forest (RF) classifier through two complementary approaches to develop the training set. In the first approach, available survey data sets (Jakarta’s informal settlements map for 2015) and visual interpretation using High-Resolution Google Map imagery have been used to build the training set. Throughout the second round of classification, OpenStreetMap (OSM) layers were used as the complementary approach for training. Results from the validation test for the second round revealed better accuracy and precision in classification. The proposed method provides an opportunity to use open data for informal settlements detection, when: 1) more expensive high resolution data sources are not accessible; 2) the area of interest is not larger than a city; and 3) the physical characteristics of the settlements differ significantly from their surrounding formal area. The method presents the application of globally accessible data to help the achievement of resilience and SDGs in informal settlements.
{"title":"Using open data to detect the structure and pattern of informal settlements: an outset to support inclusive SDGs’ achievement","authors":"Zahra Assarkhaniki, S. Sabri, A. Rajabifard","doi":"10.1080/20964471.2021.1948178","DOIUrl":"https://doi.org/10.1080/20964471.2021.1948178","url":null,"abstract":"ABSTRACT The detection of informal settlements is the first step in planning and upgrading deprived areas in order to leave no one behind in SDGs. Very High-Resolution satellite images (VHR), have been extensively used for this purpose. However, as a cost-prohibitive data source, VHR might not be available to all, particularly nations that are home to many informal settlements. This study examines the application of open and freely available data sources to detect the structure and pattern of informal settlements. Here, in a case study of Jakarta, Indonesia, Medium Resolution satellite imagery (MR) derived from Landsat 8 (2020) was classified to detect these settlements. The classification was done using Random Forest (RF) classifier through two complementary approaches to develop the training set. In the first approach, available survey data sets (Jakarta’s informal settlements map for 2015) and visual interpretation using High-Resolution Google Map imagery have been used to build the training set. Throughout the second round of classification, OpenStreetMap (OSM) layers were used as the complementary approach for training. Results from the validation test for the second round revealed better accuracy and precision in classification. The proposed method provides an opportunity to use open data for informal settlements detection, when: 1) more expensive high resolution data sources are not accessible; 2) the area of interest is not larger than a city; and 3) the physical characteristics of the settlements differ significantly from their surrounding formal area. The method presents the application of globally accessible data to help the achievement of resilience and SDGs in informal settlements.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86661110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-13DOI: 10.1080/20964471.2021.1965370
A. Rawson, Z. Sabeur, M. Brito
ABSTRACT Each year, accidents involving ships result in significant loss of life, environmental pollution and economic losses. The promotion of navigation safety through risk reduction requires methods to assess the spatial distribution of the relative likelihood of occurrence. Yet, such methods necessitate the integration of large volumes of heterogenous datasets which are not well suited to traditional data structures. This paper proposes the use of the Discrete Global Grid System (DGGS) as an efficient and advantageous structure to integrate vessel traffic, metocean, bathymetric, infrastructure and other relevant maritime datasets to predict the occurrence of ship groundings. Massive and heterogenous datasets are well suited for machine learning algorithms and this paper develops a spatial maritime risk model based on a DGGS utilising such an approach. A Random Forest algorithm is developed to predict the frequency and spatial distribution of groundings while achieving an R2 of 0.55 and a mean squared error of 0.002. The resulting risk maps are useful for decision-makers in planning the allocation of mitigation measures, targeted to regions with the highest risk. Further work is identified to expand the applications and insights which could be achieved through establishing a DGGS as a global maritime spatial data structure.
每年，船舶事故都会造成重大的人员伤亡、环境污染和经济损失。通过减少风险来促进航行安全需要评估相对发生可能性的空间分布的方法。然而，这种方法需要集成大量异构数据集，而这些数据集不适合传统的数据结构。本文提出利用离散全球网格系统(Discrete Global Grid System, DGGS)作为一种高效且具有优势的结构，整合船舶交通、海洋气象、水深、基础设施和其他相关海事数据集，预测船舶搁浅的发生。大规模和异构数据集非常适合机器学习算法，本文利用这种方法开发了基于DGGS的空间海上风险模型。随机森林算法用于预测接地频率和空间分布，R2为0.55，均方误差为0.002。由此产生的风险图有助于决策者规划针对风险最高的区域分配缓解措施。通过将DGGS建立为全球海洋空间数据结构，确定了进一步的工作，以扩大应用和见解。
{"title":"Intelligent geospatial maritime risk analytics using the Discrete Global Grid System","authors":"A. Rawson, Z. Sabeur, M. Brito","doi":"10.1080/20964471.2021.1965370","DOIUrl":"https://doi.org/10.1080/20964471.2021.1965370","url":null,"abstract":"ABSTRACT Each year, accidents involving ships result in significant loss of life, environmental pollution and economic losses. The promotion of navigation safety through risk reduction requires methods to assess the spatial distribution of the relative likelihood of occurrence. Yet, such methods necessitate the integration of large volumes of heterogenous datasets which are not well suited to traditional data structures. This paper proposes the use of the Discrete Global Grid System (DGGS) as an efficient and advantageous structure to integrate vessel traffic, metocean, bathymetric, infrastructure and other relevant maritime datasets to predict the occurrence of ship groundings. Massive and heterogenous datasets are well suited for machine learning algorithms and this paper develops a spatial maritime risk model based on a DGGS utilising such an approach. A Random Forest algorithm is developed to predict the frequency and spatial distribution of groundings while achieving an R2 of 0.55 and a mean squared error of 0.002. The resulting risk maps are useful for decision-makers in planning the allocation of mitigation measures, targeted to regions with the highest risk. Further work is identified to expand the applications and insights which could be achieved through establishing a DGGS as a global maritime spatial data structure.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80152939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-02DOI: 10.1080/20964471.2021.1965371
Bin Zhang, Fuchao Li, Gang Zheng, Yanjun Wang, Zhetao Tan, Xiaofeng Li
ABSTRACT The ocean is a critical part of the global ecosystem. The marine ecosystem balance is crucial for human survival and sustainable development. However, due to the impacts of global climate change and human activities, the ocean is rapidly changing, which poses an enormous threat to human health and the economy. “Conserve and sustainably use the oceans, seas and marine resources” is one of the 17 Sustainable Development Goals (SDGs). Therefore, it is urgent to construct a transformative marine scientific solution to promote sustainable development. Marine data is the basis of ocean cognition and governance. Marine science has ushered in the era of big data with continuous advances in modern marine data acquisition. While big data provides a large amount of data for SDG research, it simultaneously brings unprecedented challenges. This study introduces an overall framework of a system for solving the current problems faced by marine data serving SDGs from the perspective of marine data management and application. Also, it articulates how the system helps the SDGs through two application cases of managing fragmented marine data and developing global climate change data products.
{"title":"Developing big ocean system in support of Sustainable Development Goals: challenges and countermeasures","authors":"Bin Zhang, Fuchao Li, Gang Zheng, Yanjun Wang, Zhetao Tan, Xiaofeng Li","doi":"10.1080/20964471.2021.1965371","DOIUrl":"https://doi.org/10.1080/20964471.2021.1965371","url":null,"abstract":"ABSTRACT The ocean is a critical part of the global ecosystem. The marine ecosystem balance is crucial for human survival and sustainable development. However, due to the impacts of global climate change and human activities, the ocean is rapidly changing, which poses an enormous threat to human health and the economy. “Conserve and sustainably use the oceans, seas and marine resources” is one of the 17 Sustainable Development Goals (SDGs). Therefore, it is urgent to construct a transformative marine scientific solution to promote sustainable development. Marine data is the basis of ocean cognition and governance. Marine science has ushered in the era of big data with continuous advances in modern marine data acquisition. While big data provides a large amount of data for SDG research, it simultaneously brings unprecedented challenges. This study introduces an overall framework of a system for solving the current problems faced by marine data serving SDGs from the perspective of marine data management and application. Also, it articulates how the system helps the SDGs through two application cases of managing fragmented marine data and developing global climate change data products.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84682260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-09-01DOI: 10.1080/20964471.2021.1950351
Huiping Jiang, Zhongchang Sun, Huadong Guo, Q. Xing, Wenjie Du, G. Cai
ABSTRACT China’s urbanization has attracted a lot of attention due to its unprecedented pace and intensity in terms of land, population, and economic impact. However, due to the lack of consistent and harmonized data, little is known about the patterns and dynamics of the interaction between these different aspects over the past few decades. Along with the implementation of the 2030 Agenda for Sustainable Development, a standardized dataset for assessing the sustainability of urbanization in China is needed. In this paper, we used remote sensing data from multiple sources (time-series of Landsat and Sentinel images) to map the impervious surface area (ISA) at five-year intervals from 1990 to 2015 and then converted the results into a standardized dataset of the built-up area for 433 Chinese cities with 300,000 inhabitants or more. This dataset was produced following the well-established rules adopted by the United Nations (UN). Validation of the ISA maps in urban areas based on the visual interpretation of Google Earth images showed that the average overall accuracy (OA), producer’s accuracy (PA) and user’s accuracy (UA) were 91.24%, 92.58% and 89.65%, respectively. Comparisons with other existing urban built-up area datasets derived from the National Bureau of Statistics of China, the World Bank and UN-habitat indicated that our dataset, namely the standardized urban built-up area dataset for China (SUBAD–China), provides an improved description of the spatiotemporal characteristics of the urbanization process and is especially applicable to a combined analysis of the spatial and socio-economic domains in urban areas. Potential applications of this dataset include combining the spatial expansion and demographic information provided by UN to calculate sustainable development indicators such as SDG 11.3.1. The dataset could also be used in other multidimensional syntheses related to the study of urbanization in China. The published dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00004.
{"title":"A standardized dataset of built-up areas of China’s cities with populations over 300,000 for the period 1990–2015","authors":"Huiping Jiang, Zhongchang Sun, Huadong Guo, Q. Xing, Wenjie Du, G. Cai","doi":"10.1080/20964471.2021.1950351","DOIUrl":"https://doi.org/10.1080/20964471.2021.1950351","url":null,"abstract":"ABSTRACT China’s urbanization has attracted a lot of attention due to its unprecedented pace and intensity in terms of land, population, and economic impact. However, due to the lack of consistent and harmonized data, little is known about the patterns and dynamics of the interaction between these different aspects over the past few decades. Along with the implementation of the 2030 Agenda for Sustainable Development, a standardized dataset for assessing the sustainability of urbanization in China is needed. In this paper, we used remote sensing data from multiple sources (time-series of Landsat and Sentinel images) to map the impervious surface area (ISA) at five-year intervals from 1990 to 2015 and then converted the results into a standardized dataset of the built-up area for 433 Chinese cities with 300,000 inhabitants or more. This dataset was produced following the well-established rules adopted by the United Nations (UN). Validation of the ISA maps in urban areas based on the visual interpretation of Google Earth images showed that the average overall accuracy (OA), producer’s accuracy (PA) and user’s accuracy (UA) were 91.24%, 92.58% and 89.65%, respectively. Comparisons with other existing urban built-up area datasets derived from the National Bureau of Statistics of China, the World Bank and UN-habitat indicated that our dataset, namely the standardized urban built-up area dataset for China (SUBAD–China), provides an improved description of the spatiotemporal characteristics of the urbanization process and is especially applicable to a combined analysis of the spatial and socio-economic domains in urban areas. Potential applications of this dataset include combining the spatial expansion and demographic information provided by UN to calculate sustainable development indicators such as SDG 11.3.1. The dataset could also be used in other multidimensional syntheses related to the study of urbanization in China. The published dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00004.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88281538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-30DOI: 10.1080/20964471.2021.1964879
Xin Zhang, Ya’nan Zhou, Jiancheng Luo
ABSTRACT In recent years, the rapid development of Earth observation technology has produced an increasing growth in remote sensing big data, posing serious challenges for effective and efficient processing and analysis. Meanwhile, there has been a massive rise in deep-learning-based algorithms for remote sensing tasks, providing a large opportunity for remote sensing big data. In this article, we initially summarize the features of remote sensing big data. Subsequently, following the pipeline of remote sensing tasks, a detailed and technical review is conducted to discuss how deep learning has been applied to the processing and analysis of remote sensing data, including geometric and radiometric processing, cloud masking, data fusion, object detection and extraction, land-use/cover classification, change detection and multitemporal analysis. Finally, we discussed technical challenges and concluded directions for future research in deep-learning-based applications for remote sensing big data.
{"title":"Deep learning for processing and analysis of remote sensing big data: a technical review","authors":"Xin Zhang, Ya’nan Zhou, Jiancheng Luo","doi":"10.1080/20964471.2021.1964879","DOIUrl":"https://doi.org/10.1080/20964471.2021.1964879","url":null,"abstract":"ABSTRACT In recent years, the rapid development of Earth observation technology has produced an increasing growth in remote sensing big data, posing serious challenges for effective and efficient processing and analysis. Meanwhile, there has been a massive rise in deep-learning-based algorithms for remote sensing tasks, providing a large opportunity for remote sensing big data. In this article, we initially summarize the features of remote sensing big data. Subsequently, following the pipeline of remote sensing tasks, a detailed and technical review is conducted to discuss how deep learning has been applied to the processing and analysis of remote sensing data, including geometric and radiometric processing, cloud masking, data fusion, object detection and extraction, land-use/cover classification, change detection and multitemporal analysis. Finally, we discussed technical challenges and concluded directions for future research in deep-learning-based applications for remote sensing big data.","PeriodicalId":8765,"journal":{"name":"Big Earth Data","volume":null,"pages":null},"PeriodicalIF":4.0,"publicationDate":"2021-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90797129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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