Pub Date : 2022-12-01DOI: 10.1109/mgrs.2022.3223267
G. Vetharatnam, H. Ewe, H. Chuah
{"title":"IGARSS 2022 in Kuala Lumpur, Malaysia: Preserving Our Heritage, Enabling Our Future Through Remote Sensing [Conference Reports]","authors":"G. Vetharatnam, H. Ewe, H. Chuah","doi":"10.1109/mgrs.2022.3223267","DOIUrl":"https://doi.org/10.1109/mgrs.2022.3223267","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":" ","pages":""},"PeriodicalIF":14.6,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47489883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2022-12-01DOI: 10.1109/MGRS.2022.3208357
M. Kochupillai, Matthias Kahl, Michael Schmitt, Hannes Taubenboeck, Xiao Xiang Zhu
Ethics is a central and growing concern in all applications utilizing artificial intelligence (AI). Earth observation (EO) and remote sensing (RS) research relies heavily on both big data and AI or machine learning (ML). While this reliance is not new, with increasing image resolutions and the growing number of EO/RS use cases that have a direct impact on governance, policy, and the lives of people, ethical issues are taking center stage. In this article, we provide scientists engaged with AI for EO (AI4EO) research, 1) a practically useful overview of the key ethical issues emerging in this field, with concrete examples from within EO/RS to explain these issues, and 2) a first road map (flowchart) that scientists can use to identify ethical issues in their ongoing research. With this, we aim to sensitize scientists to these issues and create a bridge to facilitate constructive and regular communication among scientists engaged in AI4EO research, on the one hand, and ethics research, on the other hand. The article also provides detailed illustrations from four AI4EO research fields to explain how scientists can redesign research questions to more effectively grab ethical opportunities to address real-world problems that are otherwise akin to ethical dilemmas with no win-win solution in sight. The article concludes by providing recommendations to institutions that want to support ethically mindful AI4EO research and provides suggestions for future research in this field.
{"title":"Earth Observation and Artificial Intelligence: Understanding emerging ethical issues and opportunities","authors":"M. Kochupillai, Matthias Kahl, Michael Schmitt, Hannes Taubenboeck, Xiao Xiang Zhu","doi":"10.1109/MGRS.2022.3208357","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3208357","url":null,"abstract":"Ethics is a central and growing concern in all applications utilizing artificial intelligence (AI). Earth observation (EO) and remote sensing (RS) research relies heavily on both big data and AI or machine learning (ML). While this reliance is not new, with increasing image resolutions and the growing number of EO/RS use cases that have a direct impact on governance, policy, and the lives of people, ethical issues are taking center stage. In this article, we provide scientists engaged with AI for EO (AI4EO) research, 1) a practically useful overview of the key ethical issues emerging in this field, with concrete examples from within EO/RS to explain these issues, and 2) a first road map (flowchart) that scientists can use to identify ethical issues in their ongoing research. With this, we aim to sensitize scientists to these issues and create a bridge to facilitate constructive and regular communication among scientists engaged in AI4EO research, on the one hand, and ethics research, on the other hand. The article also provides detailed illustrations from four AI4EO research fields to explain how scientists can redesign research questions to more effectively grab ethical opportunities to address real-world problems that are otherwise akin to ethical dilemmas with no win-win solution in sight. The article concludes by providing recommendations to institutions that want to support ethically mindful AI4EO research and provides suggestions for future research in this field.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"90-124"},"PeriodicalIF":14.6,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48127866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2022-12-01DOI: 10.1109/MGRS.2022.3221824
Luís Pedro, Manuel Sa, Rui Fernandes, Flávio Jorge, Sandro Mendonca
Carrying onboard remote sensing systems, Earth observation (EO) satellites provide unique global, systematic, and consistent space-based measurements of natural and man-made phenomena. Measurements can be produced on atmospheric, surface, and subsurface characteristics, properties, and constituents as well as other indicators and related data, enabling comparisons in time and across different parts of the globe, including remote and otherwise inaccessible areas.
{"title":"Protection of Earth Observation Satellites From Radio-Frequency Interference: Policies and practices [Perspectives]","authors":"Luís Pedro, Manuel Sa, Rui Fernandes, Flávio Jorge, Sandro Mendonca","doi":"10.1109/MGRS.2022.3221824","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3221824","url":null,"abstract":"Carrying onboard remote sensing systems, Earth observation (EO) satellites provide unique global, systematic, and consistent space-based measurements of natural and man-made phenomena. Measurements can be produced on atmospheric, surface, and subsurface characteristics, properties, and constituents as well as other indicators and related data, enabling comparisons in time and across different parts of the globe, including remote and otherwise inaccessible areas.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"278-288"},"PeriodicalIF":14.6,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43151152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2022-12-01DOI: 10.1109/MGRS.2022.3219935
Ronny Hansch, C. Persello, G. Vivone, J. Castillo Navarro, Alexandre Boulch, S. Lefèvre, Bertrand Le Saux
The Image Analysis and Data Fusion (IADF) Technical Committee (TC) of the IEEE Geoscience and Remote Sensing Society (GRSS) has been organizing the annual Data Fusion Contest (DFC) since 2006. The contest promotes the development of methods for extracting geospatial information from large-scale, multisensor, multimodal, and multitemporal data. It aims to propose new problem settings that are challenging to address with existing techniques and to establish new benchmarks for scientific challenges in remote sensing image analysis [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19].
{"title":"Report on the 2022 IEEE Geoscience and Remote Sensing Society Data Fusion Contest: Semisupervised Learning [Technical Committees]","authors":"Ronny Hansch, C. Persello, G. Vivone, J. Castillo Navarro, Alexandre Boulch, S. Lefèvre, Bertrand Le Saux","doi":"10.1109/MGRS.2022.3219935","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3219935","url":null,"abstract":"The Image Analysis and Data Fusion (IADF) Technical Committee (TC) of the IEEE Geoscience and Remote Sensing Society (GRSS) has been organizing the annual Data Fusion Contest (DFC) since 2006. The contest promotes the development of methods for extracting geospatial information from large-scale, multisensor, multimodal, and multitemporal data. It aims to propose new problem settings that are challenging to address with existing techniques and to establish new benchmarks for scientific challenges in remote sensing image analysis <xref ref-type=\"bibr\" rid=\"ref1\">[1]</xref>, <xref ref-type=\"bibr\" rid=\"ref2\">[2]</xref>, <xref ref-type=\"bibr\" rid=\"ref3\">[3]</xref>, <xref ref-type=\"bibr\" rid=\"ref4\">[4]</xref>, <xref ref-type=\"bibr\" rid=\"ref5\">[5]</xref>, <xref ref-type=\"bibr\" rid=\"ref6\">[6]</xref>, <xref ref-type=\"bibr\" rid=\"ref7\">[7]</xref>, <xref ref-type=\"bibr\" rid=\"ref8\">[8]</xref>, <xref ref-type=\"bibr\" rid=\"ref9\">[9]</xref>, <xref ref-type=\"bibr\" rid=\"ref10\">[10]</xref>, <xref ref-type=\"bibr\" rid=\"ref11\">[11]</xref>, <xref ref-type=\"bibr\" rid=\"ref12\">[12]</xref>, <xref ref-type=\"bibr\" rid=\"ref13\">[13]</xref>, <xref ref-type=\"bibr\" rid=\"ref14\">[14]</xref>, <xref ref-type=\"bibr\" rid=\"ref15\">[15]</xref>, <xref ref-type=\"bibr\" rid=\"ref16\">[16]</xref>, <xref ref-type=\"bibr\" rid=\"ref17\">[17]</xref>, <xref ref-type=\"bibr\" rid=\"ref18\">[18]</xref>, <xref ref-type=\"bibr\" rid=\"ref19\">[19]</xref>.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"270-273"},"PeriodicalIF":14.6,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41403091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2022-12-01DOI: 10.1109/MGRS.2022.3219584
Xiao Xiang Zhu, Yuanyuan Wang, M. Kochupillai, M. Werner, Matthias Häberle, E. J. Hoffmann, H. Taubenböck, D. Tuia, A. Levering, Nathan Jacobs, Anna M. Kruspe, Karam Abdulahhad
As unconventional sources of geoinformation, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multiperspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing (RS) data, geoinformation from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysis-ready data preparation and data management, geoinformation extraction from social media text messages and images, and the fusion of social media and RS data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geoinformation harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geoapplications. We encourage the community to join forces by sharing their code and data.
{"title":"Geoinformation Harvesting From Social Media Data: A community remote sensing approach","authors":"Xiao Xiang Zhu, Yuanyuan Wang, M. Kochupillai, M. Werner, Matthias Häberle, E. J. Hoffmann, H. Taubenböck, D. Tuia, A. Levering, Nathan Jacobs, Anna M. Kruspe, Karam Abdulahhad","doi":"10.1109/MGRS.2022.3219584","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3219584","url":null,"abstract":"As unconventional sources of geoinformation, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multiperspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing (RS) data, geoinformation from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysis-ready data preparation and data management, geoinformation extraction from social media text messages and images, and the fusion of social media and RS data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geoinformation harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geoapplications. We encourage the community to join forces by sharing their code and data.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"150-180"},"PeriodicalIF":14.6,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41459511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2022-12-01DOI: 10.1109/MGRS.2022.3205764
Swarna Laxmi Panda, S. Maiti, U. K. Sahoo
Velocity estimation is one of the most crucial tasks in ground-penetrating radar (GPR) surveys. It is an integral part of GPR data analysis for interpreting GPR-generated images of subsurface media. Error in velocity estimation leads to wrong interpretations of underground scenarios. Since the beginning of GPR development, researchers have proposed various velocity estimation procedures. In this article, a survey of different subsurface propagation velocity estimation techniques is presented. Based on the survey methods, GPR environment, and mathematical models involved, different available techniques are presented in a classified manner. A few techniques are experimentally validated and analyzed for their performance. Finally, the impact of wave velocity on GPR imaging is demonstrated on practical GPR measurement data.
{"title":"Subsurface Propagation Velocity Estimation Methods in Ground-Penetrating Radar: A review","authors":"Swarna Laxmi Panda, S. Maiti, U. K. Sahoo","doi":"10.1109/MGRS.2022.3205764","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3205764","url":null,"abstract":"Velocity estimation is one of the most crucial tasks in ground-penetrating radar (GPR) surveys. It is an integral part of GPR data analysis for interpreting GPR-generated images of subsurface media. Error in velocity estimation leads to wrong interpretations of underground scenarios. Since the beginning of GPR development, researchers have proposed various velocity estimation procedures. In this article, a survey of different subsurface propagation velocity estimation techniques is presented. Based on the survey methods, GPR environment, and mathematical models involved, different available techniques are presented in a classified manner. A few techniques are experimentally validated and analyzed for their performance. Finally, the impact of wave velocity on GPR imaging is demonstrated on practical GPR measurement data.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"70-89"},"PeriodicalIF":14.6,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45551877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2022-12-01DOI: 10.1109/MGRS.2022.3198643
Yuxuan Li, Licheng Jiao, Zhongjian Huang, Xin Zhang, Ruohan Zhang, Xue Song, Chenxi Tian, Zixiao Zhang, F. Liu, Yang Shuyuan, B. Hou, Wenping Ma, Xu Liu, Lingling Li
As a fundamental task for research in satellite videos (SVs), object tracking is used to track the target of interest in traffic evaluation, military security, and so forth. The current satellite technology in the remote sensing field makes it possible to track moving targets with a relatively high frame rate and image resolution. However, objects under this special view are often small and blurry, making it hard to extract deep features effectively. As a result, quite a few deep learning (DL) methods were proposed for object tracking in SVs. In addition, evaluation criteria for daily life videos (DLVs) are not fully applicable to SVs, which always get low precision evaluation results for tiny objects. In this article, we make three contributions to the research on SVs. First, a new single object tracking (SOT) dataset, named SV248S, is proposed, including 248 sequences with high-precision manual annotation, and 10 kinds of attribute tags are designed to completely represent the difficulties during tracking. Second, two high-precision evaluation methods are proposed, especially for small object tracking. Finally, 28 DL-based state-of-the-art (SOTA) tracking methods, from 2017 to 2021, covering popular frameworks, are evaluated and compared on the proposed dataset. Furthermore, some guidelines for effectively adopting DL-based methods are summarized based on comprehensive experimental results.
{"title":"Deep Learning-Based Object Tracking in Satellite Videos: A comprehensive survey with a new dataset","authors":"Yuxuan Li, Licheng Jiao, Zhongjian Huang, Xin Zhang, Ruohan Zhang, Xue Song, Chenxi Tian, Zixiao Zhang, F. Liu, Yang Shuyuan, B. Hou, Wenping Ma, Xu Liu, Lingling Li","doi":"10.1109/MGRS.2022.3198643","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3198643","url":null,"abstract":"As a fundamental task for research in satellite videos (SVs), object tracking is used to track the target of interest in traffic evaluation, military security, and so forth. The current satellite technology in the remote sensing field makes it possible to track moving targets with a relatively high frame rate and image resolution. However, objects under this special view are often small and blurry, making it hard to extract deep features effectively. As a result, quite a few deep learning (DL) methods were proposed for object tracking in SVs. In addition, evaluation criteria for daily life videos (DLVs) are not fully applicable to SVs, which always get low precision evaluation results for tiny objects. In this article, we make three contributions to the research on SVs. First, a new single object tracking (SOT) dataset, named SV248S, is proposed, including 248 sequences with high-precision manual annotation, and 10 kinds of attribute tags are designed to completely represent the difficulties during tracking. Second, two high-precision evaluation methods are proposed, especially for small object tracking. Finally, 28 DL-based state-of-the-art (SOTA) tracking methods, from 2017 to 2021, covering popular frameworks, are evaluated and compared on the proposed dataset. Furthermore, some guidelines for effectively adopting DL-based methods are summarized based on comprehensive experimental results.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"181-212"},"PeriodicalIF":14.6,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46186687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 : 2022-12-01DOI: 10.1109/MGRS.2022.3219778
Adriano Camps, J. F. Muñoz-Martín, J. A. Ruiz-de-Azua, L. Fernández, A. Pérez-Portero, David Llavería, C. Herbert, M. Pablos, A. Golkar, A. Gutierrez, Carlos Antonio, J. Bandeiras, Jorge Andrade, D. Cordeiro, Simone Briatore, Nicola Garzaniti, F. Nichele, R. Mozzillo, A. Piumatti, Margherita Cardi, Marco Esposito, C. van Dijk, N. Vercruyssen, J. Barbosa, John R. Hefele, R. Koeleman, B. C. Domínguez, M. Pastena, G. Filippazzo, A. Reagan
The Federated Satellite Systems/3Cat-5 (FSSCat) mission was the winner of the European Space Agency (ESA) Sentinel Small Satellite (S3) Challenge and overall winner of the 2017 Copernicus Masters competition. It consisted of two six-unit CubeSats. The Earth observation payloads were 1) the Flexible Microwave Payload 2 (FMPL-2) onboard 3Cat-5/A, an L-band microwave radiometer and GNSS reflectometer (GNSS-R) implemented using a software-defined radio (SDR), and 2) the HyperScout-2 onboard 3Cat-5/B, a hyperspectral camera, with the first experiment using artificial intelligence to discard cloudy images. FSSCat was launched on 3 September 2020 and injected into a 535-km synchronous orbit. 3Cat-5/A was operated for three months until the payload was probably damaged by a solar flare and coronal mass ejection. During this time, all scientific requirements were met, including the generation of coarse-resolution and downscaled soil moisture (SM) maps, sea ice extent (SIE) maps, concentration and thickness maps, and even wind speed (WS) and sea surface salinity (SSS) maps, which were not originally foreseen. 3Cat-5/B was operated a few more months until the number of images acquired met the requirements. This article briefly describes the FSSCat mission and the FMPL-2 payload and summarizes the main scientific results.
{"title":"FSSCat: The Federated Satellite Systems 3Cat Mission: Demonstrating the capabilities of CubeSats to monitor essential climate variables of the water cycle [Instruments and Missions]","authors":"Adriano Camps, J. F. Muñoz-Martín, J. A. Ruiz-de-Azua, L. Fernández, A. Pérez-Portero, David Llavería, C. Herbert, M. Pablos, A. Golkar, A. Gutierrez, Carlos Antonio, J. Bandeiras, Jorge Andrade, D. Cordeiro, Simone Briatore, Nicola Garzaniti, F. Nichele, R. Mozzillo, A. Piumatti, Margherita Cardi, Marco Esposito, C. van Dijk, N. Vercruyssen, J. Barbosa, John R. Hefele, R. Koeleman, B. C. Domínguez, M. Pastena, G. Filippazzo, A. Reagan","doi":"10.1109/MGRS.2022.3219778","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3219778","url":null,"abstract":"The Federated Satellite Systems/3Cat-5 (FSSCat) mission was the winner of the European Space Agency (ESA) Sentinel Small Satellite (S3) Challenge and overall winner of the 2017 Copernicus Masters competition. It consisted of two six-unit CubeSats. The Earth observation payloads were 1) the Flexible Microwave Payload 2 (FMPL-2) onboard 3Cat-5/A, an L-band microwave radiometer and GNSS reflectometer (GNSS-R) implemented using a software-defined radio (SDR), and 2) the HyperScout-2 onboard 3Cat-5/B, a hyperspectral camera, with the first experiment using artificial intelligence to discard cloudy images. FSSCat was launched on 3 September 2020 and injected into a 535-km synchronous orbit. 3Cat-5/A was operated for three months until the payload was probably damaged by a solar flare and coronal mass ejection. During this time, all scientific requirements were met, including the generation of coarse-resolution and downscaled soil moisture (SM) maps, sea ice extent (SIE) maps, concentration and thickness maps, and even wind speed (WS) and sea surface salinity (SSS) maps, which were not originally foreseen. 3Cat-5/B was operated a few more months until the number of images acquired met the requirements. This article briefly describes the FSSCat mission and the FMPL-2 payload and summarizes the main scientific results.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"260-269"},"PeriodicalIF":14.6,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44884764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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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