Pub Date : 2023-03-01DOI: 10.1109/MGRS.2022.3226525
Burak Ekim, T. Stomberg, R. Roscher, Michael Schmitt
The advancement in deep learning (DL) techniques has led to a notable increase in the number and size of annotated datasets in a variety of domains, with remote sensing (RS) being no exception [1]. Also, an increase in Earth observation (EO) missions and the easy access to globally available and free geodata have opened up new research opportunities. Although numerous RS datasets have been published in the past years [2], [3], [4], [5], [6], most of them addressed tasks concerning man-made environments, such as building footprint extraction and road network classification, leaving the environmental and ecology-related subareas of RS underrepresented. Nevertheless, environmental protection has always been an important topic in the RS community, with RS being a useful tool to support conservation policies and strategies combating challenges such as deforestation and loss of biodiversity [7], [8], [9]. Thus, in this article, to meet the pressing need to better understand the nature we are living in, we introduce a novel task of wilderness mapping and advertise the MapInWild dataset [10]—a multimodal large-scale benchmark dataset designed for the task of wilderness mapping from space.
{"title":"MapInWild: A remote sensing dataset to address the question of what makes nature wild [Software and Data Sets]","authors":"Burak Ekim, T. Stomberg, R. Roscher, Michael Schmitt","doi":"10.1109/MGRS.2022.3226525","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3226525","url":null,"abstract":"The advancement in deep learning (DL) techniques has led to a notable increase in the number and size of annotated datasets in a variety of domains, with remote sensing (RS) being no exception <xref ref-type=\"bibr\" rid=\"ref1\">[1]</xref>. Also, an increase in Earth observation (EO) missions and the easy access to globally available and free geodata have opened up new research opportunities. Although numerous RS datasets have been published in the past years <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>, most of them addressed tasks concerning man-made environments, such as building footprint extraction and road network classification, leaving the environmental and ecology-related subareas of RS underrepresented. Nevertheless, environmental protection has always been an important topic in the RS community, with RS being a useful tool to support conservation policies and strategies combating challenges such as deforestation and loss of biodiversity <xref ref-type=\"bibr\" rid=\"ref7\">[7]</xref>, <xref ref-type=\"bibr\" rid=\"ref8\">[8]</xref>, <xref ref-type=\"bibr\" rid=\"ref9\">[9]</xref>. Thus, in this article, to meet the pressing need to better understand the nature we are living in, we introduce a novel task of wilderness mapping and advertise the MapInWild dataset <xref ref-type=\"bibr\" rid=\"ref10\">[10]</xref>—a multimodal large-scale benchmark dataset designed for the task of wilderness mapping from space.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"11 1","pages":"103-114"},"PeriodicalIF":14.6,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45468027","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 : 2023-03-01DOI: 10.1109/MGRS.2023.3236919
Hui Wang, Yushan Guo, Qiang Zhao
From 2 to 4 November 2022, the third China International Synthetic Aperture Radar (SAR) Symposium (CISS), sponsored by the Shanghai Institute of Satellite Engineering, was successfully held at the Jinjiang Metropolo Hotel Minhang, Shanghai. The CISS is an international academic conference with extensive authority, knowledge, and interaction. The symposium aims to build a high-level and international academic communication platform for scholars and researchers in the SAR field, lead the direction of technology development in the SAR field, and promote technology innovation in related fields. CISS 2022 was technically cosponsored by IEEE and the IEEE Geoscience and Remote Sensing Society (GRSS). More than 180 invited experts and scholars from China, Germany, New Zealand, Poland, and other countries assembled to discuss the latest developments and achievements in SAR-related areas, including hardware systems, processing techniques, advanced applications, and so on. CISS 2022 was held online and offline, and more than 150 participants attended offline live events. Nine experts (Figure 1) gave keynotes during the opening ceremony. During the conference, 72 researchers presented their work in oral form, while 94 reports were presented in poster form.
{"title":"The Third China International Synthetic Aperture Radar Symposium [Conference Reports]","authors":"Hui Wang, Yushan Guo, Qiang Zhao","doi":"10.1109/MGRS.2023.3236919","DOIUrl":"https://doi.org/10.1109/MGRS.2023.3236919","url":null,"abstract":"From 2 to 4 November 2022, the third China International Synthetic Aperture Radar (SAR) Symposium (CISS), sponsored by the Shanghai Institute of Satellite Engineering, was successfully held at the Jinjiang Metropolo Hotel Minhang, Shanghai. The CISS is an international academic conference with extensive authority, knowledge, and interaction. The symposium aims to build a high-level and international academic communication platform for scholars and researchers in the SAR field, lead the direction of technology development in the SAR field, and promote technology innovation in related fields. CISS 2022 was technically cosponsored by IEEE and the IEEE Geoscience and Remote Sensing Society (GRSS). More than 180 invited experts and scholars from China, Germany, New Zealand, Poland, and other countries assembled to discuss the latest developments and achievements in SAR-related areas, including hardware systems, processing techniques, advanced applications, and so on. CISS 2022 was held online and offline, and more than 150 participants attended offline live events. Nine experts (Figure 1) gave keynotes during the opening ceremony. During the conference, 72 researchers presented their work in oral form, while 94 reports were presented in poster form.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"11 1","pages":"115-C3"},"PeriodicalIF":14.6,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45110859","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 : 2023-03-01DOI: 10.1109/mgrs.2023.3240233
C. Persello, R. Hänsch, G. Vivone, Kaiqiang Chen, Zhiyuan Yan, Deke Tang, Hai Huang, Michael Schmitt, Xian Sun
{"title":"2023 IEEE GRSS Data Fusion Contest: Large-Scale Fine-Grained Building Classification for Semantic Urban Reconstruction [Technical Committees]","authors":"C. Persello, R. Hänsch, G. Vivone, Kaiqiang Chen, Zhiyuan Yan, Deke Tang, Hai Huang, Michael Schmitt, Xian Sun","doi":"10.1109/mgrs.2023.3240233","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3240233","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":" ","pages":""},"PeriodicalIF":14.6,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42828235","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 : 2023-03-01DOI: 10.1109/MGRS.2023.3237465
M. Datcu, Zhongling Huang, A. Anghel, Juanping Zhao, R. Cacoveanu
The recognition or understanding of the scenes observed with a synthetic aperture radar (SAR) system requires a broader range of cues beyond the spatial context. These encompass but are not limited to the imaging geometry, imaging mode, properties of the Fourier spectrum of the images, or behavior of the polarimetric signatures. In this article, we propose a change of paradigm for explainability in data science for the case of SAR data to ground explainable artificial intelligence (XAI) for SAR. It aims to use explainable data transformations based on well-established models to generate inputs for AI methods, to provide knowledgeable feedback for the training process, and to learn or improve high-complexity unknown or unformalized models from the data.
{"title":"Explainable, Physics-Aware, Trustworthy Artificial Intelligence: A paradigm shift for synthetic aperture radar","authors":"M. Datcu, Zhongling Huang, A. Anghel, Juanping Zhao, R. Cacoveanu","doi":"10.1109/MGRS.2023.3237465","DOIUrl":"https://doi.org/10.1109/MGRS.2023.3237465","url":null,"abstract":"The recognition or understanding of the scenes observed with a synthetic aperture radar (SAR) system requires a broader range of cues beyond the spatial context. These encompass but are not limited to the imaging geometry, imaging mode, properties of the Fourier spectrum of the images, or behavior of the polarimetric signatures. In this article, we propose a change of paradigm for explainability in data science for the case of SAR data to ground explainable artificial intelligence (XAI) for SAR. It aims to use explainable data transformations based on well-established models to generate inputs for AI methods, to provide knowledgeable feedback for the training process, and to learn or improve high-complexity unknown or unformalized models from the data.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"11 1","pages":"8-25"},"PeriodicalIF":14.6,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42612388","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 : 2023-03-01DOI: 10.1109/mgrs.2023.3244357
P. Gamba
{"title":"Not the Usual Editorial [From the Editor]","authors":"P. Gamba","doi":"10.1109/mgrs.2023.3244357","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3244357","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":" ","pages":""},"PeriodicalIF":14.6,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48365976","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 : 2022-12-19DOI: 10.1109/MGRS.2023.3272825
Yonghao Xu, Tao Bai, Weikang Yu, Shizhen Chang, P. Atkinson, Pedram Ghamisi
Recent advances in artificial intelligence (AI) have significantly intensified research in the geoscience and remote sensing (RS) field. AI algorithms, especially deep learning-based ones, have been developed and applied widely to RS data analysis. The successful application of AI covers almost all aspects of Earth-observation (EO) missions, from low-level vision tasks like superresolution, denoising, and inpainting, to high-level vision tasks like scene classification, object detection, and semantic segmentation. Although AI techniques enable researchers to observe and understand the earth more accurately, the vulnerability and uncertainty of AI models deserve further attention, considering that many geoscience and RS tasks are highly safety critical. This article reviews the current development of AI security in the geoscience and RS field, covering the following five important aspects: adversarial attack, backdoor attack, federated learning (FL), uncertainty, and explainability. Moreover, the potential opportunities and trends are discussed to provide insights for future research. To the best of the authors’ knowledge, this article is the first attempt to provide a systematic review of AI security-related research in the geoscience and RS community. Available code and datasets are also listed in the article to move this vibrant field of research forward.
{"title":"AI Security for Geoscience and Remote Sensing: Challenges and future trends","authors":"Yonghao Xu, Tao Bai, Weikang Yu, Shizhen Chang, P. Atkinson, Pedram Ghamisi","doi":"10.1109/MGRS.2023.3272825","DOIUrl":"https://doi.org/10.1109/MGRS.2023.3272825","url":null,"abstract":"Recent advances in artificial intelligence (AI) have significantly intensified research in the geoscience and remote sensing (RS) field. AI algorithms, especially deep learning-based ones, have been developed and applied widely to RS data analysis. The successful application of AI covers almost all aspects of Earth-observation (EO) missions, from low-level vision tasks like superresolution, denoising, and inpainting, to high-level vision tasks like scene classification, object detection, and semantic segmentation. Although AI techniques enable researchers to observe and understand the earth more accurately, the vulnerability and uncertainty of AI models deserve further attention, considering that many geoscience and RS tasks are highly safety critical. This article reviews the current development of AI security in the geoscience and RS field, covering the following five important aspects: adversarial attack, backdoor attack, federated learning (FL), uncertainty, and explainability. Moreover, the potential opportunities and trends are discussed to provide insights for future research. To the best of the authors’ knowledge, this article is the first attempt to provide a systematic review of AI security-related research in the geoscience and RS community. Available code and datasets are also listed in the article to move this vibrant field of research forward.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"11 1","pages":"60-85"},"PeriodicalIF":14.6,"publicationDate":"2022-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48044346","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}
In recent years, with the increasing number of Earth observation satellites and the popularization and application of various sensors, remote sensing data have shown a rapid growth trend and present typical big data characteristics. The continuous enrichment of remote sensing data has provided large information resources for Earth science research and promoted the wide application of remote sensing technology in resources, ecology, environment, energy, health, urban management, and so on. However, mining information from multisource heterogeneous remote sensing big data, which requires a large amount of computing power, has many challenges in terms of generality, security, and timeliness. In this article, we summarize the existing research on high-performance computing (HPC) and high-throughput computing (HTC) technologies toward improving the processing efficiency of remote sensing big data. We also analyze the problems and challenges of HPC/HTC technologies in the storage, computation, and analysis of remote sensing big data. Finally, we predict the trend of remote sensing big data processing in the direction of HPC/HTC.
{"title":"State of the Art: High-Performance and High-Throughput Computing for Remote Sensing Big Data","authors":"Shenmin Zhang, Yong Xue, Xiran Zhou, Xiaopeng Zhang, Wenhao Liu, Kaiyuan Li, Runze Liu","doi":"10.1109/MGRS.2022.3204590","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3204590","url":null,"abstract":"In recent years, with the increasing number of Earth observation satellites and the popularization and application of various sensors, remote sensing data have shown a rapid growth trend and present typical big data characteristics. The continuous enrichment of remote sensing data has provided large information resources for Earth science research and promoted the wide application of remote sensing technology in resources, ecology, environment, energy, health, urban management, and so on. However, mining information from multisource heterogeneous remote sensing big data, which requires a large amount of computing power, has many challenges in terms of generality, security, and timeliness. In this article, we summarize the existing research on high-performance computing (HPC) and high-throughput computing (HTC) technologies toward improving the processing efficiency of remote sensing big data. We also analyze the problems and challenges of HPC/HTC technologies in the storage, computation, and analysis of remote sensing big data. Finally, we predict the trend of remote sensing big data processing in the direction of HPC/HTC.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"125-149"},"PeriodicalIF":14.6,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42700149","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.2022.3223624
Dai Hai Ton That, Priya Deshpande, Khalid Belhajjame, Muthukumaran Ramasubramanian, Vishal Perekadan, Nishan Pantha, Todd Mahood, Kesheng Wu
In recognition of emerging new data management technologies, the IEEE Earth Science Informatics (ESI) Technical Committee (TC) recently formed a new Working Group on Databases in Remote Sensing (DBRS). This is a report about the first workshop organized by the DBRS-WG to gather information about technologies that could effectively store, query, search, and analyze remote sensing data. This workshop called for research talks relevant to geoscience and remote data sensing technologies. The hybrid event, hosted at the Earth System Science Center (ESSC), University of Alabama in Huntsville (UAH), drew 16 submissions from around the world. The workshop also featured five invited speakers to talk about the advanced data management technologies and important application drivers. The WG believes that this inaugural event is a good start of a community around remote sensing data management.
{"title":"Report on the IEEE GRSS Workshop on Remote Sensing Data Management Technologies in Geoscience 2022 [Technical Committees]","authors":"Dai Hai Ton That, Priya Deshpande, Khalid Belhajjame, Muthukumaran Ramasubramanian, Vishal Perekadan, Nishan Pantha, Todd Mahood, Kesheng Wu","doi":"10.1109/MGRS.2022.3223624","DOIUrl":"https://doi.org/10.1109/MGRS.2022.3223624","url":null,"abstract":"In recognition of emerging new data management technologies, the IEEE Earth Science Informatics (ESI) Technical Committee (TC) recently formed a new Working Group on Databases in Remote Sensing (DBRS). This is a report about the first workshop organized by the DBRS-WG to gather information about technologies that could effectively store, query, search, and analyze remote sensing data. This workshop called for research talks relevant to geoscience and remote data sensing technologies. The hybrid event, hosted at the Earth System Science Center (ESSC), University of Alabama in Huntsville (UAH), drew 16 submissions from around the world. The workshop also featured five invited speakers to talk about the advanced data management technologies and important application drivers. The WG believes that this inaugural event is a good start of a community around remote sensing data management.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":"10 1","pages":"273-277"},"PeriodicalIF":14.6,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48057299","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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