Pub Date : 2023-09-01DOI: 10.1109/mgrs.2023.3302813
Manil Maskey, Gabriele Cavallaro, Dora Blanco Heras, Paolo Fraccaro, Blair Edwards, Iksha Gurung, Brian Freitag, Muthukumaran Ramasubramanian, Johannes Jakubik, Linsong Chu, Raghu Ganti, Rahul Ramachandran, Kommy Weldemariam, Sujit Roy, Carlos Costa, Alex Corvin, Anish Asthana
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
提供社会信息，可能包括新闻，评论或技术笔记，从业者和研究人员应该感兴趣。
{"title":"A Summer School Session on Mastering Geospatial Artificial Intelligence: From Data Production to Artificial Intelligence Foundation Model Development and Downstream Applications [Technical Committees]","authors":"Manil Maskey, Gabriele Cavallaro, Dora Blanco Heras, Paolo Fraccaro, Blair Edwards, Iksha Gurung, Brian Freitag, Muthukumaran Ramasubramanian, Johannes Jakubik, Linsong Chu, Raghu Ganti, Rahul Ramachandran, Kommy Weldemariam, Sujit Roy, Carlos Costa, Alex Corvin, Anish Asthana","doi":"10.1109/mgrs.2023.3302813","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3302813","url":null,"abstract":"Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134915063","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 : 2023-09-01DOI: 10.1109/mgrs.2023.3281651
Yi Wang, Nassim Ait Ali Braham, Zhitong Xiong, Chenying Liu, Conrad M. Albrecht, Xiao Xiang Zhu
Self-supervised pretraining bears the potential to generate expressive representations from large-scale Earth observation (EO) data without human annotation. However, most existing pretraining in the field is based on ImageNet or medium-sized, labeled remote sensing (RS) datasets. In this article, we share an unlabeled dataset Self-Supervised Learning for Earth Observation-Sentinel-1/2 ( SSL4EO - S12 ) to assemble a large-scale, global, multimodal, and multiseasonal corpus of satellite imagery. We demonstrate SSL4EO-S12 to succeed in self-supervised pretraining for a set of representative methods: momentum contrast (MoCo), self-distillation with no labels (DINO), masked autoencoders (MAE), and data2vec, and multiple downstream applications, including scene classification, semantic segmentation, and change detection. Our benchmark results prove the effectiveness of SSL4EO-S12 compared to existing datasets. The dataset, related source code, and pretrained models are available at https://github.com/zhu-xlab/SSL4EO-S12 .
{"title":"SSL4EO-S12: A large-scale multimodal, multitemporal dataset for self-supervised learning in Earth observation [Software and Data Sets]","authors":"Yi Wang, Nassim Ait Ali Braham, Zhitong Xiong, Chenying Liu, Conrad M. Albrecht, Xiao Xiang Zhu","doi":"10.1109/mgrs.2023.3281651","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3281651","url":null,"abstract":"Self-supervised pretraining bears the potential to generate expressive representations from large-scale Earth observation (EO) data without human annotation. However, most existing pretraining in the field is based on ImageNet or medium-sized, labeled remote sensing (RS) datasets. In this article, we share an unlabeled dataset <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">Self-Supervised Learning for Earth Observation-Sentinel-1/2</i> ( <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">SSL4EO</i> - <italic xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">S12</i> ) to assemble a large-scale, global, multimodal, and multiseasonal corpus of satellite imagery. We demonstrate SSL4EO-S12 to succeed in self-supervised pretraining for a set of representative methods: momentum contrast (MoCo), self-distillation with no labels (DINO), masked autoencoders (MAE), and data2vec, and multiple downstream applications, including scene classification, semantic segmentation, and change detection. Our benchmark results prove the effectiveness of SSL4EO-S12 compared to existing datasets. The dataset, related source code, and pretrained models are available at <uri xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">https://github.com/zhu-xlab/SSL4EO-S12</uri> .","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134917487","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 : 2023-07-13DOI: 10.1109/mgrs.2023.3277234
Mariko Burgin
Hello and nice to see you again! My name is Mariko Burgin, and I am the IEEE Geoscience and Remote Sensing Society (GRSS) President. You can reach me at president@ieee-grss.org and @GRSS_President on Twitter.
{"title":"Letter From the President [President’s Message]","authors":"Mariko Burgin","doi":"10.1109/mgrs.2023.3277234","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3277234","url":null,"abstract":"Hello and nice to see you again! My name is Mariko Burgin, and I am the IEEE Geoscience and Remote Sensing Society (GRSS) President. You can reach me at president@ieee-grss.org and @GRSS_President on Twitter.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":14.6,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71512810","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 : 2023-06-01DOI: 10.1109/mgrs.2023.3278369
Gui Gao, Hanwen Yu, M. Migliaccio
Interpreting marine targets using remote sensing can provide critical information for various applications, including environmental monitoring, oceanographic research, navigation, and resource management. With the development of observation systems, the ocean information acquired is multi-source and multi-dimension. Data fusion, as a general and popular multi-discipline approach, can effectively use the obtained remote sensing data to improve the accuracy and reliability of oceanic target interpretation. This special issue will present an array of tutorial-like overview papers that aim to invite contributions on the latest developments and advances in the field of fusion techniques for oceanic target interpretation. In agreement with the approach and style of the Magazine, the contributors to this special issue will pay strong attention to creating a balanced mix between ensuring scientific depth, and dissemination to a wide public which would encompass remote sensing scientists, practitioners, and students.
{"title":"Special issue on “Data Fusion Techniques for Oceanic Target Interpretation”","authors":"Gui Gao, Hanwen Yu, M. Migliaccio","doi":"10.1109/mgrs.2023.3278369","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3278369","url":null,"abstract":"Interpreting marine targets using remote sensing can provide critical information for various applications, including environmental monitoring, oceanographic research, navigation, and resource management. With the development of observation systems, the ocean information acquired is multi-source and multi-dimension. Data fusion, as a general and popular multi-discipline approach, can effectively use the obtained remote sensing data to improve the accuracy and reliability of oceanic target interpretation. This special issue will present an array of tutorial-like overview papers that aim to invite contributions on the latest developments and advances in the field of fusion techniques for oceanic target interpretation. In agreement with the approach and style of the Magazine, the contributors to this special issue will pay strong attention to creating a balanced mix between ensuring scientific depth, and dissemination to a wide public which would encompass remote sensing scientists, practitioners, and students.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":14.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44063380","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 : 2023-06-01DOI: 10.1109/MGRS.2023.3269979
Agata M. Wijata, Michel-François Foulon, Yves Bobichon, R. Vitulli, M. Celesti, R. Camarero, Gianluigi Di Cosimo, F. Gascon, N. Longépé, J. Nieke, Michal Gumiela, J. Nalepa
Recent advances in remote sensing hyperspectral imaging and artificial intelligence (AI) bring exciting opportunities to various fields of science and industry that can directly benefit from in-orbit data processing. Taking AI into space may accelerate the response to various events, as massively large raw hyperspectral images (HSIs) can be turned into useful information onboard a satellite; hence, the images’ transfer to the ground becomes much faster and offers enormous scalability of AI solutions to areas across the globe. However, there are numerous challenges related to hardware and energy constraints, resource frugality of (deep) machine learning models, availability of ground truth data, and building trust in AI-based solutions. Unbiased, objective, and interpretable selection of an AI application is of paramount importance for emerging missions, as it influences all aspects of satellite design and operation. In this article, we tackle this issue and introduce a quantifiable procedure for objectively assessing potential AI applications considered for onboard deployment. To prove the flexibility of the suggested technique, we utilize the approach to evaluate AI applications for two fundamentally different missions: the Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) [European Union/European Space Agency (ESA)] and the 6U nanosatellite Intuition-1 (KP Labs). We believe that our standardized process may become an important tool for maximizing the outcome of Earth observation (EO) missions through selecting the most relevant onboard AI applications in terms of scientific and industrial outcomes.
{"title":"Taking Artificial Intelligence Into Space Through Objective Selection of Hyperspectral Earth Observation Applications: To bring the “brain” close to the “eyes” of satellite missions","authors":"Agata M. Wijata, Michel-François Foulon, Yves Bobichon, R. Vitulli, M. Celesti, R. Camarero, Gianluigi Di Cosimo, F. Gascon, N. Longépé, J. Nieke, Michal Gumiela, J. Nalepa","doi":"10.1109/MGRS.2023.3269979","DOIUrl":"https://doi.org/10.1109/MGRS.2023.3269979","url":null,"abstract":"Recent advances in remote sensing hyperspectral imaging and artificial intelligence (AI) bring exciting opportunities to various fields of science and industry that can directly benefit from in-orbit data processing. Taking AI into space may accelerate the response to various events, as massively large raw hyperspectral images (HSIs) can be turned into useful information onboard a satellite; hence, the images’ transfer to the ground becomes much faster and offers enormous scalability of AI solutions to areas across the globe. However, there are numerous challenges related to hardware and energy constraints, resource frugality of (deep) machine learning models, availability of ground truth data, and building trust in AI-based solutions. Unbiased, objective, and interpretable selection of an AI application is of paramount importance for emerging missions, as it influences all aspects of satellite design and operation. In this article, we tackle this issue and introduce a quantifiable procedure for objectively assessing potential AI applications considered for onboard deployment. To prove the flexibility of the suggested technique, we utilize the approach to evaluate AI applications for two fundamentally different missions: the Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) [European Union/European Space Agency (ESA)] and the 6U nanosatellite Intuition-1 (KP Labs). We believe that our standardized process may become an important tool for maximizing the outcome of Earth observation (EO) missions through selecting the most relevant onboard AI applications in terms of scientific and industrial outcomes.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":14.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47106755","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 : 2023-06-01DOI: 10.1109/mgrs.2023.3278368
{"title":"Call for Papers: IEEE Geoscience and remote sensing magazine","authors":"","doi":"10.1109/mgrs.2023.3278368","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3278368","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136280998","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}
Onboard information fusion for multisatellites, which is based on spatial computing mode, can improve the satellites’ capability, such as the spatial–temporal coverage, detection accuracy, recognition confidence, position precision, and prediction precision for disaster monitoring, maritime surveillance, and other emergent or continuous persistent observing situations. First, we analyze the necessity of onboard information fusion. Next, the recent onboard processing developments are summarized and the existing problems are discussed. Furthermore, the key technologies and concepts of onboard information fusion are summarized in the fields of feature representation, association, feature-level fusion, spatial computing architecture, and other issues. Finally, the future developments of onboard information fusion are investigated and discussed.
{"title":"Onboard Information Fusion for Multisatellite Collaborative Observation: Summary, challenges, and perspectives","authors":"Gui Gao, Libo Yao, Wenfeng Li, Linlin Zhang, Maolin Zhang","doi":"10.1109/MGRS.2023.3274301","DOIUrl":"https://doi.org/10.1109/MGRS.2023.3274301","url":null,"abstract":"Onboard information fusion for multisatellites, which is based on spatial computing mode, can improve the satellites’ capability, such as the spatial–temporal coverage, detection accuracy, recognition confidence, position precision, and prediction precision for disaster monitoring, maritime surveillance, and other emergent or continuous persistent observing situations. First, we analyze the necessity of onboard information fusion. Next, the recent onboard processing developments are summarized and the existing problems are discussed. Furthermore, the key technologies and concepts of onboard information fusion are summarized in the fields of feature representation, association, feature-level fusion, spatial computing architecture, and other issues. Finally, the future developments of onboard information fusion are investigated and discussed.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":14.6,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62493141","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 : 2023-06-01DOI: 10.1109/mgrs.2023.3282458
{"title":"TechRxiv: Share Your Preprint Research With the World!","authors":"","doi":"10.1109/mgrs.2023.3282458","DOIUrl":"https://doi.org/10.1109/mgrs.2023.3282458","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136280941","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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