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{"title":"The Fourth China International Synthetic Aperture Radar Symposium [Conference Reports]","authors":"Hui Wang, Yongqi Wang, Shaohui Mei, Zhaokai Pan, Hanwen Yu, Qiang Zhao","doi":"10.1109/mgrs.2024.3371933","DOIUrl":"https://doi.org/10.1109/mgrs.2024.3371933","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":14.6,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313817","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 : 2024-09-23DOI: 10.1109/mgrs.2024.3394248
Elif Sertel, Mustafa Erdem Kabadayi, Gafur Semi Sengul, Ilay Nur Tumer
Historical land cover (LC) maps are significant geospatial data sources used to understand past land characteristics and accurately determine the long-term land changes that provide valuable insights into the interactions between human activities and the environment over time. This article introduces a novel open LC benchmark dataset generated from very high spatial resolution historical Hexagon (KH-9) reconnaissance satellite images to be used in deep learning (DL)-based image segmentation tasks. This new benchmark dataset, which includes very high-resolution (VHR) mono-band Hexagon images of several Turkish and Bulgarian territories from the 1970s and 1980s, covers a large geographic area. Our dataset includes eight LC classes inspired by the European Space Agency (ESA) WorldCover project except for the tree class, which we divided into subclasses, namely agricultural fruit trees and other trees. We implemented widely used U-Net++ and DeepLabv3+ segmentation architectures with appropriate hyperparameters and backbone structures to demonstrate the versatility and impact of our HexaLCSeg dataset and to compare the performance of these models for accurate and fast LC mapping of past terrain conditions. We achieved the highest accuracy using U-Net++ with an SE-ResNeXt50 backbone and obtained an F1-score of 0.8804. The findings of this study can be applied to different geographical regions with similar Hexagon images, providing valuable contributions to the field of remote sensing and LC mapping. Our dataset, related source codes, and pretrained models are available at https://github.com/RSandAI/HexaLCSeg and https://doi.org/10.5281/zenodo.11005344 .
{"title":"HexaLCSeg: A historical benchmark dataset from Hexagon satellite images for land cover segmentation [Software and Data Sets]","authors":"Elif Sertel, Mustafa Erdem Kabadayi, Gafur Semi Sengul, Ilay Nur Tumer","doi":"10.1109/mgrs.2024.3394248","DOIUrl":"https://doi.org/10.1109/mgrs.2024.3394248","url":null,"abstract":"Historical land cover (LC) maps are significant geospatial data sources used to understand past land characteristics and accurately determine the long-term land changes that provide valuable insights into the interactions between human activities and the environment over time. This article introduces a novel open LC benchmark dataset generated from very high spatial resolution historical Hexagon (KH-9) reconnaissance satellite images to be used in deep learning (DL)-based image segmentation tasks. This new benchmark dataset, which includes very high-resolution (VHR) mono-band Hexagon images of several Turkish and Bulgarian territories from the 1970s and 1980s, covers a large geographic area. Our dataset includes eight LC classes inspired by the European Space Agency (ESA) WorldCover project except for the tree class, which we divided into subclasses, namely agricultural fruit trees and other trees. We implemented widely used U-Net++ and DeepLabv3+ segmentation architectures with appropriate hyperparameters and backbone structures to demonstrate the versatility and impact of our HexaLCSeg dataset and to compare the performance of these models for accurate and fast LC mapping of past terrain conditions. We achieved the highest accuracy using U-Net++ with an SE-ResNeXt50 backbone and obtained an F1-score of 0.8804. The findings of this study can be applied to different geographical regions with similar Hexagon images, providing valuable contributions to the field of remote sensing and LC mapping. Our dataset, related source codes, 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/RSandAI/HexaLCSeg</uri> and <uri xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">https://doi.org/10.5281/zenodo.11005344</uri>\u0000.","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":14.6,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313818","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 : 2024-09-23DOI: 10.1109/mgrs.2024.3455659
{"title":"IEEE Tech RXIV: Share Your Preprint Research with the world!","authors":"","doi":"10.1109/mgrs.2024.3455659","DOIUrl":"https://doi.org/10.1109/mgrs.2024.3455659","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":14.6,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313499","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 : 2024-09-23DOI: 10.1109/mgrs.2024.3437174
Jaya Sreevalsan-Nair, Amruth Kiran, Avik Bhattacharya, B. S. Daya Sagar, Gurudatta K. N., Ujjwal Verma, Saroj K. Meher, Lanka Karthikeyan
{"title":"InGARSS 2023 in Bangalore: Striking a Balance [Conference Reports]","authors":"Jaya Sreevalsan-Nair, Amruth Kiran, Avik Bhattacharya, B. S. Daya Sagar, Gurudatta K. N., Ujjwal Verma, Saroj K. Meher, Lanka Karthikeyan","doi":"10.1109/mgrs.2024.3437174","DOIUrl":"https://doi.org/10.1109/mgrs.2024.3437174","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":14.6,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313497","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 : 2024-09-23DOI: 10.1109/mgrs.2024.3441936
Margot Flemming, Paula Castro Brandão Vaz Dos Santos
{"title":"A Glimpse at the Women Mentoring Women Program From a Mentor/Mentee Perspective [Conference Reports]","authors":"Margot Flemming, Paula Castro Brandão Vaz Dos Santos","doi":"10.1109/mgrs.2024.3441936","DOIUrl":"https://doi.org/10.1109/mgrs.2024.3441936","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":14.6,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313502","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 : 2024-09-23DOI: 10.1109/mgrs.2024.3442820
Alberto Moreira, Francesca Bovolo, Antonio Plaza, Jaya Sreevalsan-Nair
{"title":"44th IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2024)—Athens, Greece, 7–12 July 2024: Impressions of the first days [Conference Reports]","authors":"Alberto Moreira, Francesca Bovolo, Antonio Plaza, Jaya Sreevalsan-Nair","doi":"10.1109/mgrs.2024.3442820","DOIUrl":"https://doi.org/10.1109/mgrs.2024.3442820","url":null,"abstract":"","PeriodicalId":48660,"journal":{"name":"IEEE Geoscience and Remote Sensing Magazine","volume":null,"pages":null},"PeriodicalIF":14.6,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313501","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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