Publication: HexaLCSeg: <i>A historical benchmark dataset from Hexagon satellite images for land cover segmentation</i>
dc.contributor.coauthor | Sertel, Elif | |
dc.contributor.department | Department of History | |
dc.contributor.department | VPRI (Vice Presidency for Research and Innovation) | |
dc.contributor.kuauthor | Kabadayı, Mustafa Erdem | |
dc.contributor.kuauthor | Şengül, Gafur Semi | |
dc.contributor.kuauthor | Tümer, İlay Nur | |
dc.contributor.schoolcollegeinstitute | Administrative Unit | |
dc.contributor.schoolcollegeinstitute | College of Social Sciences and Humanities | |
dc.date.accessioned | 2025-03-06T20:58:31Z | |
dc.date.issued | 2024 | |
dc.description.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 https://github.com/RSandAI/HexaLCSeg and https://doi.org/10.5281/zenodo.11005344. | |
dc.description.indexedby | WOS | |
dc.description.indexedby | Scopus | |
dc.description.publisherscope | International | |
dc.description.sponsoredbyTubitakEu | N/A | |
dc.description.sponsorship | This work was supported by the European Research Council (ERC) Proof of Concept Project "GeoAI_LULC_Seg: A GeoAI-based Land Use Land Cover Segmentation Process to Analyse and Predict Rural Depopulation, Agricultural Land Abandonment, and Deforestation in Bulgaria and Turkey, 1940-2040" under the European Union's Horizon 2020 Research and Innovation Program, Grant 101100837. | |
dc.identifier.doi | 10.1109/MGRS.2024.3394248 | |
dc.identifier.eissn | 2168-6831 | |
dc.identifier.grantno | European Research Council (ERC) Proof of Concept Project "GeoAI_LULC_Seg: A GeoAI-based Land Use Land Cover Segmentation Process to Analyse and Predict Rural Depopulation, Agricultural Land Abandonment, and Deforestation in Bulgaria and Turkey, 1940-2040" [101100837];European Research Council (ERC) [101100837] Funding Source: European Research Council (ERC) | |
dc.identifier.issn | 2473-2397 | |
dc.identifier.issue | 3 | |
dc.identifier.quartile | Q1 | |
dc.identifier.scopus | 2-s2.0-85204876627 | |
dc.identifier.uri | https://doi.org/10.1109/MGRS.2024.3394248 | |
dc.identifier.uri | https://hdl.handle.net/20.500.14288/27484 | |
dc.identifier.volume | 12 | |
dc.identifier.wos | 1322577000015 | |
dc.keywords | Image segmentation | |
dc.keywords | Biological system modeling | |
dc.keywords | European space agency | |
dc.keywords | Transfer learning | |
dc.keywords | Land surface | |
dc.keywords | Benchmark testing | |
dc.keywords | Satellite images | |
dc.keywords | Data models | |
dc.keywords | Land use planning | |
dc.keywords | Human activity recognition | |
dc.keywords | Reconnaissance | |
dc.keywords | Geospatial analysis | |
dc.keywords | Urban areas | |
dc.language.iso | eng | |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
dc.relation.ispartof | IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE | |
dc.subject | Geochemistry | |
dc.subject | Geophysics | |
dc.title | HexaLCSeg: <i>A historical benchmark dataset from Hexagon satellite images for land cover segmentation</i> | |
dc.type | Journal Article | |
dspace.entity.type | Publication | |
local.contributor.kuauthor | Kabadayı, Mustafa Erdem | |
local.contributor.kuauthor | Şengül, Gafur Semi | |
local.contributor.kuauthor | Tümer, İlay Nur | |
local.publication.orgunit1 | College of Social Sciences and Humanities | |
local.publication.orgunit1 | Administrative Unit | |
local.publication.orgunit2 | Department of History | |
local.publication.orgunit2 | VPRI (Vice Presidency for Research and Innovation) | |
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