{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["14(1)"],"submitter":["Scala P"],"pubmed_abstract":["Coastal zones are dynamic interfaces shaped by the interplay of Land Cover (LC) and Land Use (LU), influenced by both natural processes and anthropogenic activities. Grasping the historical shifts in land is essential for safeguarding coastal benefits such as defense mechanisms, biodiversity conservation, and recreational spaces, alongside enhancing their management. LC and LU products offer a valuable option for monitoring urban development, vegetation coverage, and dry-beach areas. Herein, we present the first study of the spatiotemporal evolution of LC specifically tailored for coastal zones, using the coast of Sicily as an illustration. We used classified satellite imagery from Landsat and Sentinel missions as input for a semantic segmentation model based on deep neural networks. We tr"],"journal":["Scientific reports"],"pagination":["22222"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11436747"],"repository":["biostudies-literature"],"pubmed_title":["Mapping decadal land cover dynamics in Sicily's coastal regions."],"pmcid":["PMC11436747"],"pubmed_authors":["Alvarez-Cuesta M","Manno G","Toimil A","Scala P","Ciraolo G"],"additional_accession":[]},"is_claimable":false,"name":"Mapping decadal land cover dynamics in Sicily's coastal regions.","description":"Coastal zones are dynamic interfaces shaped by the interplay of Land Cover (LC) and Land Use (LU), influenced by both natural processes and anthropogenic activities. Grasping the historical shifts in land is essential for safeguarding coastal benefits such as defense mechanisms, biodiversity conservation, and recreational spaces, alongside enhancing their management. LC and LU products offer a valuable option for monitoring urban development, vegetation coverage, and dry-beach areas. Herein, we present the first study of the spatiotemporal evolution of LC specifically tailored for coastal zones, using the coast of Sicily as an illustration. We used classified satellite imagery from Landsat and Sentinel missions as input for a semantic segmentation model based on deep neural networks. We tr","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Sep","modification":"2025-04-18T13:05:00.234Z","creation":"2025-04-06T22:35:53.154Z"},"accession":"S-EPMC11436747","cross_references":{"pubmed":["39333308"],"doi":["10.1038/s41598-024-73085-5"]}}