Satellite object-based classification maps for placer occurrence from Ria de Vigo (Spain) pilot site

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Dataset information

Country of origin
Updated
2025.06.30 00:00
Created
2025.06.30
Available languages
English
Keywords
heavy mineral mapping, placer deposit exploration, coastal mineral exploration, Object-Based Image Analysis (OBIA), Ria De Vigo, WorldView-3, high-resolution remote sensing
Quality scoring

Dataset description

Identification     Full Title   Satellite object-based classification maps for placer occurrence from Ria de Vigo (Spain) pilot site Abstract   Satellite object-based classification maps for placer occurrence in the Ria de Vigo (Spain) pilot site provide high-resolution delineation of coastal sediment deposits rich in critical raw materials (CRMs). Based on WorldView-3 imagery and Object-Based Image Analysis (OBIA), three classification methods were implemented: Single-Level (SVM-based, 89.9–93.3% accuracy), Multi-Level (hierarchical segmentation, 94.0–94.6% accuracy), and Dynamic Classification (rule-based, 91.0–94.9% accuracy). Validation was performed using UAV orthomosaics, field control points, and spectral reference data. These vector maps enhance coastal mineral exploration and environmental monitoring by providing accurate, spatially contextualized mapping of heavy mineral accumulations. Keywords   Object-Based Image Analysis (OBIA), WorldView-3, heavy mineral mapping, placer deposit exploration, high-resolution remote sensing, coastal mineral exploration, machine learning, Ria De Vigo Pilot area     Ria De Vigo Associated resources      Language     English URL      Categories   Earth Observation, Mineral Exploration, Remote Sensing, Geology Temporal reference      Creation date (dd.mm.yyyy)   13.03.2024 Revision date (dd.mm.yyyy)   30.06.2025   Quality and validity   Classification accuracy up to 94.96% (validated with UAV and field samples) Representation type   Vector Format   ESRI Shapefile   Lineage   Derived from WorldView-3 imagery using Object-Based Image Analysis (OBIA) in eCognition 10.3. Processing includes segmentation, custom spectral indices (e.g., Ilmenite Absorption, Grossular++), and rule-based classification. Classification accuracy up to 94.96% (validated with UAV and field samples) Spatial resolution   Minimum mapping unit: 0.4 m² (reflecting <5 m placer occurrences) Positional accuracy   ≤2 meters Maintenance information   Not planned (static dataset supporting final classification output for the pilot study)   Coordinate system   EPSG: 32629, Constraints related to access and use      Use limitation   For research and non-commercial use in mineral exploration. Attribution required Access constraint   Open access under appropriate license (e.g., CC-BY 4.0) Public/Private   Public Responsible organisation   Hellenic Survey of Geology and Mineral Exploration (HSGME) Responsible Contact   Dr. Irene Zananiri izananiri@eagme.gr Responsible Party   HSGME Metadata on metadata      Contact   Dr. Irene Zananiri izananiri@eagme.gr Metadata language    English
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