Comprehensive dataset from high resolution UAV land cover mapping of diverse natural environments in Serbia
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View abstract on PubMed
Summary
This summary is machine-generated.High-resolution, open-source land cover maps are crucial for environmental management. This study developed detailed maps using drone imagery and machine learning, creating a valuable resource for biodiversity and land use research.
Area Of Science
- Remote Sensing
- Geospatial Analysis
- Environmental Science
Background
- High-resolution (HR) land cover maps are essential for food security, land use planning, and environmental protection.
- There is a scarcity of freely available HR land cover datasets, highlighting the need for multi-spectral aerial imagery.
Purpose Of The Study
- To compare the accuracy of object-based image analysis (OBIA) with Random Forest and Convolutional Neural Networks (CNN) for land cover classification.
- To produce detailed land cover maps and a valuable dataset of HR multi-spectral orthomosaics for 27 diverse Serbian landscapes.
Main Methods
- Utilized unmanned aerial vehicle (UAV) technology to capture centimeter-level orthomosaic images.
- Employed object-based image analysis (OBIA) integrated with Random Forest and CNN algorithms for land cover classification.
- Identified nine distinct land cover classes and assessed human impact on natural habitats across various ecological zones.
Main Results
- Generated detailed land cover maps for 27 varied landscapes in Serbia.
- Created a valuable dataset comprising HR multi-spectral orthomosaics, land cover classifications, and associated metrics and training data.
- Assessed the human impact on natural habitats within the study areas.
Conclusions
- The developed dataset serves as a crucial resource for habitat mapping, biodiversity monitoring, and the advancement of machine learning techniques in land cover classification.
- The study demonstrates the efficacy of combining UAV-based remote sensing with advanced machine learning for creating accurate and detailed land cover maps.
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