Comprehensive dataset from high resolution UAV land cover mapping of diverse natural environments in Serbia.
Bojana Ivošević, Nina Pajević, Sanja Brdar, Rana Waqar, Maryam Khan, João Valente
Author Information
Bojana Ivošević: BioSense Institute - Research and Development Institute for Information Technologies in Biosystems, University of Novi Sad, 21101, Novi Sad, Serbia. bojana.ivosevic@biosense.rs. ORCID
Nina Pajević: BioSense Institute - Research and Development Institute for Information Technologies in Biosystems, University of Novi Sad, 21101, Novi Sad, Serbia.
Sanja Brdar: BioSense Institute - Research and Development Institute for Information Technologies in Biosystems, University of Novi Sad, 21101, Novi Sad, Serbia.
Rana Waqar: BioSense Institute - Research and Development Institute for Information Technologies in Biosystems, University of Novi Sad, 21101, Novi Sad, Serbia.
Maryam Khan: Farmevo Technologies, 10016, New York, USA.
João Valente: Centre for Automation and Robotics (CAR), Spanish National Research Council (CSIC), 28006, Madrid, Spain.
This study highlights the vital role of high-resolution (HR), open-source land cover maps for food security, land use planning, and environmental protection. The scarcity of freely available HR datasets underscores the importance of multi-spectral HR aerial images. We used unmanned aerial vehicle (UAV) to capture images for a centimeter-level orthomosaics, facilitating advanced remote sensing and spatial analysis. Our method compares the efficacy and accuracy of object-based image analysis (OBIA) combined with random forest and convolutional neural networks (CNN) for land cover classification. We produced detailed land cover maps for 27 varied landscapes across Serbia, identifying nine unique land cover classes and assessing human impact on natural habitats. This resulted in a valuable dataset of HR multi-spectral orthomosaics across ecological zones, alongside land cover classification with extensive metrics and training data for each site. This dataset is a valuable resource for researchers working on habitats mapping and assessment for biodiversity monitoring studies on one side and researchers working on novel machine learning methods for land cover classification.
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