Advancements in High-Resolution Land Use Mapping

Land use and land cover (LULC) mapping is essential for land-based climate change adaptation and mitigation strategies. This study presents the development of 10-meter high-resolution (HR) land use maps within the RethinkAction H2020 project, aimed at enhancing spatial planning for climate mitigation and adaptation. The methodology integrates multi-source remote sensing data, machine learning classification techniques, and auxiliary datasets to generate accurate and transferable land use classifications across six European bioclimatic regions. The study employs Sentinel-2 and Landsat-8 imagery, using supervised classification with Random Forest (RF) and Geographic Object-Based Image Analysis (GEOBIA) to enhance accuracy and minimize spectral confusion. This approach resulted in the creation of twelve HR land use maps at two classification levels, covering six case study (CS) areas. A key contribution of this research is the generation of suitability maps, which assess the potential for implementing land-based mitigation and adaptation solutions (LAMS) such as reforestation, water harvesting, and photovoltaic energy development. This study highlights the importance of integrating remote sensing, machine learning, and spatial analysis to support evidence-based decision-making in land use planning, offering a scalable and replicable methodology for detailed LULC classification.