Automatic Land Use Classification in High-Resolution RGB Images

The popularization of unmanned aerial vehicles (UAVs) is transforming contemporary Geotechnology related activities, offering accessibility, accuracy, and efficiency. In particular, landcover analysis and cartography with UAV-borne imagery enables precise identification and measurements for surveying purposes. While multispectral cameras facilitate detailed mapping, affordability drives the use of common RGB drone cameras. This, in turn, raises the requirement of robust and versatile analysis techniques for extracting meaningful information from RGB images. In this study, we assess different algorithms for mapping urban areas using high-resolution RGB aerial images from UAVs. We explore the use of different machine learning methods in landcover identification and classification, leading to a workflow for producing thematic maps, showcasing the potential of machine learning in urban mapping with UAV-acquired RGB imagery.