Arikan, D.Yildiz, F.2025-02-102025-02-1020232687-4997https://doi.org/10.51489/tuzal.1300939https://hdl.handle.net/20.500.13091/9871The increase in population has led to unplanned urbanization in urban areas, becoming a global issue. The identification and detection of these areas are of great importance for urban management and redevelopment planning. However, these processes can be costly and time-consuming when conducted on-site. Automatic detection and characterization of unplanned buildings in urban and rural areas using remote sensing imagery is a challenging task. Recently, with the advancements in deep learning methods, the detection of complex buildings has become possible. In this study, the building extraction process of a region from the Etimesgut district of Ankara was performed using the U-Net deep learning architecture. The Inria Aerial Image Labeling dataset, a publicly available dataset, was used for the process. Different numbers of images (500, 1000, 2500, 5000) were selected for the training process. The best learning outcome was tested with Göktürk-1 satellite imagery with a spatial resolution of 0.5 m. According to the results, the U-Net model achieved a Jaccard coefficient of 0.862 and a Dice similarity coefficient of 0.813 for building segmentation.The effectiveness and potential of deep learning methods were demonstrated using the U-Net model with the available dataset. This study showcased the efficiency and potential of deep learning methods in the detection and mapping of buildings in urban areas. © Author(s) 2023.trinfo:eu-repo/semantics/openAccessBuilding DetectionDeep LearningGokturk-1 Satellite ImageU-Net ModelArticle10.51489/tuzal.13009392-s2.0-85215411017