The analysis of aerial and satellite images for building footprint detection is one of the major challenges in photogrammetry and remote sensing. This information is useful for various applications, such as urban planning, disaster monitoring, and 3D city modeling. However, it has become a significant challenge due to the diverse characteristics of buildings, such as shape, size, and shadow interference. This study investigated the simultaneous use of aerial and satellite images to improve the accuracy of deep learning models in building footprint detection. For this purpose, aerial images with a spatial resolution of 30 cm and Sentinel-2 satellite imagery were employed. Several satellite-derived spectral indices were extracted from the Sentinel-2 image. Then, U-Net models combined with ResNet-18 and ResNet-34 were trained on these data. The results showed that the combination of the U-Net model with ResNet-34, trained on a dataset obtained by integrating aerial images and satellite indices, referred to as RGB–Sentinel–ResNet34, achieved the best performance among the evaluated models. This model attained an accuracy of 96.99%, an F1-score of 90.57%, and an Intersection over Union of 73.86%. Compared to other models, RGB–Sentinel–ResNet34 showed a significant improvement in accuracy and generalization capability. The findings indicated that the simultaneous use of aerial and satellite data can substantially enhance the accuracy of building footprint detection.

Automatic extraction of building footprints from aerial and space imageries has been found ever increasing importance in urban planning, disaster management, and environmental monitoring. However, achieving accurate building footprint extraction poses significant challenges due to diverse building characteristics and their similarities to their background elements. While conventional methods in building footprint extraction have mainly relied on image processing techniques, recent advancements in deep learning, particularly semantic segmentation algorithms like U-Net, have shown promise in addressing these challenges through machine learning. This study explores different depths of the U-Net model for building footprint extraction, aiming to identify the optimum architecture while investigating the semantic uncertainty of the building footprint extraction. Utilizing aerial imagery from cities including Berlin, Paris, Chicago, and Zurich, collected from Google Maps and OpenStreetMap (OSM) data, five U-Net models have been compared with varying depths. In addition, the impact of dataset sizes and learning rates on model performance has been investigated. Results confirmed that the U-Net-32-1024 model achieves the highest intersection over union (IoU), Accuracy, and F1-score. Moreover, increasing the training dataset size leads to significant improvements in model performance with IoU, Accuracy and F1-score reaching their values of 73.73%, 88.65% and 88.53%. However, challenges remain in accurately delineating buildings in dense urban areas. Nonetheless, our findings demonstrated the effectiveness of U-Net models in building footprint extraction.

Accurate building footprint extraction from high-resolution aerial imagery is essential for urban planning, cadastral mapping, disaster management, and digital twin development. Existing deep learning approaches often improve individual architectural components in isolation or rely on increasingly complex designs, at the expense of efficiency and generalization. This study proposes AR2Net, an encoder–decoder architecture integrating residual learning, recurrent convolutional refinement, and channel–spatial attention within a unified framework to improve optimization stability, spatial feature refinement, and building-relevant feature selectivity. A two-stage training strategy is introduced on a large-scale international dataset with OpenStreetMap-derived annotations before fine-tuning on a high-quality regional dataset from Iranian cities. AR2Net is evaluated through ablation experiments and comparisons with five widely used segmentation models (U-Net, U-Net++, DeepLabV3+, HRNetV2, and SegFormer), achieving the best overall performance (IoU = 88.35%, F1-score = 93.31%). A geometric accuracy assessment against manually digitized ground truth across 486,000 buildings yielded a mean positional error of 0.92 m. The extracted footprints were further used to generate LoD1 CityGML building models, illustrating a practical pathway toward 3D cadastral map production. These results demonstrate that AR2Net provides an accurate, geometrically reliable, and scalable solution for automated building footprint extraction.