Detecting and monitoring small-scale buildings is a critical challenge in various sectors and can be crucial for various important tasks. Buildings considered small are those with a footprint smaller than 10 × 10 meters, a definition justified by the resolution of Sentinel-2 images, which is 10 meters at best. Examples of tasks for which small building detection is essential include assessment and monitoring of damage caused by natural disasters or war, identification of illegal settlements, detecting new buildings in key military areas, and identification of structures in protected areas. Regrettably, widely available satellite imagery, such as Sentinel-2, provides very limited spatial resolution, making it impossible to efficiently detect small structures. Despite its importance, this issue remains unexplored, with very limited research directly concerning building segmentation and detecting small structures. Traditional segmentation and classification methods exhibit severe limitations, particularly due to small buildings frequently being positioned at pixel boundaries, essentially occupying an insignificant fraction of the original pixel. Additionally, in the context of buildings in complex environments (e.g., surrounded by vegetation or shadows), popular methods such as U-Net with ResNet often fail to deliver reliable results. In this study, we propose a novel approach to this issue by integrating a state-of-the-art object detection model with a deep-learning-based super-resolution network.