Multimedia Tools and Applications, vol.1, pp.1-31, 2024 (SCI-Expanded)
Buildings that are constructed without the necessary permits and
building inspections affect many areas, including safety, health, the
environment, social order, and the economy. For this reason, it is
essential to determine the number of buildings and their boundaries.
Determining the boundaries of a building based solely on its location in
the world is a challenging task. In the context of this research, a new
approach, BBD, is proposed to detect architectural objects from
large-scale satellite imagery, which is an application of remote
sensing, together with the geolocations of buildings and their
boundaries on the Earth. In the proposed BBD method, open-source
GeoServer and TileCache software process huge volumes of satellite
imagery that cannot be analyzed with classical data processing
techniques using deep learning models. In the proposed BBD method,
YOLOv5, DETR, and YOLO-NAS models were used for building detection. SAM
was used for the segmentation process in the BBD technique. In addition,
the performance of the RefineNet model was investigated, as it performs
direct building segmentation, unlike the aforementioned methods. The
YOLOV5, DETR and YOLO-NAS models in BBD for building detection obtained
an f1 score of 0.744, 0.615, and 0.869 respectively on the images
generated by the classic TileCache. However, the RefineNet model, which
uses the data generated by the classic TileCache, achieved an f1 score
of 0.826 in the building segmentation process. Since the images produced
by the classic TileCache are divided into too many parts, the buildings
cannot be found as a whole in the images. To overcome these problems, a
fine-tuning based optimization was performed. Thanks to the proposed
fine-tuning, the modified YOLOv5, DETR, YOLO-NAS, and RefineNet models
achieved F1 scores of 0.883, 0.772, 0.975 and 0.932, respectively. In
the proposed BBD approach, the modified YOLO-NAS approach was the
approach that detected the highest number of objects with an F1 score of
0.975. The YOLO-NAS-SAM approach detected the boundaries of the
buildings with high performance by obtaining an IoU value of 0.912.