TDSO: a new hybrid method for temporal detection of spatial objects from large-scale satellite images
All Earth, cilt.38, sa.1, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 38 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1080/27669645.2026.2718549
- Dergi Adı: All Earth
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Anahtar Kelimeler: Building and road extraction, change detection, deep learning, Mask R-CNN, multi-temporal satellite imagery, YOLO
- Erciyes Üniversitesi Adresli: Evet
Özet
Accurate identification of buildings and roads in large-scale satellite imagery is critical for disaster management, urban transformation, and city planning. This study proposes a TDSO (Temporal Detection of Spatial Objects) approach that considers the temporal changes of spatial objects using different-temporal satellite images from 2020 and 2024. Satellite images were obtained from the server infrastructure of Kayseri Metropolitan Municipality; to make the high-volume data processable, they were published as WMS services via the GeoServer platform and decomposed into sub-images with dimensions of 256 × 256 pixels, preserving geographic reference information, using the TileCache system. Mask R-CNN and YOLO architectures were used to detect building and road objects on the resulting tile images, and object detection performance was evaluated with object-based metrics under different image quality conditions (PNG (0.99), JPEG (0.99), and JPEG (0.75)). Experimental findings show that Mask R-CNN exhibits more balanced and stable performance than YOLO across all scenarios, notably achieving the highest performance under JPEG (0.99) conditions, with F1-Scores of 83.63% and 88.51% for building and road detection, respectively. Furthermore, it has been demonstrated that newly constructed and demolished structures can be reliably detected by integrating asynchronous images; the proposed approach provides a robust decision-support infrastructure for post-disaster damage assessment, urban transformation, and sustainable city planning applications.