Detection of road extraction from satellite images with deep learning method
Yükleniyor...
Tarih
2025
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Road extraction from satellite data is of great importance in various fields such as climate change, urban planning, forestry and sustainable development. In addition, fast and accurate road detection plays a critical role in disaster management and smart city applications, especially in emergency situations. In this context, U-net architecture provides an effective solution for tasks such as semantic segmentation and urban planning support. In this work, Edge U-net, a different adaptation of the U-net architecture, is used to map roads and streets and to detect changes over time. When the performance of the architecture is evaluated using Mean Intersection over Union (mIoU) and global accuracy metrics, superior results are obtained compared to other studies in the literature. In addition, the performance of the model was improved by applying transfer learning to the ImageNet dataset and various hyperparameter settings were performed. The results of the study show that path inferences are detected with 98.4% accuracy. These results show that Edge U-Net architecture and deep learning methods can be effectively used in road detection applications from satellite imagery.
Açıklama
Anahtar Kelimeler
Edge U-net, Road extractions, Satellite imagery, Segmentatio, Transfer learning
Kaynak
Cluster Computing
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
28
Sayı
1
Künye
Aslan, E. ve Özüpak, Y. (2025). Detection of road extraction from satellite images with deep learning method. Cluster Computing, 28(1), 1-10.
