Land Cover Segmentation using DeepLabV3 and ResNet50
Göster/ Aç
Erişim
info:eu-repo/semantics/openAccessTarih
2023Yazar
Zamanoglu, Esref SamilErbay, Sergen
Cengil, Emine
Kosunalp, Selahattin
Tumen, Vedat
Demir, Kubilay
Üst veri
Tüm öğe kaydını gösterKünye
Esref Samil Zamanoglu, Sergen Erbay, Emine Cengil, & Demir, K. (2023, November 23). Land Cover Segmentation using DeepLabV3 and ResNet50. Retrieved March 4, 2024, from ResearchGate website: https://www.researchgate.net/publication/377133018_Land_Cover_Segmentation_using_DeepLabV3_and_ResNet50 Özet
Land cover segmentation has a great importance in various fields, including remote sensing, environmental monitoring, urban planning, agriculture, and natural resource management. It involves a division process of a landscape or region into different classes or categories with respect to the type of land cover in each place. With the recent developments in remote sensing area, high-resolution satellite images can be simply acquired. For an efficient land cover segmentation, in this study, a hybrid approach using deep learning architectures DeepLabV3 and ResNet34 is proposed. The proposed method has been trained and tested using the LandCover AI dataset. As a result, 88.2% F1-score value was obtained with the proposed hybrid approach. © 2023 IEEE.