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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLVIII-4/W9-2024
https://doi.org/10.5194/isprs-archives-XLVIII-4-W9-2024-229-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-archives-XLVIII-4-W9-2024-229-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
08 Mar 2024
| 08 Mar 2024
FOREST SEMANTIC SEGMENTATION BASED ON DEEP LEARNING USING SENTINEL-2 IMAGES
C. Hızal
Yildiz Technical University, Civil Engineering Faculty, 34220 Esenler Istanbul, Türkiye
G. Gülsu
Yildiz Technical University, Civil Engineering Faculty, 34220 Esenler Istanbul, Türkiye
H. Y. Akgün
Yildiz Technical University, Civil Engineering Faculty, 34220 Esenler Istanbul, Türkiye
B. Kulavuz
Yildiz Technical University, Civil Engineering Faculty, 34220 Esenler Istanbul, Türkiye
T. Bakırman
Yildiz Technical University, Civil Engineering Faculty, 34220 Esenler Istanbul, Türkiye
A. Aydın
Düzce University, Forestry Faculty, 81620 Merkez Düzce, Türkiye
B. Bayram
Yildiz Technical University, Civil Engineering Faculty, 34220 Esenler Istanbul, Türkiye
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Revised manuscript not accepted
Short summary
Short summary
Because the section of the Maritza River inside the Turkish border is short, there is not adequate warning time for Turkey to alert the population against flash floods. Although early warning systems offer the population time to evacuate before floods, improving such systems involves multiple components, each with a cost. That's why, four fuzzy models were developed satisfactorily predict the flow regime with high accuracy from transboundary flow data originating from three gauging stations.
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Nat. Hazards Earth Syst. Sci., 14, 1145–1154, https://doi.org/10.5194/nhess-14-1145-2014, https://doi.org/10.5194/nhess-14-1145-2014, 2014
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