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- W4200044643 abstract "Forest areas are profoundly important to the planet, since they offer considerable advantages. The mapping and estimation of burned areas covered with trees are critical during decision making processes. In such cases, remote sensing can be of great help. This paper presents a method to estimate burned areas based on the Sentinel-2 imagery using a convolutional neural network (CNN) algorithm. The framework touches change detection using pre- and post-fire datasets. The proposed framework utilizes a multi-scale convolution block to extract deep features. We investigate the performance of the proposed method via visual and numerical analyses. The case study for this research is Golestan Forest, which is located in the north of Iran. The results of the burned area detection process show that the proposed method produces a performance accuracy rate of more than 97% in terms of overall accuracy, with a Kappa score greater than 0.933." @default.
- W4200044643 created "2021-12-31" @default.
- W4200044643 creator A5018156889 @default.
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- W4200044643 date "2021-11-01" @default.
- W4200044643 modified "2023-10-13" @default.
- W4200044643 title "Forest Burned Area Mapping Using Bi-Temporal Sentinel-2 Imagery Based on a Convolutional Neural Network: Case Study in Golestan Forest" @default.
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- W4200044643 doi "https://doi.org/10.3390/ecsa-8-11291" @default.
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