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- W3115669077 abstract "Illegal tree cutting is a big problem for developing countries. In Ukraine by official data, by 2019 118000 square meters of trees were cut illegally. The total losses this year due to illegal logging were 814 million of hryvna. This problem is still actual, because in Ukraine there is no tool for independent monitoring of forestry at the state level. It can be solved by the use of machine learning and deep learning approaches and satellite data, that can produce in the operational mode actual data about forest state in the country. This research demonstrate the result of deforestation detection with use of state-of-the-art approaches: multi-layer perceptron, long short term memory recurrent neural network. These neural networks were trained using various satellite data: 10 meters spatial resolution Sentinel-1 and Sentinel-2 images. Results obtained with the use of the different methods and data collections show that the best accuracy can be obtained by the combination of SAR and optical data using time series of satellite and recurrent neural networks. However, performance of the state-of-the-art deep learning approaches is insufficient for fully automated deforestation detection system that works on the country level. Thus, in this paper authors propose use them in combination with the post processing steps based on the polygons area and vegetation characteristics." @default.
- W3115669077 created "2021-01-05" @default.
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- W3115669077 date "2020-09-17" @default.
- W3115669077 modified "2023-10-16" @default.
- W3115669077 title "Remote Sensing Approaches for Deforestation Identification in Ukraine" @default.
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- W3115669077 doi "https://doi.org/10.1109/idaacs-sws50031.2020.9297054" @default.
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