Early Detection of Deforestation through Satellite Land Geospatial Images based on CNN Architecture

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Nor Kumalasari Caecar Pratiwi
Yunendah Nur Fu'adah
Edwar Edwar

Abstract

This study has developed a CNN model applied to classify the eight classes of land cover through satellite images. Early detection of deforestation has become one of the study’s objectives. Deforestation is the process of reducing natural forests for logging or converting forest land to non-forest land. The study considered two training models, a simple four hidden layer CNN compare with Alexnet architecture. The training variables such as input size, epoch, batch size, and learning rate were also investigated in this research. The Alexnet architecture produces validation accuracy over 100 epochs of 90.23% with a loss of 0.56. The best performance of the validation process with four hidden layers CNN got 95.2% accuracy and a loss of 0.17. This performance is achieved when the four hidden layer model is designed with an input size of 64 × 64, epoch 100, batch size 32, and learning rate of 0.001. It is expected that this land cover identification system can assist relevant authorities in the early detection of deforestation.

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How to Cite
[1]
N. Pratiwi, Y. N. Fu’adah, and E. Edwar, “Early Detection of Deforestation through Satellite Land Geospatial Images based on CNN Architecture”, INFOTEL, vol. 13, no. 2, May 2021.
Section
Informatics