Environmental monitoring using drone images and convolutional neural networks
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Recently, drone images have been used in a number of applications, mainly for pollution control and surveillance purposes. In this paper, we introduce the well-known Convolutional Neural Networks in the context of environmental monitoring using drone images, and we show their robustness in real-world images obtained from uncontrolled scenarios. We consider a transfer learning-based approach and compare two neural models, i.e., VGG16 and VGG19, to distinguish four classes: water, deforesting area, forest, and buildings. The results are analyzed by experts in the field and considered pretty much reasonable.
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Convolutional Neural Networks, Drones, Land-use classification
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Inglês
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International Geoscience and Remote Sensing Symposium (IGARSS), v. 2018-July, p. 8941-8944.
