Thomazella, R. [UNESP]Castanho, J. E. [UNESP]Dotto, F. R. L. [UNESP]Rodrigues Junior, O. P.Rosa, G. H. [UNESP]Marana, A. N. [UNESP]Papa, J. P. [UNESP]IEEE2019-10-042019-10-042018-01-01Igarss 2018 - 2018 Ieee International Geoscience And Remote Sensing Symposium. New York: Ieee, p. 8941-8944, 2018.2153-6996http://hdl.handle.net/11449/185094Recently, 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.8941-8944engLand-use classificationDronesConvolutional Neural NetworksTrabalho apresentado em eventoWOS:000451039808130Acesso aberto