| dc.contributor | Universidade Estadual Paulista (Unesp) | |
| dc.contributor | Corumba Concessoes SA | |
| dc.date.accessioned | 2019-10-04T12:32:39Z | |
| dc.date.accessioned | 2022-12-19T18:02:58Z | |
| dc.date.available | 2019-10-04T12:32:39Z | |
| dc.date.available | 2022-12-19T18:02:58Z | |
| dc.date.created | 2019-10-04T12:32:39Z | |
| dc.date.issued | 2018-01-01 | |
| dc.identifier | Igarss 2018 - 2018 Ieee International Geoscience And Remote Sensing Symposium. New York: Ieee, p. 8941-8944, 2018. | |
| dc.identifier | 2153-6996 | |
| dc.identifier | http://hdl.handle.net/11449/185094 | |
| dc.identifier | WOS:000451039808130 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/5366147 | |
| dc.description.abstract | 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. | |
| dc.language | eng | |
| dc.publisher | Ieee | |
| dc.relation | Igarss 2018 - 2018 Ieee International Geoscience And Remote Sensing Symposium | |
| dc.rights | Acesso aberto | |
| dc.source | Web of Science | |
| dc.subject | Land-use classification | |
| dc.subject | Drones | |
| dc.subject | Convolutional Neural Networks | |
| dc.title | ENVIRONMENTAL MONITORING USING DRONE IMAGES AND CONVOLUTIONAL NEURAL NETWORKS | |
| dc.type | Actas de congresos | |
