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Automatic Detection and Mapping of Espeletia Plants from UAV Imagery

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Automatic Detection and Mapping of Espeletia Plants from UAV Imagery. / Rodriguez, Jorge; Zhang, Ce; Lizarazo, Ivan et al.
2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE, 2021. p. 2831-2834 (2021 IEEE International Geoscience and Remote Sensing Symposium).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Rodriguez, J, Zhang, C, Lizarazo, I & Prieto, F 2021, Automatic Detection and Mapping of Espeletia Plants from UAV Imagery. in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. 2021 IEEE International Geoscience and Remote Sensing Symposium, IEEE, pp. 2831-2834. https://doi.org/10.1109/IGARSS47720.2021.9554263

APA

Rodriguez, J., Zhang, C., Lizarazo, I., & Prieto, F. (2021). Automatic Detection and Mapping of Espeletia Plants from UAV Imagery. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 2831-2834). (2021 IEEE International Geoscience and Remote Sensing Symposium). IEEE. https://doi.org/10.1109/IGARSS47720.2021.9554263

Vancouver

Rodriguez J, Zhang C, Lizarazo I, Prieto F. Automatic Detection and Mapping of Espeletia Plants from UAV Imagery. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE. 2021. p. 2831-2834. (2021 IEEE International Geoscience and Remote Sensing Symposium). Epub 2021 Jul 11. doi: 10.1109/IGARSS47720.2021.9554263

Author

Rodriguez, Jorge ; Zhang, Ce ; Lizarazo, Ivan et al. / Automatic Detection and Mapping of Espeletia Plants from UAV Imagery. 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE, 2021. pp. 2831-2834 (2021 IEEE International Geoscience and Remote Sensing Symposium).

Bibtex

@inproceedings{4b8174f25cee477998b93fb1c0a02a8f,
title = "Automatic Detection and Mapping of Espeletia Plants from UAV Imagery",
abstract = "This paper proposes an automatic method for detection and mapping of Espeletia plants from aerial images acquired by UAV drone. The proposed approach integrated a computer vision for automatic extraction of training zones and tested on three well-established machine learning algorithms to detect regions belonging to Espeletia plants. The main components of the method are: (i) data capture and preprocessing; (ii) automatic extraction of training zones; and (iii) classification procedure using machine learning algorithms. Experimental results show that the method can achieve accurate detection and mapping of Espeletia plants, with up to 98.3% accuracy.",
keywords = "Paramos, Espeletia, UAV, Machine Learning, Computer Vision",
author = "Jorge Rodriguez and Ce Zhang and Ivan Lizarazo and Flavio Prieto",
year = "2021",
month = oct,
day = "12",
doi = "10.1109/IGARSS47720.2021.9554263",
language = "English",
isbn = "9781665447621",
series = "2021 IEEE International Geoscience and Remote Sensing Symposium",
publisher = "IEEE",
pages = "2831--2834",
booktitle = "2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS",

}

RIS

TY - GEN

T1 - Automatic Detection and Mapping of Espeletia Plants from UAV Imagery

AU - Rodriguez, Jorge

AU - Zhang, Ce

AU - Lizarazo, Ivan

AU - Prieto, Flavio

PY - 2021/10/12

Y1 - 2021/10/12

N2 - This paper proposes an automatic method for detection and mapping of Espeletia plants from aerial images acquired by UAV drone. The proposed approach integrated a computer vision for automatic extraction of training zones and tested on three well-established machine learning algorithms to detect regions belonging to Espeletia plants. The main components of the method are: (i) data capture and preprocessing; (ii) automatic extraction of training zones; and (iii) classification procedure using machine learning algorithms. Experimental results show that the method can achieve accurate detection and mapping of Espeletia plants, with up to 98.3% accuracy.

AB - This paper proposes an automatic method for detection and mapping of Espeletia plants from aerial images acquired by UAV drone. The proposed approach integrated a computer vision for automatic extraction of training zones and tested on three well-established machine learning algorithms to detect regions belonging to Espeletia plants. The main components of the method are: (i) data capture and preprocessing; (ii) automatic extraction of training zones; and (iii) classification procedure using machine learning algorithms. Experimental results show that the method can achieve accurate detection and mapping of Espeletia plants, with up to 98.3% accuracy.

KW - Paramos

KW - Espeletia

KW - UAV

KW - Machine Learning

KW - Computer Vision

U2 - 10.1109/IGARSS47720.2021.9554263

DO - 10.1109/IGARSS47720.2021.9554263

M3 - Conference contribution/Paper

SN - 9781665447621

T3 - 2021 IEEE International Geoscience and Remote Sensing Symposium

SP - 2831

EP - 2834

BT - 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS

PB - IEEE

ER -