Automatic Detection and Mapping of Espeletia Plants from UAV Imagery

Jorge Rodriguez; Ce Zhang; Ivan Lizarazo; Flavio Prieto

doi:10.1109/IGARSS47720.2021.9554263

Automatic Detection and Mapping of Espeletia Plants from UAV Imagery

Jorge Rodriguez, Ce Zhang, Ivan Lizarazo, Flavio Prieto

School of Geographical Sciences

Research output: Contribution to conference › Conference Paper › peer-review

1 Citation (Scopus)

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.

Original language	English
Pages	2831-2834
Number of pages	4
DOIs	https://doi.org/10.1109/IGARSS47720.2021.9554263
Publication status	Published - 2021
Event	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium Duration: 12 Jul 2021 → 16 Jul 2021

Conference

Conference	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Country/Territory	Belgium
City	Brussels
Period	12/07/21 → 16/07/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE

Keywords

Computer vision
Espeletia
Machine learning
Páramos
UAV

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@conference{2bb0c16bdceb4703ae814c138c8c0360,

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 = "Computer vision, Espeletia, Machine learning, P{\'a}ramos, UAV",

author = "Jorge Rodriguez and Ce Zhang and Ivan Lizarazo and Flavio Prieto",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 ; Conference date: 12-07-2021 Through 16-07-2021",

year = "2021",

doi = "10.1109/IGARSS47720.2021.9554263",

language = "English",

pages = "2831--2834",

}

TY - CONF

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

Y1 - 2021

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 - Computer vision

KW - Espeletia

KW - Machine learning

KW - Páramos

KW - UAV

UR - https://www.scopus.com/pages/publications/85129831910

U2 - 10.1109/IGARSS47720.2021.9554263

DO - 10.1109/IGARSS47720.2021.9554263

M3 - Conference Paper

AN - SCOPUS:85129831910

SP - 2831

EP - 2834

T2 - 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021

Y2 - 12 July 2021 through 16 July 2021

ER -

Automatic Detection and Mapping of Espeletia Plants from UAV Imagery

Abstract

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Keywords

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