Decision tree classification model for detecting and tracking precipitating objects from series of meteorological images

S. Ramirez; I. Lizarazo

doi:10.3990/2.384

Decision tree classification model for detecting and tracking precipitating objects from series of meteorological images

S. Ramirez^*, I. Lizarazo

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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Abstract

Accurate detection and identification of convective (cumulonimbus) clouds, which are potentially precipitating objects, as well as tracking cloud movement, are important tasks to locate and predict precipitation. In the present work, a Decision Tree classification model was used to locate and track precipitating objects from series of GOES-13 meteorological image sub-scenes covering the territory of Colombia, located to the northwest corner of South America. Results show that it is possible to infer a classification model that can be used repeatedly for accurately locating and tracking precipitating objects from multispectral meteorological images.

Original language	English
Title of host publication	Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands
Editors	N. Kerle, M. Gerke, S. Lefevre
Place of Publication	Enschede
Publisher	University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC)
Number of pages	5
ISBN (Print)	978-90-365-4201-2
DOIs	https://doi.org/10.3990/2.384
Publication status	Published - 14 Sept 2016
Externally published	Yes
Event	6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016: Solutions & Synergies - University of Twente Faculty of Geo-Information and Earth Observation (ITC), Enschede, Netherlands Duration: 14 Sept 2016 → 16 Sept 2016 Conference number: 6 https://www.geobia2016.com/

Conference

Conference	6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016
Abbreviated title	GEOBIA
Country/Territory	Netherlands
City	Enschede
Period	14/09/16 → 16/09/16
Internet address	https://www.geobia2016.com/

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10.3990/2.384

Ramirez-Decision Tree Classification Model For Detecting And Tracking Precipitating Objects-157Final published version, 2.29 MB

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GEOBIA 2016 : Solutions and Synergies., 14-16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC): open access e-book
Kerle, N. (Editor), Gerke, M. (Editor) & Lefèvre, S. (Editor), 2016, Enschede: University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC).
Research output: Book/Report › Book › Academic

Open Access
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Ramirez, S., & Lizarazo, I. (2016). Decision tree classification model for detecting and tracking precipitating objects from series of meteorological images. In N. Kerle, M. Gerke, & S. Lefevre (Eds.), Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC). https://doi.org/10.3990/2.384

Ramirez, S. ; Lizarazo, I. / Decision tree classification model for detecting and tracking precipitating objects from series of meteorological images. Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands. editor / N. Kerle ; M. Gerke ; S. Lefevre. Enschede : University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), 2016.

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title = "Decision tree classification model for detecting and tracking precipitating objects from series of meteorological images",

abstract = "Accurate detection and identification of convective (cumulonimbus) clouds, which are potentially precipitating objects, as well as tracking cloud movement, are important tasks to locate and predict precipitation. In the present work, a Decision Tree classification model was used to locate and track precipitating objects from series of GOES-13 meteorological image sub-scenes covering the territory of Colombia, located to the northwest corner of South America. Results show that it is possible to infer a classification model that can be used repeatedly for accurately locating and tracking precipitating objects from multispectral meteorological images.",

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publisher = "University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC)",

note = "6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016 : Solutions & Synergies, GEOBIA ; Conference date: 14-09-2016 Through 16-09-2016",

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Ramirez, S & Lizarazo, I 2016, Decision tree classification model for detecting and tracking precipitating objects from series of meteorological images. in N Kerle, M Gerke & S Lefevre (eds), Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands. University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), Enschede, 6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016, Enschede, Netherlands, 14/09/16. https://doi.org/10.3990/2.384

Decision tree classification model for detecting and tracking precipitating objects from series of meteorological images. / Ramirez, S.; Lizarazo, I.
Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands. ed. / N. Kerle; M. Gerke; S. Lefevre. Enschede: University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), 2016.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

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AU - Ramirez, S.

AU - Lizarazo, I.

N1 - Conference code: 6

PY - 2016/9/14

Y1 - 2016/9/14

N2 - Accurate detection and identification of convective (cumulonimbus) clouds, which are potentially precipitating objects, as well as tracking cloud movement, are important tasks to locate and predict precipitation. In the present work, a Decision Tree classification model was used to locate and track precipitating objects from series of GOES-13 meteorological image sub-scenes covering the territory of Colombia, located to the northwest corner of South America. Results show that it is possible to infer a classification model that can be used repeatedly for accurately locating and tracking precipitating objects from multispectral meteorological images.

AB - Accurate detection and identification of convective (cumulonimbus) clouds, which are potentially precipitating objects, as well as tracking cloud movement, are important tasks to locate and predict precipitation. In the present work, a Decision Tree classification model was used to locate and track precipitating objects from series of GOES-13 meteorological image sub-scenes covering the territory of Colombia, located to the northwest corner of South America. Results show that it is possible to infer a classification model that can be used repeatedly for accurately locating and tracking precipitating objects from multispectral meteorological images.

U2 - 10.3990/2.384

DO - 10.3990/2.384

M3 - Conference contribution

SN - 978-90-365-4201-2

BT - Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands

A2 - Kerle, N.

A2 - Gerke, M.

A2 - Lefevre, S.

PB - University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC)

CY - Enschede

T2 - 6th International Conference on Geographic Object-Based Image Analysis, GEOBIA 2016

Y2 - 14 September 2016 through 16 September 2016

ER -

Ramirez S, Lizarazo I. Decision tree classification model for detecting and tracking precipitating objects from series of meteorological images. In Kerle N, Gerke M, Lefevre S, editors, Proceedings of GEOBIA 2016 : Solutions and synergies, 14-16 September 2016, Enschede, Netherlands. Enschede: University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC). 2016 doi: 10.3990/2.384

Decision tree classification model for detecting and tracking precipitating objects from series of meteorological images

Abstract

Conference

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Research output

GEOBIA 2016 : Solutions and Synergies., 14-16 September 2016, University of Twente Faculty of Geo-Information and Earth Observation (ITC): open access e-book

Cite this