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Automatic Individual Tree Detection from Combination of Aerial Imagery, LiDAR and Environment Context

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16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021) (SOCO 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1401))

Abstract

Geographic Information Systems (GIS) allow analysis based on geo-referenced data. Currently only simple geo-referenced information is available, such as road networks or types of terrain, but there are other geo-referenced data that would be very useful to facilitate decision-making. These data are not collected as they are very hard to generate manually, but remote sensing data and artificial intelligence can be used to accomplish it. This work aims to develop an automatic framework for the extraction of geo-referenced trees, through the union Light Detection and Ranging (LiDAR) point clouds, aerial imagery, and existing GIS environment context. The results of the process are satisfactory, improving in some several areas the LiDAR-based detections using only imagery. However, issues such as false positives need to be corrected in the future. Merging both data sources would allow better results to be achieved.

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Acknowledgement

This work was funded by public research projects of Spanish Ministry of Economy and Competitivity (MINECO), reference TEC2017-88048-C2-2-R.

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Authors and Affiliations

  1. Group GIAA, University Carlos III of Madrid, Madrid, Spain

    Daniel Amigo, David Sánchez Pedroche, Jesús García & José M. Molina

Authors
  1. Daniel Amigo
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  2. David Sánchez Pedroche
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  3. Jesús García
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  4. José M. Molina
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Corresponding author

Correspondence to Daniel Amigo .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Hugo Sanjurjo González

  2. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Iker Pastor López

  3. Faculty of Engineering, University of Deusto, Bilbao, Spain

    Pablo García Bringas

  4. Department of Industrial Engineering, University of A Coruña, Ferrol, Spain

    Héctor Quintián

  5. BISITE Research Group, University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Amigo, D., Pedroche, D.S., García, J., Molina, J.M. (2022). Automatic Individual Tree Detection from Combination of Aerial Imagery, LiDAR and Environment Context. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_28

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