Exploring Machine Learning and Remote Sensing Techniques for Mapping Pinus Invasion Beyond Crop Areas

Andrey Naligatski Dias, Maria Eduarda Guedes Pinto Gianisella, Amanda Dos Santos Gonçalves, Rodrigo Minetto, Mauren Louise Sguario Coelho de Andrade

2025

Abstract

The spread of the exotic tree species from the Pinus spp. family has been increasing over the years in the Ponta Grossa region and other areas of southern Brazil, making its monitoring necessary. This study proposes to monitor this spread using deep neural networks trained on satellite images from the Campos Gerais region. For this task, three deep neural network models focused on pixel-by-pixel classification were employed: U-Net, SegNet, and FCN (Fully Convolutional Network). These models were trained on a dataset containing 34 images with a resolution of 2048x2048 pixels, obtained from Google Earth satellites. All images were downloaded using the QuickMapServices extension available in QGIS, and labeled using the same program. Promising results suggest that the U-Net model outperformed the others, achieving 82.49% accuracy, 69.62% Jaccard index, 41.19% recall, and 78.47% precision. The SegNet model showed good accuracy at 82.84%, but underperformed on the Jaccard index at 45.93%, with 58.34% recall and 68.35% precision. Meanwhile, the FCN model produced less reliable results among the three, with 79.37% accuracy, 29.17% Jaccard index, 34% recall, and 67.21% precision.

Paper Citation

in Harvard Style

Dias A., Gianisella M., Gonçalves A., Minetto R. and Coelho de Andrade M. (2025). Exploring Machine Learning and Remote Sensing Techniques for Mapping Pinus Invasion Beyond Crop Areas. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 873-879. DOI: 10.5220/0013383900003912

in Bibtex Style

@conference{visapp25,
author={Andrey Dias and Maria Eduarda Gianisella and Amanda Gonçalves and Rodrigo Minetto and Mauren Coelho de Andrade},
title={Exploring Machine Learning and Remote Sensing Techniques for Mapping Pinus Invasion Beyond Crop Areas},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={873-879},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013383900003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Exploring Machine Learning and Remote Sensing Techniques for Mapping Pinus Invasion Beyond Crop Areas
SN - 978-989-758-728-3
AU - Dias A.
AU - Gianisella M.
AU - Gonçalves A.
AU - Minetto R.
AU - Coelho de Andrade M.
PY - 2025
SP - 873
EP - 879
DO - 10.5220/0013383900003912
PB - SciTePress