A deep learning approach for average height estimation in oak colony using rgb images

Britto, Raphael Duarte; Mendes, João; Grilo, Vinicius; Castro, João Paulo; Santos, Murillo Ferreira dos; Castro, Marina; Pereira, Ana I.; Lima, José

http://hdl.handle.net/10198/35169

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Authors

Britto, Raphael Duarte

Mendes, João

Grilo, Vinicius

Castro, João Paulo

Santos, Murillo Ferreira dos

Castro, Marina

Pereira, Ana I.

Lima, José

Abstract(s)

Many strategies have been developed to monitor the volume of volume of Above Ground Biomass (AGB) in forest areas as a fundamental step for managing carbon concentration. This study explores the use of use of Light Detection and Ranging (LiDAR) data obtained through Unmanned Aerial Vehicles (UAVs) to estimate height values in a vegetation colony composed of oaks (Quercus pyrenaica Willd.) in northern Portugal. The extraction of pertinent information from LiDAR data was facilitated by using the LAStools extension within the Quantum Geographic Information System (QGIS) software framework. The generated raster and image information were used to calculate the height values of the vegetation. Following this extraction, the information was meticulously organized into datasets, which were then employed in Deep Learning (DL) algorithms. The VGG16 model was selected as the underlying framework for the present study. Height predictions were made using dimensions of 16× 16, 32× 32, and 64 × 64 pixels for the Red, Green and Blue (RGB) images. The data was estimated and compared using both the standard format of the VGG16 model and a superficially adapted version of its convolution layers. The algorithm’s efficacy was validated by comparing the forecast results with the data obtained from QGIS, which revealed minimal discrepancies. It was observed that using 64× 64 pixel scale images yielded enhanced accuracy, resulting in reduced values for the Mean Absolute Error (MAE). The study demonstrates the viability of applying DL techniques to accurately capture information about a forest area using RGB images.

Keywords

Deep Learning LiDAR QGIS RGB Images VGG16

URI

http://hdl.handle.net/10198/35169

Citation

Britto, Raphael; Duarte Mendes, João; Grilo, Vinicius; Castro, João P.; Santos, Murillo; Ferreira dos Castro, Marina; Pereira, Ana I.; Lima, José (2026). A deep learning approach for average height estimation in oak colony using rgb images. In 5th International Conference OL2A. Cham: Springer Nature. p. 272-285. ISBN 9783032001368