Automated Detection of Araraucaria angustifolia (Bertol.) Kuntze in Urban Areas Using Google Earth Images and YOLOv7x.

Karasinski, Mauro Alessandro; Leite, Ramon de Sousa; Guaraná, Emmanoella Costa; Figueiredo, Evandro Orfanó; Broadbent, Eben North; Silva, Carlos Alberto; Santos, Erica Kerolaine Mendonça dos; Sanquetta, Carlos Roberto; Dalla Corte, Ana Paula

This study addresses the urgent need for effective methods to monitor and conserve Araucaria angustifolia, a critically endangered species of immense ecological and cultural significance in southern Brazil. Using high-resolution satellite images from Google Earth, we apply the YOLOv7x deep learning model to detect this species in two distinct urban contexts in Curitiba, Paraná: isolated trees across the urban landscape and A. angustifolia individuals within forest remnants. Data augmentation techniques, including image rotation, hue and saturation adjustments, and mosaic augmentation, were employed to increase the model's accuracy and robustness. Through a 5-fold cross-validation, the model achieved a mean Average Precision (AP) of 90.79% and an F1-score of 88.68%. Results show higher detection accuracy in forest remnants, where the homogeneous background of natural landscapes facilitated the identification of trees, compared to urban areas where complex visual elements like building shadows presented challenges. To reduce false positives, especially misclassifications involving palm species, additional annotations were introduced, significantly enhancing performance in urban environments. These findings highlight the potential of integrating remote sensing with deep learning to automate large-scale forest inventories. Furthermore, the study highlights the broader applicability of the YOLOv7x model for urban forestry planning, offering a cost-effective solution for biodiversity monitoring. The integration of predictive data with urban forest maps reveals a spatial correlation between A. angustifolia density and the presence of forest fragments, suggesting that the preservation of these areas is vital for the species' sustainability. The model's scalability also opens the door for future applications in ecological monitoring across larger urban areas. As urban environments continue to expand, understanding and conserving key species like A. angustifolia is critical for enhancing biodiversity, resilience, and addressing climate change.

CONVOLUTIONAL neural networks; URBAN forestry; FOREST surveys; FOREST density; ENVIRONMENTAL monitoring; DEEP learning

Remote Sensing, 2025, Vol 17, Issue 5, p809

2072-4292

Academic Journal

10.3390/rs17050809