This study explores the challenges of identifying forest areas in the “Carolina” Park in Quito, Ecuador, using Convolutional Neural Networks (CNN) and aerial imagery to support sustainable urban expansion plans. A dataset was constructed using 32 \(\,\times \,\) 32 \(\,\times \,\) 3 pixel patches extracted from 230 aerial images obtained from several videos captured by drones over the park. Three CNN models (ResNet50, EfficientNetB0 and VGG16) were trained to differentiate between forested, non-forested and hybrid areas. The methodology involved manual classification of 2100 patches into these three categories. The results showed that ResNet50 performed the best overall, with an accuracy of 76.66% ± 8%, followed closely by VGG16, while EfficientNetB0 showed inferior performance on this specific dataset. Qualitative analysis of predictions on test images confirmed the effective identification of forest areas. These findings suggest that ResNet50 may be a suitable model for this task, demonstrating a high ability to learn and recognize patterns in forested areas through patch-based analysis, even with relatively small datasets derived from aerial drone imagery.