Abstract:
Deforestation is a significant threat to biodiversity, climate stability, and global heat
regulation. There are various reasons that lead to deforestation. Some are natural reasons,
and some are human activities. Human activities are the most contributing factor for the
deforestation. Various actions have been taken by the government, NGOs, and international
organisations to prevent deforestation. Strict regulations, reforestation programs, and
educating people on preserving land resources are some of the main actions taken by the
mentioned group of people. However, still deforestation is happening all around the world.
Deep learning based ensemble model was developed by combining two powerful
architectures to detect deforestation. Convolutional Neural Network (CNN) and Vision
Transformer (ViT) are two powerful architectures that used in this research project. The
CNN was designed with four sequential blocks (from 64 to 512 filters) that progressively
learned more detailed features from satellite images. Techniques such as batch
normalization, dilated convolutions, and residual connections were applied to improve
learning stability. Also that expand the model’s view of image patterns, and support deeper
layers.To prevent overfitting, dropout was gradually increased in deeper layers. The ViT
model was optimized using patch-based input (16×16), a 256-dimensional embedding, and
five transformer encoder layers with 8 attention heads, allowing it to learn global patterns
across the image. Both models were fine-tuned using systematic hyperparameter tuning via
Optuna, which guided the selection of learning rates, dropout rates, and layer sizes. Their
outputs were combined using a two-layer neural stacker, creating a robust ensemble capable
of predicting multiple deforestation-related labels for each image.
Because of this Research based on multi label image classification, researcher used suitable
metrics to evaluate the model. Below you can find the evaluation results for the whole
model.
Micro-Averaged Precision: 0.9641, Recall: 0.7458, F1-score: 0.8410
Macro-Averaged Precision: 0.6030, Recall: 0.3444, F1-score: 0.4038
Hamming Loss: 0.0480
