FOREST WILDFIRE DETECTION FROM SATELLITE IMAGES USING DEEP LEARNING

This study presents a novel method for automated detection of forest wildfires from satellite images using deep learning techniques. The method uses Convolutional Neural Networks (CNNs) to extract relevant features from high-resolution satellite images, which are preprocessed to remove noise and enhance features like smoke plumes, flames, and hotspots.The CNN model is trained on the preprocessed dataset using transfer learning, taking advantage of pre-trained models like VGG, ResNet, or Inception. Fine-tuning is performed to adapt the model to the specific task of wildfire detection. Performance metrics such as accuracy, precision, recall, and F1-score are used to assess the model’s effectiveness. The experimental results demonstrate the potential of deep learning-based wildfire detection from satellite images, achieving high accuracy and rapid identification of wildfire occurrences.Integrating this system into existing satellite image analysis pipelines can provide timely alerts and inform decisions to manage and mitigate wildfire impacts. This research contributes to early wildfire detection capabilities, protecting natural ecosystems and human populations in fire-prone regions.