Hybrid Deep Learning Framework for Real-Time Forest Monitoring and Early Fire Detection Using Multi-Source Imagery | IJCT Volume 13 – Issue 2 | IJCT-V13I2P26
Hybrid Deep Learning Framework for Real-Time Forest Monitoring and Early Fire Detection Using Multi-Source Imagery | IJCT Volume 13 – Issue 2 | IJCT-V13I2P26
This study presents a hybrid deep learning framework for real-time forest monitoring and early fire detection using multi-source imagery. The proposed system combines Convolutional Neural Networks (CNNs) for spatial feature extraction with Long Short-Term Memory (LSTM) networks to capture temporal variations in forest environments. A preprocessing and augmentation pipeline improves robustness under diverse environmental conditions, while ensemble learning reduces false detections. Model optimization enables deployment on edge devices for real-time applications. Experimental results show improved accuracy and reliability compared with conventional approaches. Integration with Geographic Information System (GIS) visualization supports spatial risk mapping and automated alert generation, assisting efficient forest management and wildfire prevention.
Keywords
Deep learning, Forest fire detection, CNN–LSTM, Real-time monitoring, GIS.
Conclusion
This project successfully demonstrated that a deep learning-based integrated framework can accurately classify forest fire and non-fire images while supporting real-time deployment. By combining CNNs and LSTMs, the system addressed challenges including limited labeled datasets, high visual variability, and the need for fast, stable predictions. The hybrid CNN–LSTM pipeline significantly improved feature extraction, temporal understanding, and operational reliability.
The CNN component captured fine spatial features including flame edges, smoke regions, vegetation patterns, and anomalous bright spots. The LSTM layers analyzed sequential patterns such as drifting smoke and flickering flames, reducing false alarms from static objects. Together they produced a reliable spatiotemporal model with accurate predictions across diverse forest types, seasons, and atmospheric conditions.
Optimization via pruning, quantization, and knowledge distillation enabled deployment on IoT devices, drones, and edge cameras. The model delivered strong accuracy, high precision, and reliable recall. Early detection minimizes ecological loss, reduces financial damage, and provides authorities with ample response time, demonstrating the potential of modern AI in transforming forest monitoring and management.
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How to Cite This Paper
I. Sree Anushka, B. Rishitha, Ms. Neha Tanveer (2026). Hybrid Deep Learning Framework for Real-Time Forest Monitoring and Early Fire Detection Using Multi-Source Imagery. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
