A CNN and Deep Learning-based Smart Satellite Surveillance System for Wildfire Detection and Wildlife Damage Assessment

Wildfires are a growing concern due to their catastrophic impacts on the environment, ecosystem services, biodiversity, and human settlements. Different fire detection systems face challenges with responding within a reasonable timeframe and have limited spatial coverage, which makes proactive intervention more difficult. This paper designs a smart reconnaissance system based on the Convolutional Neural Network architecture (CNN) and the principles of deep learning for the automatic detection of wildfires and for assessing wildlife damage employing satellite images. The proposed system aims at processing satellite data in real-time to ascertain fire hazard zones, detect ongoing fire outbreaks, and estimate ecological damage in a timely manner and with the highest accuracy possible. Unlike conventional approaches that rely heavily on handcrafted features or static thresholding methods, our approach combines advanced spatial feature extraction and temporal pattern recognition. Moreover, the model is enhanced for more reliable detection across diverse terrain and weather conditions over a longer time period. In addition, the architecture proposed incorporates a dedicated wildlife damage assessment subsystem which classifies the affected zones as vegetation loss, indicators of animal migration, and thermal signatures. Their first analysis of the system shows that the system can surpass current solutions in sensitivity and operational speed. With the proposed model in place, we anticipate that it will act as an invaluable decision aid as it can provide environmental agencies automated scalable real-time wildfire intelligence, helping to mitigate ecological loss.