Recent advancements in fire detection algorithms, particularly through the use of Convolutional Neural Networks (CNNs), have significantly enhanced both accuracy and efficiency.

The integration of visible and infrared images has proven to be crucial, especially under challenging conditions like smoke or darkness, thereby improving detection capabilities.

Current fire detection technologies often lack clarity in the information they provide, which has led to increased research into advanced image processing techniques.

The GCM-YOLO model has demonstrated impressive results, achieving an average precision of 82.9% and a recall rate of 76.9%, while also reducing computational load.

The model's performance was rigorously evaluated, focusing on metrics such as recall, precision, and computational efficiency, showcasing its effectiveness compared to mainstream object detection algorithms.

In addition, a forest fire prediction model has shown strong performance with an accuracy of 86.00% and an AUC value of 90.50%, highlighting its potential for real-world applications.

Key findings from the research indicate that high-density areas of fire occurrences are primarily located in Hunan and Jiangxi provinces, underscoring the need for targeted fire prevention strategies.

The study emphasizes that forest fires, mainly caused by human activities, have detrimental effects on the environment, necessitating proactive detection strategies.

Wildland fires not only lead to significant environmental damage, including air pollution and deforestation, but also pose fatal risks to firefighters and the public.

Utilizing Geographic Information System (GIS) technology alongside CNN models, the research aims to predict forest fire occurrences and develop effective management strategies.

Notable advancements in fire detection include systems like SmokeNet, which enhances smoke detection from satellite data, and the integration of wind sensors for early fire warnings.