Surface Fire Test Using Diffusion-Augmented Multimodal Deep Belief Learning

Aurnob, Al Rafi

doi:10.1007/s10694-026-01881-8

Surface Fire Test Using Diffusion-Augmented Multimodal Deep Belief Learning

2026-04-01

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DOI：10.1007/s10694-026-01881-8

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One of the issues with a wildfire detection system is its limited and poor-quality datasets of images, which hinder the performance of accurate and timely detection. To address this, as a solution, the proposed study aims to utilize a Generative Artificial Intelligence (GAI) based methodology, in which high-fidelity wildfire-related imagery would be generated using Stable Diffusion, thus improving the quality and level of training data diversity. A multimodal deep belief network (DBN) is used for classification using deep learning capabilities to improve the accuracy of distinguishing between fire and non-fire scenarios. Explainable Artificial Intelligence (XAI) techniques are incorporated into the classification framework as a further attempt to increase the transparency of the model. The experimental results demonstrate the effectiveness of the proposed approach, achieving the test accuracy of 99.67% in the synthetic data set and 99.55% and 99.63% in two real-world data sets. The results show that the integration of generative artificial intelligence, deep learning, and explainability contributes to better wildfire detection performance. Furthermore, this method delivers more efficient and transparent AI tools, which could be trained in various settings, and can support the creation of high-quality contextual-adequate imagery.

Keywords:

Generative artificial intelligence

Stable diffusion

Multimodal deep belief network

Explainable artificial intelligence

Fire image analysis

Surface Fire Test Using Diffusion-Augmented Multimodal Deep Belief Learning

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