Computer vision techniques for natural hazard detection

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Authors
Haridasan, Smitha
Advisors
Rattani, Ajita
Issue Date
2023-12
Type
Dissertation
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Abstract

The escalating frequency and magnitude of wildfires, exacerbated by climate change, present a formidable environmental challenge with devastating consequences. This dissertation addresses the imperative need for early wildfire detection using multi-spectral deep learning models and explores the intersection of social media data analytics for enhanced disaster response.

The first chapter introduces a novel multi-spectral deep learning model, leveraging diverse spectral information for improved accuracy in forest fire detection. Utilizing a heterogeneous dataset, the proposed model outperforms single-spectrum models by 1.9% and 14.8% in test and challenge sets, respectively, showcasing its efficacy in challenging environments.

Recognizing the pivotal role of social media in disaster reporting, the second chapter delves into the analysis of multi-modal Twitter datasets from natural disasters. We present a fusion-based decision-making technique that surpasses baseline models, achieving a 6.98% improvement in informative tweet classification and an 11.2% enhancement in humanitarian categorization.

The third chapter underscores the impact of wildfires on ecosystems and human communities, emphasizing the significance of deep learning in real-time monitoring. Deploying lightweight deep-learning architectures on drones and satellites, our survey elucidates the transformative potential of these technologies in early wildfire detection, offering precise, real-time information crucial for decision-making and resource allocation.

In response to computational challenges in real-time applications, the fourth chapter proposes a lightweight model for wildfire detection on unmanned aerial vehicles (UAVs). Evaluating three neural architecture search (NAS) methods, MNAS, RNAS, and BONAS, we identify efficient techniques for optimizing model size, speed, and accuracy. The proposed RNAS model achieves 84.38% accuracy with a remarkable compression ratio of 2.47 × 10−6 compared to the ResNet50 model.

In conclusion, our comprehensive review and proposed lightweight model contribute to advancing the state-of-the-art in wildfire detection. The synthesis of multi-spectral deep learning, social media analytics, and lightweight UAV models offers a holistic approach to mitigating the ecological, social, and economic impacts of wildfires.

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Thesis (Ph.D.)-- Wichita State University, College of Engineering, School of Computing
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Wichita State University
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