PREVENT Computer Vision Theory (Helixconnect)

Computer Vision - PREVENT Project

Theory & Applications in Disaster Management

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Introduction

In the context of disaster management, computer vision plays a crucial role in enhancing the capabilities of response teams. Computer vision, a field at the intersection of computer science and artificial intelligence, focuses on enabling machines to interpret and understand visual information from the world. By processing and analysing images and videos, computer vision systems can perform tasks that typically require human visual perception. These tasks range from object detection and recognition to image segmentation and 3D scene reconstruction.

Index

Recent Trends and Generative AI

What is Computer Vision

Computer Vision in Disaster Management

Importance & Advancements

Activity

History of Computer Vision

Assesment

Useful definition

What is Computer Vision

In the context of disaster management, computer vision plays a crucial role in enhancing the capabilities of response teams. It enables real-time monitoring and assessment of disaster-affected areas, supports search and rescue operations, and aids in damage assessment and resource allocation. By providing detailed and accurate visual data, computer vision helps decision-makers act swiftly and effectively during emergencies. Key Tasks:

• Object detection
• Image segmentation
• 3D scene reconstruction

'Computer Vision is a field at the intersection of computer science and AI that enables machines to interpret and understand visual information.'

Techniques such as convolutional neural networks (CNNs) have revolutionised the field, enabling high accuracy in image and video analysis. Today, computer vision is integral to various applications, from autonomous vehicles and facial recognition systems to medical imaging and augmented reality.

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Importance & Advancements

Computer vision plays a crucial role in disaster prevention by enabling early detection, monitoring, and response to natural and man-made disasters. It enhances the efficiency of emergency management by providing real-time data analysis, which helps authorities make informed decisions.

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Key benefits

Early Warning Systems – Computer vision can detect patterns of environmental changes, such as rising water levels, wildfire smoke, or structural damage, enabling early alerts.Real-Time Monitoring – Drones and surveillance cameras equipped with computer vision can track disaster-prone areas to assess risks continuously. Damage Assessment – After a disaster, computer vision can rapidly analyze satellite images and videos to assess the extent of damage and prioritize relief efforts. Search & Rescue Operations – AI-driven object recognition helps identify survivors trapped in debris or flooded areas, improving rescue efficiency. Resource Allocation – By analyzing terrain and infrastructure damage, computer vision helps optimize the distribution of emergency resources.

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'Computer vision is revolutionizing disaster prevention by providing real-time monitoring, predictive analysis, and automated response capabilities. As AI and machine learning technologies continue to advance, disaster management strategies will become more proactive and effective, ultimately saving lives and reducing economic losses.

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Hisotry of computer vision

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The Deep Learning Revolution (Early 2000s – 2010s)

Evolution in the 1980s & 1990s

Beginnings (1960s-1970s)

Recent Trends and Generative AI

The beginnings

Computer Vision

Beginnings (1960s-1970s)

Early research in computer vision centered on interpreting two-dimensional images, with a significant focus on edge detection and basic shape analysis.

• 2D Image Interpretation: Researchers initially worked with flat images, striving to understand how machines could extract meaningful information from them. This involved translating visual cues into data that computers could analyze.
• Edge Detection: Identifying edges was one of the first steps in image processing. Edges represent the boundaries where there is a sudden change in intensity or color, which helps in distinguishing different objects or parts of an object in an image.
• Basic Shape Analysis: Once edges were detected, early systems attempted to piece them together to form simple geometric shapes. This process allowed computers to begin recognizing and classifying objects based on their outlines and structural features.

Larry Roberts is a computer vision pioneer whose groundbreaking 1960s work, especially his dissertation "Machine Perception of Three-Dimensional Solids," established methods for extracting 3D structure from 2D images. His research on edge detection, object segmentation, and pattern recognition laid the groundwork for modern computer vision systems and spurred the development of algorithms now used in diverse fields, including disaster prevention for early detection, rapid damage assessment, and real-time monitoring with aerial and satellite imagery.

Evolution in the 1980s & 1990s

In the 1980s, computer vision evolved from theoretical research to practical applications, with a focus on motion detection and image segmentation. Motion detection techniques allowed systems to track moving objects, leading to early implementations in surveillance and robotics. Meanwhile, image segmentation enabled the separation of objects from backgrounds, improving automated analysis in fields like medical imaging and industrial inspection (Rosenfeld & Kak, 1982). By the 1990s, statistical methods became a key component of computer vision. Probabilistic models were introduced to enhance image recognition, making systems more robust against noise and variations (Marr, 1991). Neural networks and Support Vector Machines (SVMs) began to gain traction, improving object classification and facial recognition (Scholkopf et al., 1997). One of the most significant breakthroughs of the decade was the Viola-Jones framework for real-time face detection, which combined simple features with machine learning to enable fast and efficient recognition (Viola & Jones, 2001, building on work from the late 1990s). These advancements in motion tracking, segmentation, and statistical learning paved the way for modern applications in security, medical diagnostics, and disaster prevention, where real-time image analysis is critical for early warning and response systems.

Evolution in the 1980s & 1990s

1980s: Motion Detection & Image Segmentation The 1980s marked significant progress in computer vision, particularly in motion detection and image segmentation. Rosenfeld and Kak’s Digital Picture Processing (1982) provided a foundational framework for digital image analysis, covering essential techniques for object detection and segmentation. Horn and Schunck (1981) introduced the optical flow concept, which became a key method for motion tracking by estimating pixel displacement between image frames. Later, Canny (1986) developed the Canny Edge Detector, a widely used algorithm for detecting edges with optimal accuracy and minimal noise, which significantly improved image segmentation and feature extraction. 1990s: Statistical Methods, Neural Networks, SVMs & Face Detection In the 1990s, computer vision saw a shift toward statistical models and machine learning approaches. Marr (1991) explored computational models for human vision, influencing probabilistic techniques for image interpretation. Schölkopf et al. (1997) advanced the field by developing Support Vector Machines (SVMs) for object classification, enabling better feature extraction and decision-making in computer vision tasks. The decade also saw the emergence of Convolutional Neural Networks (CNNs), as introduced by LeCun et al. (1998), revolutionizing image recognition and setting the stage for deep learning advancements. One of the most notable breakthroughs was the Viola-Jones framework for real-time face detection (2001), which applied machine learning to detect faces efficiently in images. This technique became a cornerstone for modern facial recognition systems, greatly enhancing security, biometrics, and automated surveillance.

The Deep Learning Revolution (Early 2000s – 2010s)

The early 2000s to the 2010s marked a transformative period in computer vision, driven by deep learning, especially Convolutional Neural Networks (CNNs). While neural networks had been explored in the 1990s, it was during this period that advances in computational power, large-scale datasets, and improved algorithms led to breakthroughs in image recognition, object detection, and scene understanding.The Deep Learning Revolution (DLR) transformed computer vision by shifting from handcrafted feature extraction to data-driven learning using deep neural networks. It revolutionized how machines perceive and interpret visual data, leading to major breakthroughs in image classification, object detection, segmentation, and image synthesis.Impact of the Deep Learning Revolution

• AI-powered vision systems now surpass human-level accuracy in many tasks (e.g., medical imaging, facial recognition).
• Enabled real-time applications like autonomous vehicles, AI surveillance, and augmented reality.
• Gave rise to new research areas like self-supervised learning and multimodal AI, pushing computer vision beyond traditional boundaries.

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'The future of artificial intelligence is not in programming machines to follow rules, but in teaching them to learn from data.' Fei-Fei Li, AI Researcher & Creator of ImageNet

The Deep Learning Revolution (Early 2000s – 2010s)

The deep learning revolution of the 2000s–2010s transformed computer vision, replacing handcrafted features with data-driven learning and setting the stage for modern AI-powered visual perception systems.

Key Milestones & Breakthroughs AlexNet (2012) – Deep CNNs Take Over: Krizhevsky, Sutskever, and Hinton introduced AlexNet, which won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) by a significant margin. Demonstrated that deep CNNs, trained on large datasets using GPUs, could dramatically outperform traditional machine learning methods.ResNet (2015) – Deeper Networks with Skip Connections: He et al. introduced ResNet (Residual Networks), solving the problem of vanishing gradients in deep networks by using skip connections. Enabled training of extremely deep networks (e.g., 152 layers), setting new records in image classification accuracy.Faster R-CNN & YOLO (2015-2016) – Real-Time Object Detection: Faster R-CNN (Ren et al.) introduced a Region Proposal Network (RPN) for efficient object detection. Redmon et al. introduced YOLO (You Only Look Once), a real-time object detection algorithm that processed images in a single pass, making applications like autonomous driving and surveillance more practical.GANs (2014) – Image Synthesis & Generation: Goodfellow et al. introduced Generative Adversarial Networks (GANs), allowing machines to generate realistic images, which later found applications in image enhancement, deepfakes, and data augmentation.Transformers in Vision (2017-Present): Vaswani et al. introduced the Transformer architecture for NLP, later adapted into Vision Transformers (ViTs), which challenged CNN dominance in image recognition.

Recent Trends and Generative AI

Computer vision is evolving rapidly, with Generative AI leading a new wave of innovations. Modern advancements go beyond image recognition, enabling AI to generate, manipulate, and understand visual data in more sophisticated ways.

AI-Powered Video Synthesis & Deepfake Detection

Generative AI – Creating & Manipulating Visual Content

Self-Supervised Learning (SSL) – AI Learning Without Labels

Vision Transformers (ViTs) – A Shift Beyond CNNs

Vision Transformers (ViTs) – A Shift Beyond CNNs

Traditional Convolutional Neural Networks (CNNs) dominated for years, but Vision Transformers (ViTs) are now challenging CNN supremacy. ViTs, introduced by Dosovitskiy et al. (2020), process entire images at once using self-attention mechanisms, improving performance in image classification and segmentation. Companies like Google and Meta are integrating ViTs into real-world applications like medical imaging and autonomous systems.

Self-Supervised Learning (SSL) – AI Learning Without Labels

Traditional deep learning needs huge labeled datasets, but labeling is slow, expensive, and impractical—especially for disaster response. Self-Supervised Learning (SSL) solves this by training AI on unlabeled data, learning patterns without manual annotations. Key methods include:

• SimCLR & MoCo: Contrastive learning to recognize similar/different images.
• DINO: Self-teaching AI for scene understanding.

Impact: SSL is transforming medical imaging, autonomous driving, and disaster response by making AI scalable, faster, and cost-effective. In crisis situations, it enables real-time damage assessment and rescue planning using unlabeled satellite and drone imagery.

Generative AI – Creating & Manipulating Visual Content

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of visual informationis better assimilated.

Generative AI is one of the biggest breakthroughs in recent years, allowing AI to create realistic images, videos, and 3D models.

Key Technologies in Generative AI: GANs (Generative Adversarial Networks) – Introduced by Goodfellow et al. (2014), GANs generate hyper-realistic images, deepfakes, and AI-generated artwork. Diffusion Models – Used in AI tools like DALL·E 2, MidJourney, and Stable Diffusion, diffusion models can generate high-quality, photorealistic images from text descriptions. NeRF (Neural Radiance Fields) – Converts 2D images into 3D models, enabling applications in virtual reality (VR), gaming, and architecture.

AI-Powered Video Synthesis & Deepfake Detection

AI-powered video analysis enhances early disaster detection, risk assessment, and emergency response. By generating, editing, and enhancing video data, AI improves real-time monitoring of hazards like wildfires, floods, and earthquakes. However, the rise of misinformation and deepfakes poses risks, such as false disaster reports that can cause panic. To counter this, AI-driven deepfake detection tools ensure the authenticity and reliability of disaster-related footage, supporting accurate decision-making.

Deepfake Prevention in Disaster Reporting

Earthquake Impact Assessment

Flood Monitoring

Wildfire Detection

AI tools like Microsoft’s Video Authenticator and Deepfake Detection Challenge models verify the authenticity of disaster footage, preventing false alarms and misinformation that could lead to unnecessary panic or resource misallocation.

AI processes videos from disaster zones, automatically detecting collapsed buildings and infrastructure damage. After the 2023 Turkey-Syria earthquake, AI-assisted drone footage helped first responders locate survivors more efficiently.

AI models analyze surveillance cameras and satellite imagery to track rising water levels and predict flood risks. The European Space Agency’s FloodAI system provides early warnings by monitoring rivers and coastlines, helping communities prepare in advance.

AI-enhanced satellite and drone footage can identify early signs of wildfires, such as smoke patterns and heat signatures. Systems like California’s ALERTWildfire use real-time AI analysis to detect and predict fire spread, allowing faster evacuation and response.

Useful definition

Useful deficnitions

Deep learning is a subset of machine learning that uses neural networks with many layers (hence "deep") to model complex patterns in data. In computer vision, deep learning techniques such as CNNs have revolutionised the field by significantly improving performance on tasks like image recognition and object detection. A Convolutional Neural Network is a type of deep learning algorithm designed to process structured grid data such as images. CNNs use convolutional layers that apply filters to input images, enabling the extraction of hierarchical features, from edges and textures to complex patterns and objects. Feature extraction involves identifying and isolating specific attributes or pieces of information from an image, such as edges, textures, or shapes. These features are used to represent the image in a way that is useful for further processing tasks like classification or object recognition. Edge detection is a technique used to identify the boundaries within images. It is a fundamental tool in computer vision for object detection and recognition. Algorithms like the Canny edge detector are widely used for this purpose. Image classification is the task of assigning a label to an entire image based on its visual content. This involves training a model to recognise patterns and features that correspond to different classes. Common datasets for image classification include MNIST and ImageNet.

Object detection is a computer vision task that involves identifying and locating objects within an image or video. It combines image classification and localisation, often using bounding boxes to highlight detected objects. Popular object detection algorithms include YOLO (You Only Look Once) and Faster R-CNN. Image segmentation is the process of partitioning an image into multiple segments or regions to simplify its analysis. It is used to identify boundaries and objects within images with methods like semantic segmentation (labelling each pixel with a class) and instance segmentation (differentiating between instances of the same object class). Semantic segmentation is the process of classifying each pixel in an image into a predefined category. Unlike object detection, which identifies objects as whole entities, semantic segmentation provides a pixel-level understanding of the image, making it useful for tasks like autonomous driving and medical imaging. Optical flow is the pattern of apparent motion of objects, surfaces, and edges in a visual scene caused by the relative movement between an observer and the scene. It is used in computer vision for motion detection, video tracking, and 3D reconstruction. A Generative Adversarial Network is a class of machine learning frameworks consisting of two neural networks—a generator and a discriminator—that compete with each other. The generator creates fake images while the discriminator attempts to distinguish them from real images, leading to the generation of highly realistic images. Diffusion models are a type of generative model that creates data by simulating the process of gradual denoising. Starting with a noisy version of the target data, these models iteratively refine it to produce high-quality, realistic outputs. Diffusion models have been particularly effective in generating detailed images and have applications in tasks such as image synthesis and enhancement, offering an alternative to Generative Adversarial Networks (GANs) for high-fidelity image generation.

5 Real World Applications of Computer Vision

​The video titled "5 Real World Applications of Computer Vision" explores five practical uses of computer vision technology. It highlights how computer vision is applied in various industries, including healthcare, automotive, retail, agriculture, and security. The video provides insights into how machines interpret visual data to perform tasks such as medical diagnostics, autonomous driving, inventory management, crop monitoring, and surveillance.

Recent Trends and Generative AI

AI in disaster prevention

Predicting Environmental Disasters and Climate Change

Ethical Concerns and Challenges

AI in disaster prevention

AI models, particularly those based on deep learning, have shown great potential in improving early warning systems for disasters like earthquakes, tsunamis, and hurricanes. Earthquake Prediction: While predicting earthquakes is still a challenge, recent advancements in seismic data analysis using AI, especially CNNs and recurrent neural networks (RNNs), are making it possible to detect anomalies in seismic patterns and issue warnings about potential tremors. Flood Prediction: By analyzing satellite imagery, climate data, and weather forecasts, AI models can predict areas at high risk of flooding, especially in regions prone to flash floods. Generative models like Generative Adversarial Networks (GANs) are being used to create realistic simulations of potential disaster scenarios, helping cities plan for the worst by visualizing flood paths, damage, and resource allocation. Once a disaster occurs, computer vision and generative AI help quickly assess the damage and direct resources. Damage Assessment Using Drone and Satellite Images: Drones and satellites equipped with AI-powered computer vision algorithms can rapidly assess damage after events like earthquakes, wildfires, and flooding. AI can analyze images for structural damage, landslides, or flooded areas, and generate reports almost in real-time. Search and Rescue Operations: AI can also assist in search and rescue efforts by identifying people trapped in debris after natural disasters. By analyzing drone or satellite images, AI can pinpoint areas of interest for human rescuers, speeding up the recovery process.

Predicting Environmental Disasters and Climate Change

Generative AI also plays a role in predicting the long-term effects of climate change, which in turn helps with preparing for environmental disasters such as droughts, heatwaves, and rising sea levels. Climate Modeling: Generative models can simulate future climate scenarios based on historical data, predicting regions that may face increased risk of drought, flooding, or extreme heat. These simulations can help policymakers design mitigation strategies for long-term climate change adaptation. Ecosystem Monitoring: Generative AI models can simulate changes in ecosystems, identifying areas where habitats may be lost due to environmental changes. This can assist in disaster preparedness for species at risk. AI models are being integrated into risk assessment frameworks to evaluate the likelihood and potential impact of various disasters. These models can then recommend optimal responses, including resource allocation and evacuation strategies. Insurance and Financial Planning: AI models help the insurance industry predict the financial impact of natural disasters, setting appropriate premiums and preparing for large-scale payouts after an event. Resource Allocation: Generative AI can simulate disaster scenarios to optimize the allocation of emergency resources, helping emergency teams to prioritize their efforts based on simulated damage estimates.

Ethical Concerns and Challenges

While generative AI and computer vision have provided significant advancements in disaster prediction and response, several challenges remain: Data Quality: AI models rely on high-quality data, and in many regions, there may not be enough reliable data to create accurate models. Bias: AI systems may be biased based on the data they are trained on, potentially leading to inaccurate predictions for certain regions or populations. Privacy and Security: As AI systems collect and analyze data, especially real-time video and imagery, issues surrounding privacy and data security must be addressed to ensure ethical deployment.

Computer Vision in Disaster Management

Computer vision technology has become a pivotal tool in disaster management, significantly enhancing the efficiency and effectiveness of response efforts.

Computer vision algorithms process satellite and drone images to continuously monitor disaster-prone regions.

AI models detect changes in the landscape, such as submerged areas, and help authorities identify flooded regions quickly, which is critical for deploying emergency responses.

Computer Vision in Disaster Management

Real-Time Monitoring: AI processes satellite and drone imagery to track disasters like wildfires, floods, and storms, enabling rapid detection of affected areas. Damage Assessment: After a disaster, computer vision analyzes imagery to quickly assess damage to buildings, infrastructure, and roads, helping prioritize relief efforts. Search and Rescue: AI identifies survivors and obstacles in debris through human detection in real-time drone footage, guiding rescue teams. Disaster Prediction: Computer vision aids in predicting disasters by analyzing weather patterns and satellite imagery, allowing for early warnings. Resource Allocation: AI optimizes the distribution of resources by evaluating damage maps and assessing urgent needs, enhancing response coordination. Recovery and Reconstruction: Post-disaster, computer vision helps assess long-term damage and tracks reconstruction efforts through updated imagery.

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Exploring Computer Vision in Disaster Management

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In this section, you will have the opportunity to test your acquired knowledge throughout the course. Our interactive quiz will provide a detailed assessment of your understanding of key topics. Get ready to challenge your skills and reinforce your learning as you move towards mastering the fundamental concepts. Don't miss the chance to demonstrate everything you've learned so far!

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Deep artificial intelligence applications for natural disaster management systems: A methodological review

The paper by Akhyar et al. (2024), titled "Deep artificial intelligence applications for natural disaster management systems: A methodological review" and published in Ecological Indicators, is an excellent resource that comprehensively surveys how deep AI is being applied in disaster management contexts. It offers a detailed methodological review of various deep learning techniques—including computer vision applications—that are used to monitor, assess, and manage natural disasters.Key highlights of the paper The authors review a range of AI methods, discussing how they are tailored for real-time data analysis, damage assessment, and predictive modeling in disaster scenarios. The paper emphasizes the importance of integrating AI with other disaster management tools, such as GIS and communication networks, to enhance response capabilities. It also addresses the challenges of data quality, computational limitations, and system interoperability, while pointing out future research directions to overcome these hurdles. Reference:Akhyar, A., Zulkifley, M. A., Lee, J., Song, T., Han, J., Cho, C., ... & Hong, B. W. (2024). Deep artificial intelligence applications for natural disaster management systems: A methodological review. Ecological Indicators, 163, 112067. https://doi.org/10.1016/j.ecolind.2024.112067