PREVENT Computer Vision Practical Session (Helixconnect)

Computer Vision - PREVENT Project

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Practical sesion

Introduction

'You know a presentation is WOW when you capture the attention of your audience and everyone assimilates the information'Computer vision technology has become a pivotal tool in disaster management, significantly enhancing the efficiency and effectiveness of response efforts

Assesment

Introduction

Objectives

Applications to Disaster Management

Challenges for Computer Vision in Disaster Management

Advantages of Disaster management

Modules

Activity

Index

Objectives

1. To explore the key applications of computer vision technology in disaster management, including real-time monitoring, search and rescue, damage assessment, resource allocation, and environmental monitoring, and understand how these applications enhance disaster response and recovery efforts.
2. To identify the challenges faced by computer vision in disaster management, such as issues with data quality, environmental complexity, computational requirements, and integration with other systems, and to discuss the impact of these challenges on the effectiveness of disaster management operations.
3. To examine the potential of computer vision in disaster prediction and early warning systems, focusing on how predictive analytics and visual data can improve preparedness, help in risk assessment, and guide timely interventions to mitigate the impact of natural and man-made disasters.

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'A presentation is NOT WOW when it's boring and you see drowsiness taking over your audience because no one has understood anything'

To explore the key applications of computer vision technology in disaster management

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To identify the challenges faced by computer vision in disaster management

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'To examine the potential of computer vision in disaster prediction and early warning systems

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Advantages of Computer Vision in Disaster Management

Modules

Through ongoing research, innovation, and collaboration among technologists, disaster management professionals, and policymakers, the full potential of computer vision can be harnessed to strengthen disaster resilience and response.

Applications to Disaster Management

Challenges for Computer Vision in Disaster Management

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Applications to Disaster Management

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Applications to Disaster Management

Real-Time Monitoring and Surveillance: Computer vision systems are crucial for real-time monitoring and surveillance during disasters. Drones equipped with cameras can provide aerial views of affected areas, allowing for immediate assessment of damage and identification of hazards. These systems can detect changes in the environment, such as rising water levels during floods or the spread of wildfires, providing valuable data for timely intervention. Search and Rescue Operations: In search and rescue missions, computer vision technology enhances the ability to locate survivors quickly and accurately. Advanced algorithms can analyse video feeds from drones or ground robots to detect human figures, even in complex environments with debris and obstacles. Thermal imaging combined with computer vision can identify heat signatures of trapped individuals, improving the chances of successful rescues. Damage Assessment: Post-disaster damage assessment is critical for planning recovery efforts. Computer vision can automate the process of evaluating the extent of damage to infrastructure such as buildings, bridges, and roads. High-resolution images captured by drones can be processed using image recognition algorithms to classify and quantify the damage, providing detailed reports that aid in prioritising repair and reconstruction activities.

Applications to Disaster Management

Resource Allocation and Distribution: Effective resource allocation and distribution are essential during disaster relief operations. Computer vision can help track the distribution of supplies and monitor crowd movements in shelters or aid distribution centres. This technology ensures that resources are distributed efficiently and equitably, preventing bottlenecks and ensuring that aid reaches those most in need. Environmental Monitoring: Continuous environmental monitoring is vital for disaster preparedness and response. Computer vision systems can analyse satellite imagery to monitor natural phenomena such as hurricanes, earthquakes, and landslides. By detecting early signs of potential disasters, authorities can issue timely warnings and take preventive measures to mitigate the impact. Infrastructure Inspection and Maintenance: Computer vision aids in the inspection and maintenance of critical infrastructure. After a disaster, it is essential to assess the structural integrity of buildings, bridges, and other infrastructure quickly. Automated systems using computer vision can identify cracks, deformations, and other signs of damage, facilitating rapid and accurate assessments that are crucial for ensuring public safety and planning repairs.

'Computer vision technology is transforming disaster management by enabling real-time monitoring, enhancing search and rescue operations, and optimizing resource allocation—turning data into decisive action when every second counts.'- Disaster Technology Research Initiative

Disaster Prediction and Early Warning Systems: Integrating computer vision with predictive analytics enhances early warning systems. By analysing historical data and current conditions, these systems can predict the likelihood of events such as floods, landslides, and storms. Computer vision algorithms can process images and videos to identify patterns and anomalies that indicate impending disasters, allowing for early warnings and proactive measures.Automated Mapping and Geographic Information Systems (GIS): Computer vision technology is essential for creating accurate maps and GIS applications in disaster management. Aerial and satellite imagery processed with computer vision techniques can produce detailed maps that highlight affected areas, evacuation routes, and safe zones. These maps are invaluable for coordinating response efforts and guiding emergency services. Computer vision significantly enhances disaster management capabilities by providing real-time data, improving accuracy in assessments, and enabling efficient resource allocation. As technology continues to advance, the integration of computer vision in disaster management will further improve preparedness, response, and recovery efforts, ultimately saving lives and reducing the impact of natural and man-made disasters.

Applications to Disaster Management

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Challenges for Computer Vision in Disaster Management

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Challenges for Computer Vision in Disaster Management

Despite its transformative potential, the application of computer vision in disaster management faces several significant challenges: Data Quality and Availability: High-quality, up-to-date visual data is essential for effective computer vision applications. However, disasters often disrupt communication networks and power supplies, making it difficult to obtain reliable data. Additionally, adverse weather conditions and debris can obstruct clear imaging, complicating analysis efforts. Environmental Complexity: Disaster environments are highly variable and unpredictable, presenting complex scenarios for computer vision systems. Factors such as smoke, dust, water, and varying light conditions can hinder the accuracy and reliability of image processing and object detection algorithms. Adapting computer vision models to handle these diverse and challenging conditions remains a significant hurdle.

Challenges for Computer Vision in Disaster Management

Computational Requirements: Advanced computer vision algorithms, particularly those involving deep learning, require substantial computational power and resources. In disaster scenarios, deploying such systems in real-time and on-site can be challenging due to limited computational infrastructure and power availability. Ensuring that these systems can operate efficiently under constrained conditions is a critical challenge.

Integration with Other Systems: Computer vision systems must seamlessly integrate with other disaster management technologies, such as Geographic Information Systems (GIS), communication networks, and decision-support tools. Achieving interoperability and real-time data exchange between different systems can be technically complex and requires robust standards and protocols.False Positives and Negatives: The accuracy of computer vision systems is crucial in disaster management. False positives (incorrectly identifying hazards) and false negatives (failing to detect actual hazards) can lead to inappropriate responses and missed opportunities for timely intervention. Enhancing the precision and reliability of computer vision algorithms to minimise such errors is essential.

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Challenges for Computer Vision in Disaster Management

Ethical and Privacy Concerns:

The deployment of computer vision technologies raises ethical and privacy issues, particularly regarding the surveillance and monitoring of affected populations. Balancing the need for detailed visual data with respect for individuals' privacy rights and ensuring the ethical use of collected data is a significant challenge.

Cost and Accessibility:

Implementing advanced computer vision systems can be expensive, potentially limiting their accessibility to resource-constrained regions and organisations. Developing cost-effective solutions and ensuring that these technologies are accessible to all communities, regardless of their financial capabilities, is crucial for equitable disaster management.

R&D and Policy Recomenadation

Addressing these challenges requires ongoing research, innovation, and collaboration among technologists, disaster management professionals, and policymakers. By overcoming these obstacles, the full potential of computer vision in enhancing disaster resilience and response can be realised.

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Advantages of Disaster management with Computer vision

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Advantages of Disaster management with Computer vision

Faster & More Accurate Damage Assessment – Analyzes satellite and drone imagery to assess infrastructure damage quickly, aiding in resource allocation and recovery planning.Enhanced Search & Rescue Operations – Uses AI-powered image recognition and thermal imaging to locate survivors in disaster zones, even in low-visibility conditions. Efficient Resource Allocation – Tracks aid distribution and crowd movements to ensure equitable and timely delivery of emergency supplies. Improved Disaster Prediction & Early Warnings – Processes historical and real-time data to predict potential disasters, allowing authorities to issue timely alerts and evacuation plans. Automation & Reduced Human Risk – Automates critical tasks like structural inspections and hazard detection, reducing the need for human intervention in dangerous environments. Better Coordination & Decision-Making – Integrates with Geographic Information Systems (GIS) and other disaster response tools to enhance coordination between emergency teams.

Natural disasters

Over the last twenty years, more than 7,000 disasters were recorded worldwide. In the previous two decades, there have been more climate-related natural disasters: floods, storms, droughts, and wildfires. The number of extreme temperatures rose by 232% from 1980-99 compared to 2000-19. Disasters claimed more than 1.2 million lives and affected a total of over 4 billion people. Additionally, disasters led to approximately US$ 3 trillion in economic losses worldwide. However, the total number of natural disasters is not higher because more have been recorded in the past 20 years. Such events are registered by EM-DAT from ten dead or 100 people affected.

Artificial Intelligence for Natural Disaster Management

This Webinar will explore the main barriers to the adoption of these disruptive technologies in the area of disaster management and will also examine the integrated approaches related to machine learning, big data analytics, and AI for supporting the detection, forecasting, and communication of natural hazards and disasters.

Activities Show what you know!

EU policy update

Bibliografic research on Computer vision in disaster prediction

Economics for Disaster Prevention and Preparedness

EU Climate Action: Responding to the global emergency

EU Civil Protection Mechanism explained

Activity 1

Update on your EU natural disaster defence mechanisms

European Commission

How Europe helps in worldwide crises from natural disasters to armed conflicts

Activity 2

Mokayed, H., Quan, T. Z., Alkhaled, L., & Sivakumar, V. (2023). Real-time human detection and counting system using deep learning computer vision techniques. In Artificial Intelligence and Applications (Vol. 1, No. 4, pp. 205-213)..

Zhu, Y., & Li, N. (2021). Virtual and augmented reality technologies for emergency management in the built environments: A state-of-the-art review. Journal of safety science and resilience, 2(1), 1-10.

Li, W., & Hsu, C. Y. (2022). GeoAI for large-scale image analysis and machine vision: Recent progress of artificial intelligence in geography. ISPRS International Journal of Geo-Information, 11(7), 385.

Read the 3 papers

Assessment

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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Course completed!

Chao Fan, Cheng Zhang, Alex Yahja, Ali Mostafavi, Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management, International Journal of Information Management, Volume 56, 2021, 102049, ISSN 0268-4012,49.

https://doi.org/10.1016/j.ijinfomgt.2019.102049

Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management

Write a title here

Haven't we convinced you to use graphics in your creations yet? Well: there are studies that show that 50% of the brain is responsible for processing visual stimuli, and that it is retained 42% more when the content is animated.

Write a title here

Haven't we convinced you to use graphics in your creations yet? Well: there are studies that show that 50% of the brain is responsible for processing visual stimuli, and that this is retained 42% more when the content is animated.

Explore how computer vision assists in real-time monitoring, damage assessment, and disaster prediction.Instructions: Research the role of computer vision in disaster management. Read about the key applications and how computer vision helps in real-time monitoring, damage assessment, and resource distribution: Savio Rajan, K., Rajan, A.A., Waltin, S.M., Joseph, T., Anjali, C. (2022). Disaster Management Using Artificial Intelligence. In: Patgiri, R., Bandyopadhyay, S., Borah, M.D., Emilia Balas, V. (eds) Edge Analytics. Lecture Notes in Electrical Engineering, vol 869. Springer, Singapore. https://doi.org/10.1007/978-981-19-0019-8_21 Watch 2 videos: AI vs Disaster Response: Tackles Global Challenges-Episode 7: https://youtu.be/ePtZDmsC_78?si=mhA1gQT5XAwg0maT Predict, Rescue, Optimize: How AI is Leading Disaster Response Efforts : https://www.youtube.com/watch?v=0DSlHLZERXE How Drones Are Saving Lives in Disasters https://youtu.be/V-NDO7In7Fg?si=F51WrqZ54gT2gmwt

Understand the Key Applications of Computer Vision in Disaster Management

https://doi.org/10.1016/j.ijdrr.2023.103906

Deep learning and stereo vision based detection of post-earthquake fire geolocation for smart cities within the scope of disaster management: İstanbul case

Kustu, T., & Taskin, A. (2023). Deep learning and stereo vision based detection of post-earthquake fire geolocation for smart cities within the scope of disaster management: İstanbul case. International journal of disaster risk reduction, 96, 103906.

Write a title here

Haven't we convinced you to use graphics in your creations yet? Well: there are studies showing that 50% of the brain is responsible for processing visual stimuli, and that this is retained 42% more when the content is animated.

Sharma, K., Anand, D., Sabharwal, M., Tiwari, P. K., Cheikhrouhou, O., & Frikha, T. (2021). A disaster management framework using Internet of Things‐based interconnected devices. Mathematical Problems in Engineering, 2021(1), 9916440.

https://doi.org/10.1155/2021/9916440

A disaster management framework using Internet of Things‐based interconnected devices