Using Computer Vision to Better Understand Our World

Computer vision technology allows for the integration of data from various sources, providing a holistic and comprehensive understanding of ecosystems that enables informed decision making and policy development.

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What Are Digital Serious Games?

Digital serious games, also known simply as serious games, are interactive digital applications designed not just for entertainment but for educational, training, or informative purposes. These applications leverage game design principles to engage users and facilitate learning or skill development in a more interactive and engaging manner. Serious games can be used in various fields and industries to address specific objectives such as educational, training, or informative goals. Whether for education, professional training, or raising awareness, these games offer a dynamic platform for conveying complex concepts, simulating real-life scenarios, and fostering problem-solving abilities.Areas within IEEE where you can learn more about Computer Vision Technology:

Computer vision technology is a subfield of computer science and artificial intelligence (AI) where machines are designed to interpret and make decisions based on visual data. This technology enhances human capabilities in understanding and interpreting the visual world. Computer vision has the ability to process and analyze vast amounts of visual data in real time, which can be collected from various sources such as satellites, drones, and ground-based sensors. Real-time processing and analysis of visual data allow for immediate insights and actions, enabling more informed decisions that can contribute to reducing carbon emissions and aiding in general climate-change mitigation efforts by identifying trends, assessing impacts, and monitoring changes in the environment efficiently. 

Dr. Polat Goktas is a member of the IEEE Young Professionals Climate and Sustainability Task Force. He is currently a Senior AI Researcher at University College Dublin’s School of Computer Science and Ireland’s Centre for Applied Artificial Intelligence (CeADAR) where he integrates AI with life and environmental sciences to develop innovative solutions for real-world problems. 

Dr. Goktas answers a few questions on computer vision technology and its role in helping to better understand the environment while developing viable solutions to climate-change problems.

Question: How does computer vision technology contribute to monitoring and addressing environmental challenges such as deforestation [ 1 ] and monitoring pollution [ 2 ] ?

Dr. Goktas: The capability of technology to analyze an extensive dataset is crucial in addressing the fast-paced changes of environmental conservation and sustainability efforts. The scalability and precision of these technological tools are essential in combating environmental degradation and climate change.

In the context of deforestation, computer vision algorithms can process satellite imagery over time to monitor changes in forest coverage, detect illegal logging activities, or identify forest degradation. By comparing imagery from different time periods, these systems can pinpoint areas of deforestation with significant accuracy, allowing for timely intervention or reforestation efforts. Additionally, computer vision helps in assessing forest health by detecting disease outbreaks or pest infestations, thus facilitating rapid actions to preserve forest ecosystems.

Computer vision also plays a crucial role in climate change research by analyzing imagery across various timelines to observe indicators, such as air pollution spatiotemporal mapping [ 3 ] , melting of glaciers, sea-level fluctuations, variations in snow cover, the condition of coral reefs or flood occurrences. By tracking these indicators, scientists can better understand the impacts of climate change effects and formulate effective mitigation and adaptation strategies.

Question: What are some key advantages of using computer vision technology over traditional methods in environmental monitoring and conservation efforts?

Dr. Goktas: One advantage is the enhancement of accuracy and precision. By utilizing advanced algorithms, computer vision systems can identify and classify environmental features with greater precision, thereby minimizing the risk of human error. This improvement is particularly valuable in tasks such as counting animal populations, identifying species from camera trap images, or monitoring changes in forest coverage. 

Another significant advantage is the capability of long-term trend analysis, observing changes over time by comparing historical data with recent observations. This longitudinal analysis is crucial for understanding prolonged environmental changes such as deforestation rates or the recovery of ecosystems following conservation interventions. Moreover, computer vision technology allows for the integration of data from various sources, offering a holistic view of ecosystems. This comprehensive approach leads to a better informed decision making and more effective policy formulation in environmental management.

Question: What are digital serious games, and how do they complement computer vision technology in educating and engaging individuals in sustainability issues?

Dr. Goktas: Digital serious games [ 4 ]  [see sidebar] are education tools designed to facilitate the understanding of a given subject. By integrating the immersive and interactive features of digital serious games with the analytical power of computer vision technology, educators and developers can create compelling educational tools. For example, employing real environmental data to craft simulated scenarios, serious games powered by computer vision can help players to make informed decisions mirroring real-life conditions.

This approach underlines the importance of data in addressing and mitigating environmental challenges, thereby promoting critical thinking and problem-solving skills. Furthermore, by illustrating the real-world outcomes of various actions, these games can effectively demonstrate the tangible impact of both individual and collective decisions on the environment.

Question: In your opinion, what are some emerging trends or future directions for the integration of computer vision technology and sustainability initiatives?

Dr. Goktas: Computer vision technology holds the potential to transform sustainability and climate-change initiatives by offering solutions that are not only more effective and efficient than traditional methods but also have the ability to be more accessible for a global accessibility of engineers and policymakers. Key areas of innovation include advanced remote sensing for ecosystem monitoring [ 5 ] , AI-driven predictive analytics, automated biodiversity assessments [ 6 ] , enhanced pollution monitoring [ 7 ] , and smart agriculture practices [ 8 ] . Building on its strengths, computer vision technology provides significant advancements that contribute to addressing global sustainability issues. These innovations enable more precise monitoring, prediction, and management of environmental resources, leading to better-informed decisions and more sustainable outcomes.

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[1] G. P. Kumar, V. Harshini, S. Nithyakalyani and R. Sneha, “Tree Profiling and Data Analysis of Forest Canopy Cover Using Aerial Images,” 2023 2nd International Conference on Vision Towards Emerging Trends in Communication and Networking Technologies (ViTECoN), Vellore, India, 2023, pp. 1-6, doi: 10.1109/ViTECoN58111.2023.10157202.

[2]  J. Wu, C. O’Sullivan, F. Orlandi, D. O’Sullivan and S. Dev, “Measurement of Industrial Smoke Plumes from Satellite Images,” IGARSS 2023 – 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 2023, pp. 5680-5683, doi: 10.1109/IGARSS52108.2023.10282713.

[3] Goktas, P., Rakholia, R., Carbajo, R.S. (2024). “Investigating air pollution dynamics in Ho Chi Minh City: A spatiotemporal study leveraging XAI-SHAP clustering methodology.” In: Nowaczyk, S., et al. Artificial Intelligence. ECAI 2023 International Workshops. ECAI 2023. Communications in Computer and Information Science, vol. 1948. Springer, Cham.

[4] F. Archuby, C. Sanz and C. Manresa-Yee, “DIJS: Methodology for the Design and Development of Digital Educational Serious Games,” in IEEE Transactions on Games, vol. 15, no. 2, pp. 273-284, June 2023, doi: 10.1109/TG.2022.3217737.

[5] T. Yang, W. Xu and Y. Hua, “Application of Band Combination in Landslide Identification of Remote Sensing Images Driven by Deep Learning,” 2023 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), Chengdu, China, 2023, pp. 939-945, doi: 10.1109/ICICML60161.2023.10424877.

[6] F. Archuby, C. Sanz and C. Manresa-Yee, “DIJS: Methodology for the Design and Development of Digital Educational Serious Games,” in IEEE Transactions on Games, vol. 15, no. 2, pp. 273-284, June 2023, doi: 10.1109/TG.2022.3217737.

[7] D. Ather, N. Rashevskiy, D. Parygin, A. Gurtyakov and S. Katerinina, “Intelligent Assessment of the Visual Ecology of the Urban Environment,” 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS), Tashkent, Uzbekistan, 2022, pp. 361-366, doi: 10.1109/ICTACS56270.2022.9988692.

[8] R. TOMBE, “Computer Vision for Smart Farming and Sustainable Agriculture,” 2020 IST-Africa Conference (IST-Africa), Kampala, Uganda, 2020, pp. 1-8.