🌏 The Future of Protecting our Planet
AI enhances pollution monitoring through machine learning, big data, and technologies like drones, remote sensing, and noise pollution analysis.
Today's Highlights
- How AI is changing pollution monitoring
- This Week On BuzzBelow - a recap on this week's topics
- In Other News - a few interesting developments we're tracking
AI is playing a massive role in disrupting environmental monitoring, specifically pollution monitoring. By leveraging machine learning algorithms and big data analytics, AI facilitates a more nuanced understanding and management of environmental parameters. Here are some ways in which AI is changing how pollution is monitored:
Image Recognition & Remote Sensing
Pollutants are recognized and quantified through the analysis of satellite imagery and remote sensing data using Convolutional Neural Networks (CNNs) and other sophisticated image recognition techniques. These AI-driven methods have a high accuracy rate for detecting anomalies such as illegal dumping, deforestation, and oil spills. Stantec delivers precise and dependable data for environmental monitoring by utilizing remote sensing technologies such as satellite images, LiDAR, underwater acoustics, photogrammetry, and unmanned aerial systems (UAS).
Drones & Autonomous Robots
Drones and autonomous robots with onboard artificial intelligence processing units can operate in hazardous or unreachable places to gather data and monitor the environment. They send the data to centralized AI systems for examination, or they examine it onboard. Mapware leverages drone technology for environmental monitoring, showcasing the versatility of drones in collecting environmental data.
Noise Pollution Monitoring
To monitor noise pollution, AI algorithms can process and analyze sound data recorded by acoustic sensors. In urban and industrial environments, noise levels are identified, categorized, and measured using advanced signal processing techniques and machine learning algorithms. Sinay developed an Underwater Acoustics Module that utilizes AI for underwater noise pollution monitoring, which is essential to ocean health.
The World Health Organization (WHO) reported that 97% of cities with more than 100,000 residents in middle-income and low-income countries do not adhere to WHO air quality recommendations, highlighting the critical need for advanced monitoring and mitigation techniques like artificial intelligence. High-performance computing power and the advancement of machine learning techniques have created new opportunities for studying environmental pollution, particularly in monitoring and predicting the temporal and spatial changes in pollution during the past ten years. By driving accurate insights and fostering proactive measures, AI stands as a linchpin in the modern-day environmental conservation toolkit, paving the way for more robust and effective pollution monitoring and mitigation strategies.
This Week on BuzzBelow
Sukanya Ganesh
Sukanya Ganesh
In Other News
Yagnesh Pandya