If you’re reading this, you’re probably all too aware of the many environmental challenges that the world is facing. At Temboo, we believe that innovative technologies can be a valuable part of the toolkit that helps us tackle these complex issues. The most interesting technology to come along in recent years is artificial intelligence (AI), which has the potential to greatly improve the way we approach and manage environmental projects. In this post we’re going to take a look at what is expected from AI in the environmental realm, and how AI technologies are being applied on real projects today.
AI, with its ability to process vast amounts of data, identify patterns, and make intelligent predictions, is making an impact across pretty much every aspect of our lives right now. Maybe you’ve played with conversational chatbots like ChatGPT, which can do everything from write essays and hold philosophical conversations, to creating travel itineraries and composing emails? Or maybe you’ve seen the amazingly and sometimes disconcertingly realistic images generated by Midjourney?
Away from the mainstream, in healthcare, AI is enhancing diagnostic accuracy, personalized treatments, and drug discovery. In transportation, self-driving cars and traffic management systems are finally becoming a reality, promising safer and more efficient journeys. AI-powered virtual assistants and smart home devices are starting to work in ways that we actually find useful instead of annoying, while e-commerce platforms are using AI algorithms behind the scenes for better personalized recommendations that feel natural and helpful. Additionally, AI is advancing cybersecurity measures, improving weather forecasting models, optimizing energy consumption, and driving breakthroughs in fields such as finance, entertainment, and education.
AI is being applied across the board in ways that are genuinely making existing processes better and helping drive innovations that push the boundaries of what we previously thought was possible. Let’s take a look how AI can be applied to environmental challenges.
What is Environmental AI?
Environmental AI is simply the application of existing and established AI-driven technologies to environmental problems. About five years ago, Temboo built some AI-driven features so that our customers could not just collect data, but also predict future data. We also trained models to detect anomalies in datasets in real-time and automatically notify customers if something out of the ordinary was happening. That was the environmental AI of its time, driven by the capabilities of the AI tools and technologies of the late 2010s. Now that the underlying technologies are advancing so rapidly, a lot more than predictions and anomalies are possible.
The Potential of Environmental AI
AI can be applied to environmental challenges in many ways. Once you understand the core capabilities of the latest AI-driven technologies, you’ll notice that the same set of central ideas (data analysis and pattern recognition) can be applied in many different contexts to help produce meaningful outcomes. Here’s the main areas in which AI is expected to drive innovation in addressing environmental issues over the next few years.
- Environmental Monitoring: AI can analyze large amounts of data from satellites, sensors, and other IoT devices to monitor environmental indicators such as air quality, water quality, deforestation, and wildlife habitats, providing real-time insights and early detection of environmental threats.
- Wildlife Conservation: AI can aid in species identification and tracking, helping to protect endangered animals by monitoring their populations, migration patterns, and behavior. AI-enabled systems can also detect and prevent wildlife poaching and illegal logging activities.
- Sustainable Agriculture: AI can optimize farming practices by analyzing soil data, weather patterns, and crop characteristics to improve irrigation efficiency, minimize pesticide use, and maximize crop yields. AI can also assist in crop disease detection and suggest appropriate remedies.
- Climate Change Analysis: AI can analyze climate models and historical data to predict and understand the impacts of climate change, such as sea-level rise, extreme weather events, and shifts in ecosystems. This information can guide policymakers in developing effective mitigation and adaptation strategies.
- Energy Efficiency: AI algorithms can optimize energy consumption in buildings, industries, and transportation systems by analyzing energy usage patterns, identifying inefficiencies, and recommending energy-saving measures, thereby reducing carbon emissions and promoting sustainability.
- Waste Management: AI can improve waste sorting and recycling processes by using computer vision to identify and sort different types of waste materials. AI can also analyze patterns and predict waste generation, helping optimize waste management logistics and reduce landfill usage.
- Environmental Planning: AI can assist urban planners and policymakers in making informed decisions by simulating and predicting the environmental impacts of infrastructure projects, land-use changes, and transportation systems, ensuring sustainable development and minimizing ecological disruptions.
- Natural Disaster Management: AI can analyze data from various sources, including satellite imagery, social media, and sensor networks, to provide early warning systems, assess damage, and aid in disaster response and recovery efforts.
By leveraging the power of AI, we can more easily gain deeper insights that help us make better decisions, ultimately fostering a more sustainable and resilient future.
The Current State of Environmental AI
Now that we have a good sense of where environmental AI is going, let’s take a look at where it is right now so that we can understand the gaps that need to be filled before the technology can reach its full potential.
Much of the work to date in environmental AI has been conducted within an academic setting, though fully realized products are starting to emerge now. Here’s some of the best projects, reports, and articles that we’ve seen recently that explore how emerging AI technologies can be applied to environmental issues.
- Machine Learning: New Ideas and Tools in Environmental Science and Engineering — this paper serves as a very good introduction to the state of the art in applying machine learning (AI) techniques to environmental science problems. Specifically it covers opportunities and possibilities in making predictions, extracting feature importance, detecting anomalies, and discovering new materials or chemicals.
- Chesapeake Bay: AI used for wetlands preservation — researchers used satellite images and AI to create maps that predicted the contours of wetlands with 94% accuracy. This included correcting maps that hadn’t been updated in decades, and also developing maps that show where wetlands used to be. This will make it easier to avoid accidentally paving over wetlands during future development.
- Clean Water AI — a Microsoft project, Clean Water AI uses a deep learning neural network to detect dangerous bacteria and harmful particles in water. Drinking water can be seen at a microscopic level with real-time detection. They have trained a model to detect poor water quality, and deploy the model on IoT edge devices to do monitoring on location in real time.
- AI Is Essential for Solving the Climate Crisis — this is a very good overview of the results of a survey into the role of AI in addressing climate change. The general areas in which AI can help are broken down into Mitigation, Adaptation & Resilience, and Fundamentals. This is a good way to guide your thinking into how AI can be integrated into the specific type of environmental work that you care about.
- First AI-based forest growth model deployed at a global scale — this project aims to accurately capture the carbon accumulation rates of forested ecosystems across the world, which is more challenging than mapping carbon emissions from forest loss. The model combines information collected about billions of trees measured on-site with satellite and other geospatial data to map local forest growth rates throughout the global forest range.
- Terrain-AI — Terrain-AI is exploring innovative AI and computational methodologies for collecting, analyzing and modeling data in order to develop a better understanding of greenhouse gas (GHG) exchange across farms, forests, peatlands and urban spaces. These improved GHG monitoring methodologies and standards will be adapted & scaled for other regions and countries across the globe.
- PureFlyt flight management system — AI is being used to reduce delays in the air and on the ground, meaning less fuel consumed and a reduced carbon footprint for the airline industry. AI is also being used to optimize aircraft trajectories with the objective of a 10% reduction in aircraft CO2 emissions by 2023. Additionally, research is being done on how to use AI-assisted trajectories to reduce the environmental impact of aircraft contrails by having planes fly at a slightly lower altitude, as well as to have planes make a continuous descent to landing, as opposed to descending by levels.
- FarmRoad — FarmRoad gives growers farm-and crop-specific insights and recommendations help improve their growing decisions, yield, consistency, and quality, for more sustainable farming. The team behind this technology also partner with seed producers to utilize the FarmRoad platform and FarmRoad’s crop-contextual AI for seed efficacy and quality.
- Anti-Poaching AI — Rouxcel Technology have developed AI-enabled bracelets for rhinos to help conservation teams easily locate the animals and monitor their behavior in real time. The devices are being used at different sites from Kenya down to South Africa. Their algorithm alerts wildlife operations centers and anti-poaching teams when animals are exhibiting abnormal movement patterns, such as distress from when poachers are close.
If you’re interested in using innovative emerging technologies to help your community address environmental challenges, please contact us to learn how Temboo can help you!