AI, Google Earth Engine, and the future of sustainability data

AI takes on climate change

AI is quickly transforming nearly every industry and human endeavor. It’s no surprise, then, that AI sustainability efforts aimed at confronting climate change, developing renewable energy, and driving positive environmental outcomes are on the rise. We’re beginning to grasp innovative new opportunities for applying AI technology to sustainability data to ensure a greener future.

As artificial intelligence and machine learning technologies–especially ones with large language models such as Gemini (formerly Bard and Duet AI)–have dominated the headlines, it’s sometimes difficult to understand what the landscape looks like and where we might be able to apply these technologies in a practical sense. One area that deserves more attention is how AI technologies might be able to make progress toward global sustainability goals. 

The environment is full of data

Data has always been at the core of technology and sustainability efforts. This includes using geospatial data to measure the environmental impact of various human activities so that we can accurately course correct and predict the environmental impact of reducing greenhouse gas emissions, establishing circular economies, and adopting autonomous vehicles, among other solutions.

Google and DeepMind have been working to take the data generated from Google data centers to leverage machine learning and AI to run the data centers more efficiently and optimize energy use. This use of data has been in effect at least since 2016; today’s innovations point to even more compelling solutions related to data storage, low carbon energy systems, big data, and beyond.

Google tools, aligned with sustainability goals

Recently, Google unveiled a series of initiatives, powered by AI, to help promote sustainability initiatives around the globe. Notably, Google partnered with cities, including Seattle, Jakarta, Hamburg, and Rio de Janeiro, to optimize traffic patterns and reduce wait times at stoplights, both of which can lead to a sharp reduction in carbon dioxide emissions.

Other recent use cases that Google shared this month include fuel-efficient vehicle routing and using AI to predict flooding patterns, track wildfire spread, and share insights to mitigate extreme heat in cities around the globe.

These initiatives dovetail with existing AI applications, which can be used to analyze data and make operations more efficient across industries, from agriculture to waste management to delivery route efficiency, empowering operations to consume less.

AI, deeply integrated with ecology

Another interesting application of AI in the sustainability space is the technology’s use in wildlife management and conservation efforts. Using AI models for audio entity recognition is often regarded in the context of language and speech recognition, but these can also be used in non-human contexts.

Amazingly, AI models can be trained on bird and land animal calls to track animal populations, movement patterns, and identify threat patterns that are human-made and otherwise. AI that is integrated with remote sensing technology that monitors the air, soil, and water can also contribute to a deeper understanding of the health of ecosystems, and even feed this data into cloud-based platforms that visualize this data in the form of digital twins.

Scientists, researchers, and those at the front lines of conservation efforts will increasingly be assisted by AI models operating in large-scale interconnected databases, resulting in a more comprehensive understanding of the health of the planet.

Remote sensing, wildlife migration, and Google Earth Engine

Combining this with predictive analytics on weather and climate change in the long term allows us to have a more comprehensive picture of ongoing threats to wildlife. The advancements in remote sensing have also allowed us to incorporate technologies such as Google Earth Engine to see how raster and imagery data can augment the insights gained from more traditional datasets.

Previously, being able to analyze this to get a holistic view required disparate systems and a myriad of engineering challenges, but new advancements in AI and cloud computing allow us to process, and run predictions on ever-growing populations of data than we used to before, at continuously lowering costs.

The technology aspect is also interesting as we look at the macro trends in cloud computing. We are seeing the move from point data processing solutions to fully integrated data platforms that also incorporate a wide range of machine learning and AI capabilities.

Lowering the barrier to entry to achieve sustainability goals

AI lets us leverage more advanced computing capabilities to solve problems, while requiring fewer skills (such as the ability to code) to achieve goals that previously would have been far more laborious. The barrier to entry is being lowered every day, allowing for more users with new ideas to solve problems in the sustainability space.

What’s important to keep in mind as both the sustainability and AI communities continue to make leaps forward is to make sure we are cognizant of the potential for bias and discrimination and to ensure that these tools are used responsibly, equitably, and ethically.

While the discussion around training biases is far from over, it is very active and has wide-ranging implications. Data selection and training are typically human-driven processes, making awareness of potential biases and the ability to adjust to them of paramount importance

The environmental impact and energy consumption of AI

On the deployment side, it’s also essential to put these things in production, understanding that the digital divide is very real. There are socioeconomic and racial divides in sustainability as well. Ensuring equity in solutions is vital, as these problems impact us all.

The other variable in this is the power consumption of AI. Training advanced models can require massive computational resources, driving spikes in energy consumption and raising the environmental cost of using these tools to begin with.

As more users adopt AI tools, that can mean that our collective power consumption can increase as well. We must be able to balance the value and economic growth of AI with energy efficiency.

The impact of AI on sustainability efforts promises to be profound. AI represents an exciting intersection of technology and its application that the potential to benefit the world. It’s definitely a technology to keep an eye on.

SADA’s AI sustainability services

As a six-time Google Cloud Partner of the Year offering services in Artificial Intelligence, Location Intelligence, and Data, SADA has begun to cross-pollinate these domains as customers approach us with novel solutions and ambitious proposals.

SADA’s AI team is diving deep into Google AI products including Gemini, Duet AI, and Vertex, and consulting with companies representing a wide array of industries on custom AI solutions. New integrations of Duet AI into Google Workspace are just the beginning of the ways AI will continue to drive business value and inform decision making across enterprises.

We’re seeing tremendous opportunities for innovation at the intersection of location intelligence solutions and generative AI. Think artificial intelligence further enhancing Google Maps.

By pushing the capabilities of Google Earth Engine with machine learning, natural language processing, and the accelerating power of AI models, we anticipate AI advances in smart cities, reducing emissions, and other cases where sustainable AI will rise to the key challenges of our era.

What most excites you about AI adoption for a sustainable future? Don’t hesitate to contact us to schedule a consultation with SADA AI experts to discuss what you have in mind and start planning on how to make it happen. And for even more insights on how to leverage AI to help you achieve your business goals, download the guide, The 4 Most Common AI Adoption Challenges and How to Overcome Them.