The Earth is changing, and with it comes the urgent need for innovative solutions to combat climate change. Enter geospatial AI or geoAI, a rapidly evolving field that promises to expand one’s understanding and response to environmental challenges.
What is Geospatial AI?
Geospatial AI combines the power of artificial intelligence (AI) with the vast resources of geospatial data that includes satellite imagery, weather patterns and land use maps. This fusion allows one to analyze environmental data at an unmatched scale, revealing hidden patterns and making informed decisions about resource management, disaster preparedness, and sustainable development.
It is a discipline that combines innovations in spatial science, AI methods in machine learning (like deep learning), data mining, and high-performance computing to extract knowledge from spatial big data.
How can Geospatial AI be used to address Climate Change?
Geospatial AI applications can be very useful in the fight against climate change. The applications are numerous and include AI models that can monitor and predict climate events by analyzing satellite images and weather data to track deforestation, predict floods and wildfires, and monitor the health of ecosystems. This information is vital for mitigation strategies and early warning systems. Geospatial AI helps in optimizing renewable energy by identifying the most suitable locations for solar panels, wind farms, and other renewable energy sources, ensuring efficient deployment and maximizing output.
AI can help in improving agricultural practices by analyzing land use patterns and soil conditions to recommend optimal crop rotation strategies, reduce water usage, and boost agricultural yields, promoting sustainable food production. Geospatial AI models can support climate adaptation by predicting the impact of rising sea levels, extreme weather events, and other climate-related changes on different regions, enabling communities and governments to develop effective adaptation plans.
Many companies have taken it upon themselves to spearhead the development of geospatial AI solutions for climate change. The important ones are listed below:
IBM & NASA
On January 2023, IBM and NASA Launched an open-source Geospatial AI Foundation Model. This model demonstrates a 15% improvement over existing techniques in analyzing satellite images for tasks like flood and burn scar mapping. It showcases the potential of AI in boosting scientific discovery and Earth observation applications. Within months, they revealed their progress on a new AI model specifically focused on weather and climate analysis, complementing their existing geospatial foundational model.
The largest geospatial AI foundation model, trained on massive Earth observation data from NASA, was released as open-source on Hugging Face. This initiative aims to facilitate collaboration and accelerate research in various fields like disaster response, environmental monitoring, and agriculture.
IBM in UAE, Kenya & UK
Last week IBM announced new applications of its geospatial AI technologies, including the foundation model developed with NASA, to address climate challenges in various regions. This includes analyzing urban heat islands in the UAE with Mohamed Bin Zayed University of Artificial Intelligence to map urban heat islands in the United Arab Emirates and develop solutions to mitigate their effects. An urban heat island occurs when a city experiences much warmer temperatures than nearby rural areas. The difference in temperature between urban and less-developed rural areas has to do with how well the surfaces in each environment absorb and hold heat.
Another fascinating application includes enhancing climate resilience in the UK by Collaborating with the UK’s Science and Technology Facilities Council (STFC) Hartree Centre to use geospatial AI to assess the risks of climate change and develop adaptation strategies and supporting reforestation efforts in Kenya.
Microsoft and Esri launch Geospatial AI on Azure
The partnership with ESRI brings together Esri’s ArcGIS platform with Microsoft’s Azure cloud platform to provide a comprehensive suite of tools for gathering, managing, analyzing, and predicting from geospatial data. This collaboration aims to make it easier for organizations to leverage the power of AI for geospatial applications.
Microsoft’s Azure AI platform includes geospatial capabilities like the Planetary Computer, which offers access to petabytes of Earth observation data for environmental monitoring and analysis while Esri’s ArcGIS platform offers a comprehensive suite of geospatial tools for visualizing, analyzing, and managing environmental data, enabling users to make informed decisions about sustainability initiatives.
Google for Climate Resilience
Google Earth Engine provides access to a vast collection of satellite images and geospatial data, allowing researchers and organizations to analyze environmental trends and develop data-driven solutions. Google Cloud’s Geospatial AI solutions help organizations prepare for the impacts of climate change by providing insights into environmental trends and risks.
Google Cloud offers a range of geospatial cloud products and solutions that can be used for a variety of purposes, such as retail site selection and cell tower optimization.
One such product is the Geospatial Creator powered by ARCore and Google Maps Platform. This new platform is used by developers to create innovative AR experiences, such as virtual tours and educational simulations.
The ARCore Geospatial API enables you to remotely attach content to any area covered by Google Street View and create AR experiences on a global scale. It uses device sensor and GPS data to detect the device’s environment, then matches the recognizable parts of that environment to a localization model provided by Google’s Visual Positioning System (VPS) to determine the precise location of a user’s device. Another Geospatial AI platform is the Atlas AI platform which is built on Google Cloud’s BigQuery. This platform helps organizations anticipate changing societal conditions and make better decisions about investments.
Geospatial AI holds immense potential for tackling the complex challenges of climate change. As the technology continues to evolve and becomes more accessible with innovative applications expected to emerge soon several key challenges need to be addressed before GeoAI can reach its full potential.
One challenge is data availability & access acquiring and accessing high-quality geospatial data can be difficult and expensive. This is often due to proprietary data formats, privacy concerns, and regulatory restrictions. Integrating and analyzing this diverse data can be a complex task with Data in Heterogeneous form.
Another limitation of AI is that AI algorithms can inherit and amplify biases from the data they are trained on. This can lead to discriminatory outcomes.
Despite these challenges, with continued investment and collaboration, GeoAI has the potential to create a more sustainable, resilient, and equitable future.
