Generative AI is taking the world by storm. Exemplified by recently released models such as Stable Diffusion and ChatGPT, generative AI has become a viral theme among technologists, investors, policymakers and the general public. These advancements in generative AI have opened up new possibilities for various sectors, including the geospatial domain, highlighting its potential to transform industries. Discussions about “GeoGPT” modelled around ChatGPT, specialising in geospatial data, too, have already begun. It is thus not a far-fetched idea to access real-time information about planetary and climate systems with a level of accessibility similar to that of weather reports on mobile devices.

Read our blog to learn more about AI and how it enables climate action.

Convergence of AI and Geospatial Data

AI-based GIS solutions are already driving positive change in the field by providing new capabilities and improving the accuracy and efficiency of data processing and analysis.

Geospatial data collection and analysis has historically been time-consuming and error-prone, involving countless hours of land surveying and record research. The sheer volume of data, combined with human biases can lead to faulty evaluations that put people or critical infrastructure at risk. However, artificial intelligence is ushering in a new era of efficiency and accuracy in this field.

Today, advances in artificial intelligence and machine learning have enabled the development of algorithms that can automatically identify patterns and relationships within geospatial data, allowing for faster and more accurate analysis. With the help of satellite data, AI algorithms are providing historical, real-time and predictive insights on various climate and environmental parameters. Whether it is:

These insights are being generated in a shorter time frame and at lower costs. At present, there are several companies leveraging AI, cloud computing, and satellite technology to provide climate intelligence solutions which are revolutionising climate risk assessments.

Envisaging What the Future Holds

The combination of generative AI and geospatial technology thus offers a potent mix of capabilities and holds great promise to unlock new products and solutions. Inspired by ChatGPT, it may be possible to create a language model that is specifically designed for geospatial information.

Unlike chatGPT, which has updated information till its cutoff date, GeoGPT/GeoQA needs to be more real-time to serve its purpose. To build a system like this, it would be necessary to train a GPT model on a large corpus of geospatial data related to weather and climate. This might include historical climate data, real-time weather sensor readings, and other relevant information such as satellite imagery, land use data, and population density data.

In addition to training the model on this data, it would also be necessary to connect the model to real-time weather and climate datasets so that it can provide up-to-date information in response to user queries. This could be done using APIs or other data sources that provide access to current weather and climate data. By doing so, it may be possible to generate responses to questions and prompts such as:

Climate change is one of the biggest threats to our planet, and understanding the impacts of human activities on the environment is critical for addressing this issue.

However, climate and environmental data can be complex and difficult to understand, particularly for non-experts. This is where the concept of linking conversational-like prompts with climate and environmental data comes in, providing a more accessible and user-friendly way for people to explore and understand the data.

By using conversational-like prompts, users can ask questions in a more natural and conversational way, without needing to know specific technical terms or jargon. For example, instead of searching for "PM2.5 concentration in Los Angeles on June 1st, 2022", a user could simply ask "What was the air quality like in Los Angeles on June 1st, 2022?" The system could then use natural language processing algorithms to understand the intent behind the question and provide relevant data from a database of climate and environmental data.

Linking prompts with API parameters can make the system simpler to build and maintain, as it can enable users to easily access relevant data without needing to know complex query language. Users can simply ask questions in natural language, and the system can use these prompts to retrieve the relevant data from the database. This can make the system more accessible to a wider range of users, including those unfamiliar with technical jargon or database querying.

In addition, summarization can be used to condense large amounts of data into a brief summary that can be easily understood by the user. For example, an AI-powered system could analyze air quality data from Los Angeles on June 1st, 2022, and summarize the results in a sentence such as "The air quality in Los Angeles on June 1st, 2022 was moderate, with PM2.5 levels reaching a peak in the afternoon." This can help users quickly understand the key insights without being overwhelmed by too much information.

Furthermore, AI technology can be used to enhance the existing features of climate and environmental data systems. Machine learning algorithms can identify patterns and relationships in the data, and provide more detailed and accurate predictions. This can help users to better understand the impacts of climate change on their local environment, and make informed decisions to reduce their own carbon footprint.

Concluding thoughts

The concept of linking conversational-like prompts with climate and environmental data has the potential to greatly enhance the user experience and provide more accurate and relevant information. By leveraging AI technology, this system can be made even more powerful, enabling users to better understand the impacts of human activities on the environment and take action to mitigate climate change.

The pace of technological innovations has been truly remarkable, and it's exciting to think about what the future holds. With the potential for a language model specifically designed for geospatial information, we may be on the cusp of a revolution in the field. As we continue to innovate and push the boundaries of what is possible, it's important to remember that the only limitations in this regard will be our ideas.