Initially, OE has been driven by public sector contributions, especially those motivated by defence and security interests, but a commercial industry has established itself with a strong growth trend.
Earth observation can be a valuable tool for improving business performance by enabling data-driven decisions that help minimise risk or boost operational efficiency in a natural environment fraught with uncertainties due to climate change, which could be subject to enormous changes.
Technology can also contribute to improving the transparency and information sharing of organisations, governments and institutions in accountability processes. More than 50% of essential climate variables can only be measured at scale from space, making EO a key factor in meeting ESG (environment, social and corporate governance) requirements in multinational organisations whose activities span multiple geographies.
Earth observation (EO) can significantly improve logistics management by providing data on transport routes, weather patterns and resource availability.
EO data can also be used to drive revenue growth for some companies that produce it and have not exploited it before. New business models emerge, for example in the airline industry, by integrating it into products and services to create new and innovative offerings.
It has been shown that fertiliser inputs can be reduced by 4% to 6% overall when using this data in precision agriculture. In the field of energy, they can assess the energy potential of new solar, wind and hydroelectric sites, as well as vulnerabilities in large-scale infrastructure such as pipelines and power grids.
Despite this sea of opportunity for business, today, public spending accounts for almost three quarters of the market for EO data and services, while a good percentage of commercial demand remains «dormant».
Geospatial technologies have been widely applied to address, for example, urban and environmental challenges in cities, as they provide various levels of detail, temporal and spatial. - By mid-2025, none of the leading AI models, such as ChatGPT, Llama or Claude, had announced the use of Earth data in their training or were able to understand it. Emerging vision models based on transformers for geospatial data, also called geospatial-based models (GeoFM), can introduce a powerful new alternative to bridge this gap.