June 30, 2020

Geospatial Intelligence from Earth Observation

Today the United Office for Outer Space Affairs (UNOOSA) records over 2000 active satellites are orbiting Earth. Most are earmarked for other functions like communications, technology development, navigation and space science. 884 of these satellites are deployed for Earth observation.They are observing Earth and sending back diverse information from; biological, physical and even chemical data on our planet.

The interest in Earth observation started with the launch of Sputnik back in 1957 and grew exponentially in the last 20 years. Earth observation has since evolved to take advantage of other remote-sensing technologies to include extra dimensions to the spatial and temporal values collected, like hyperspectral images, creating big linked geospatial data.


A study published by Acta Astronautica, observes that while the initial players in Earth Observation (EO) were governments and large corporations, the field is witnessing an increase in the private sector actors. This is not only ramping up the volume and velocity of the data collection but, it has made EO data more accessible and affordable. 

10 years ago fewer active satellites were observing the earth. Terabytes of data were generated. Yet, we had no capacity to neither store nor analyse that volume of data. Using the right technology, institutions can now access the full data value-chain and mine gems from data embedded in earth observation products even with current greater volumes and higher velocity.

The increase in EO producers – satellite manufacturers and launchers – is in tandem with EO consumers – data processors and analysts. Establishments find newer use-cases for EO objects that are especially valuable in geospatial intelligence: from environmental analysis that informs policies to urban planning that leads to smarter cities and not to forget IoT tracking. 

Firms are especially investing significantly in big data analysis for business intelligence. Whether the intent is to reduce costs or save time, the outcome in speedy decision making leads to increased revenue.

Gisaia’s ARLAS® framework, an open-source technology, is developed to create high-value information for policymakers in the public and private sectors. Currently, it is deployed by France’s consortium of Earth Observation and Environmental sciences, Theia and also by Data-terra as Dinamis. They both use ARLAS technology for Earth observation cataloguing and analysis of Earth observation objects. 


If we look back, we see that the journey for the pioneers in Earth observation was not always easy. Billions of earth observation objects were left in the archive with little to no exploration due to the amount of time, and expertise that was required to sift through them.

Even with improved technology, many EO producers find it difficult to aggregate data from different sources for studies. Like, how do you know that you have chosen the right image to analyse? This alone, can be a tedious exercise that takes a lot of man-hours, sometimes, looking at millions of images with no certainty in results.

While some tools can now visualise the objects, often there isn’t an option to pre-visualise, allowing one to check if the image meets a criteria for the query like; cloud coverage or mission type. 

It is no wonder that most of these tasks are mostly relegated to data scientists and IT professionals within teams as they often require some knowledge in coding.



As the ARLAS® framework was initially conceived for earth observation, its development has evolved to absorb some prickly points in geospatial big data technologies’ usage. 

The main principle behind ARLAS is to make it accessible. One does not have to be a coder nor a data scientist to use it. This then cuts the amount of time spent on the technical details – setting up and filtering – to be used for instantaneous query of the data. 

One of the many resourceful features of ARLAS when conducting a search is, getting results to your query plus, suggestions close to your query. This gives you the option to quickly pick the right image.

There are several built-in time-saving configurations like the ability to save your filters if you use them often. Advance filters by detail like excluding images with cloud coverage or even sharing your URL with teammates to quickly view before making decisions.

If you work with data from different sources, ARLAS is also able to aggregate them under one platform on a user-friendly interface. Because it is open-source software, there is no vendor lock-in giving you; 

  • Freedom to adapt it
  • Freedom to extend it
  • Freedom to change it



There are numerous prospects in earth observation objects analysis and we have expertise in geospatial intelligence. If you have questions about how to combine the volume, velocity and variety from EO to create high-value information, get in touch with us at contact@gisaia.com