ARLAS EO software for building robust Earth Observation Data Catalogues



   21 January 2022, Media Release

Increase value of EO data

Earth Observation (EO) data providers must facilitate consumption of data by making it easier for users to find what they want. 

If you have in place an Earth Observation products archive that is not working as envisioned, you may be thinking of updating it.  Or maybe, you are setting up a new one, and would like to do it correctly. EO data is constantly changing and a great catalogue must also adapt. Building a reliable and scalable display of your EO data is not easy but this is now possible thanks to big data technologies.

Using these technologies, Gisaïa developed ARLAS EO. A software solution to save you the time it takes to build a robust EO catalogue from scratch.

A great catalogue makes all perimeters from Earth observations accessible; satellite images, remote sensing data, measurements from ocean, land and atmospheric stations. ARLAS EO accelerates and guarantees the valorisation of all your EO data with a catalogue that scales with your archive.

67 years after “Sputnik 1” provided the first potential for meteoroid detection from orbit in 1957, no one knew where Earth’s observations would be. After all, it had taken over 70 years to put what Konstantin Tsiolkovsky had written in his book outlining how this could be put into action. Since Sputnik 1, more satellites have been launched into space, collecting and sharing valuable information about our planet. They are not the only ones. There are currently other data sources derived from ground-based, sea-borne, or air-borne monitoring systems, as well as geospatial reference or ancillary data. All these, combined, provide rich information that supports data-driven evidence on spatio-temporal patterns of human activities or natural processes.

Build a robust EO catalogue

But this data is growing vast and fast. It is big, complex and hard to use. We are now faced with big Earth data sets that can accelerate understanding, modelling, and predicting natural and physical processes. While it is great, it is also a challenge. Showcasing all these products in the same container is not easy. Catalogues assist and support users to make effective use of satellite imagery and space remote sensing derived products. But with the data volume growing by the minute, only a robust Earth Observation catalogue can scale with need and use.
ARLAS EO software for building robust Earth Observation Data Catalogues

Enhance EO product discovery

National bodies like space agencies and research centers have a quest to understand the past, current, and future states of the human and Earth systems. Business institutions too seek this information. They are looking for change detection, object identification and classification, and other patterns of life in the Earth Observation products and derived products. This supports policies, planning and execution activities. 

An enhanced filtering system is ideal to make sure that they are looking at the right product from the millions available. This not only saves time, it limits errors in basing one’s decision on the missing data. Users then make selections after looking at all possible options. It provides all users with optimal access to Earth Observation data and Geospatial information.

Facilitate EO products consumption

There has been more push to strengthen national and regional capacities in Earth products and derived products consumption. This has been followed up with open data policies like access to Sentinel 1 and Sentinel 2 data among others. In the Earth observation industry, open data is the downstream catalyst for value addition and innovation in the field. Many sectors currently benefit from insights provided by EO research: water management resources, sustainable urban development, public health surveillance, infrastructure and transport management, food security and sustainable agriculture, energy and mineral resources management, disaster resilience, biodiversity and ecosystem management, telecommunication and even insurance. All for resource tracing and management. 

But consumption of the products is not as fast and extensive as expected. EO product providers must facilitate consumption by making it easier for users to find what they want. Maybe even making it possible to discover what they need. 

Setting catalogue permissions to access based on usage could be an option to limit frustrations where users do not need to see everything. This can also be a great way to enhance security on how the data is used.

Increase value of EO data

While Earth Observation products already provide invaluable information, derived products and augmented products are key to drawing the real picture. A great catalogue takes in all perimeters from Earth observations — satellite images, remote sensing data, measurements from ocean, land and atmospheric stations and all other in situ measurements may be used to show real, near real-time, and historic weather, vegetation, water and population patterns. It facilitates data-intensive science.

Showcase all available EO data in one place

Having a clear window to what is available in your product catalogue will most definitely increase appreciation and promote value-added services. Value-added services are hailed as key enablers of the Earth Observation downstream science and market.providers’ market is growing, and competition, like in other sectors, will favour those who provide a positive user experience when; searching, discovering, and exploring available products. 

Now more than ever, catalogues have to be intuitive and responsive besides being informative.

Data-driven evidence

Earth Observation data – products and derived products are key in ensuring that we use data-driven evidence in sustainable management of the environment and resources, security, and to reveal sustainable business opportunities. This will further promote the adoption of Earth Observation products and derived products for other uses that we have yet to identify.


 About Gisaïa:

Gisaïa brings eight years of geo-big data analytics expertise, and a robust geo-analytics solution, “ARLAS to help accelerate EO data consumption. The ARLAS framework has been tried and tested in diverse use cases for geospatial intelligence, especially in Earth Observation data optimisation.

Arlas EO solution software for building Earth Observation Data catalogues

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  22 Octobre 2021, Media Release

Media release, 22 Octobre 2021

Our team joins others from the French space sectors to form a consortium that has been chosen by the Directorate General of Enterprises (DGE) and Centre National d’Études Spatiales for the DOMINO-X project.

We are proud and happy to contribute to this innovative initiative that will exploit Cloud and Artificial Intelligence technologies to standardize the architectures of ground-based Earth Observation segments and promote the emergence of a modular product and service offering. This will facilitate a need to offer customers lower costs for their requests.

Airbus Defence and Space are leading the DOMINO-X  consortium that brings together other experts from: Airbus, Thales Alenia Space, Safran, CS Group, Orange and Capgemini, Gisaïa, Stack Labs, Human design Group, Geotrend and Leanspace.

Gisaïa will be fronting our solution, ARLAS  for geospatial data analytics . This is not the first time that Gisaïa joins some of the consortium members and offers solutions that simplify the access to Earth Observation products. ARLAS is part of the framework that Theia and Dinamis are built on. 

ARLAS is open source architecture software that is also built on the latest big data technologies making it both easily interoperable with other solutions. It scales easily with a growing archive . This is important in the DOMINO-X mission which seeks to explore volumes of Earth Observation data, from diverse product families. But that is not all, ARLAS will also be offering interactive visualisation of the data with a simplified filtering process that makes it easy to quickly sort through data and find what one is looking for. 

The DOMINO-X ultimate quest is to deliver valuable insights for decision making from Earth Observation data by making it accessible to as many users as possible.

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 About Gisaïa:

Gisaïa brings seven years of geo-big data analytics expertise, and a robust geo-analytics solution, “ARLAS”. The ARLASframework has been tried and tested in diverse use cases for geospatial intelligence, especially in Earth observation data optimisation.


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