Screenshot of ARLAS.IO with Copernicus weather data and turtle tracking data from Argonautica, CLS.



   20, October 2022,  by Quentin Martin-Cocher

we NEED ACCURATE INFORMATION To preserve endangered species

As floods, forest fires and heat waves become more severe and frequent each year, the evidence of climate change is not a subject up for debate. Thanks to satellite telemetry and Earth Observation, we have more accurate knowledge of the potential of climate evolution.  Apart from air pollution, a projected rise of the air temperature of up to 7°C at the end of the century, many species are endangered as they cannot adapt to such quick changes.

But to preserve endangered species, we need to have as much knowledge as possible on their behaviours. In the past, our team explored birds’ migration using storks tracking data from Max Planck Institute. Another symbol of wildlife preservation is the marine turtle. There are 7 species on Earth, but only 6 of them are classified as endangered: the flatback turtle is not classified as sufficient data has not yet been gathered.

Thanks to wildlife monitoring programmes such as Argos, specialists have precise data in their hands to study species’ behaviour.

As we are not specialists of marine wildlife, we will use scientific literature results, to explore the impact of climate conditions on loggerhead turtles movements. To do so, we need to cross-explore two large data sets, which brings challenges in terms of scalability and multiple collection exploration. We will also see how ARLAS helps us solve those issues.for



To carry out this study, we will observe the movements of 54 loggerhead turtles in three main areas: the Atlantic Ocean, the Mediterranean Sea, as well as the Indian Ocean. Thanks to Copernicus’ Marine Resources platform, we will explore four climate parameters in the areas neighbouring the turtles’ trails:

    • Concentration of chlorophyll in the water;
    • Sea Level Anomaly (SLA);
    • Surface marine currents;
    • Sea Surface Temperature (SST);

For decades, the amount of data that exists in the world has been growing exponentially, to reach a projected 175 Zetabyte in 2025. To clarify, a Zetabyte is a number with 21 zeros, but it still is hard to grasp the magnitude of such big numbers. To compare with something that is easily representable, it is estimated that the oceans of Earth contain a bit over 1 Zeta-litre of water. In order to process even a slice of this data, data exploration solutions need to be scalable and withstand heavy queries.

To explore the impact of climate conditions on loggerhead turtles’ movements, we used two datasets : turtle positions from the Argos beacon of CLS and climate data from Copernicus’ Marine Resources platform.

Unfortunately, due to the large geographical and temporal extent of our loggerhead turtles’ data, one Copernicus dataset does not suffice. To produce the final data set that we used for our study, multiple sources had to be aligned and merged. Once aligned on a common grid, the representation with ARLAS’s Network Analytics layers proved to be an effective way of displaying this raster data.

In the oceans, the bottom of the food pyramid is held by phytoplanktons. Their green colour is due to the chlorophyll they contain and produce: thanks to satellite image, it is possible to infer the concentration of chlorophyll in the water and thus how rich in nutrients the water can be for the higher tiers of the food pyramid.

In areas with high concentrations, turtles are seen deviating from the migration path and extending their trails to forage in those waters, as can be observed below.

Screenshot of ARLAS.IO with Copernicus weather data and turtle tracking data from Argonautica, CLS.

The Sea Level Anomaly is the local difference from the long-term average sea level. It can indicate unusual values for other climate conditions such as water temperatures, salinities, average monthly winds, atmospheric pressures, or coastal currents. Around these occur eddies that circle around those anomalies and induce a pumping phenomenon that will pull up nutrient-rich deeper waters that will also constitute interesting foraging grounds for turtles.

In the example below, the turtle Gloria can be seen bypassing the different anomalies that are denoted with deep red and blue colours.

Screenshot of ARLAS.IO with Copernicus weather data and turtle tracking data from Argonautica, CLS.

Marine currents are crucial when it comes to our oceans’ health: they help control the climate by carrying warm water from the Equator to the poles, as well as help marine wildlife. Currents carry nutrients from colder polar waters to more temperate areas, in addition to carrying spawns, younglings and adults towards their destination.

In the case of loggerhead turtles, adjuvant marine currents can help them accelerate, as seen below. As currents become stronger in the turtle Tina’s trail direction, she gains noticeable traction as shown with her trail’s colour pulling towards redder shades.

Screenshot of ARLAS.IO with Copernicus weather data and turtle tracking data from Argonautica, CLS.


Sea turtles are cold-blooded reptiles that depend on their environment to regulate their body temperature. Due to that, they tend to live in waters between 13 and 28°C. Below 13°C their metabolism slows down and can reach a cold-stunned state where they are unable to move. At high temperatures, their metabolism accelerates. Temperature also plays a core part in the sex-balance of the species: the warmer the nesting beach’s sand temperature, the more female there will be.

With the warming of our climate, it is estimated that the coming decades will see an imbalance in the population which can have a harmful impact on sea turtles’ genetic diversity. Being able to monitor the temperature of nesting beaches as well as of the turtles’ environment can help us protect them better.

With ARLAS-Explo, we can study the evolution of the water’s temperature across multiple egg-laying seasons (June to August in the Mediterranean Sea). Over longer time periods, we could observe whether there is a change in their behaviours or habitats that could occur due to the rise of sea temperatures.

In the following ARLAS dashboard view, the turtles’ positions are represented during their egg-laying season in the Mediterranean Sea. As shown in the first graphs, most of the temperature variations are due to the evolution of the temperature across this period. However, the swimlanes in the second set of graphs could help us track along the years the variation in temperature. Here, they are limited to around less than a degree in average, with variations that can be linked to average temperature records.


Screenshot of ARLAS.IO with Copernicus weather data and turtle tracking data from Argonautica, CLS.


All efforts towards wildlife monitoring are expected to help gain more knowledge on their behaviours as well as protect them better.

If you are working in wildlife observation, monitoring or studies, check out our demo to get a glimpse of ARLAS at work on loggerhead turtles and climate data.

ARLAS Explo offers a robust framework to help accelerate development of geoanalytics platforms of diverse use-cases.


Gisaia Presents ARLAS-EO at Eureka Meets the Atlantic Rio de Janeiro

"Eureka Meets the Atlantic"

EUREKA MEETS THE ATLANTIC focuses on the promotion of entities which bring about disruptive innovation aligned with the sustainable development goals.

March 29, 2022

Massive Earth Observation (EO) data that is not fully harnessed

The Earth Observation (EO) industry was set off with the upstream sector with deployment of satellites. The numerous constellations that have been orbiting the Earth to send back products continue to expand. This is great news for data generation because, the more diverse and massive the data, the higher the likelihood to get valuable insights.

But most of the EO data generated is not fully harnessed.
This challenge is posed by the massive nature of the EO data and the technical capacity to easily distribute it.

There are numerous societal benefits from Earth observation data; change detection, situational analysis and predictions. The faster more people can access the data, the easier it will be to put it to use. Gisaïa leverages its expertise in geo big data analytics to accelerate the promotion and valorisation of EO data.

Eureka Meets the Atlantic for wider Copernicus data applications

The “Eureka meets the Atlantic” seeks to advance the European Commission agreement to promote the use of Copernicus images and products generated, for positive impact in various sectors, such as agriculture, forestry, resource optimisation or infrastructure management.

At Gisaïa we have shown diverse use of Copernicus data using our ARLAS framework, a solution that facilitates rapid development of geoanalytics platforms for various use cases.

So, when the Portugal Space Agency put out a call for applications for Startup and SMEs to be part of the entourage going to Rio de Janeiro for the third round of Eureka Meets the Atlantic, we were excited to apply and be chosen among the ten companies on this mission. We will be among Spotlite, Agroinsider, Eyecon Group, Bold Robotics and Spin Works (Portugal), Sentinel Hub (Austria), World From Space (Czech Republic), Lelieur BV (Belgium) and Terradue (Italy) and Gisaia from France.

We use this opportunity to advance our goal to make the massive EO data, including Copernicus data; reachable, exporable and valued. The third Eureka meets the Atlantic will focus on how innovation based on Earth Observation (EO) can be disruptive in the current status quo of the blue economy.

Brazil Earth observation data industry

Brazil is an Earth Observation innovation leader in Latin America. In over 50 years of Earth observation activities, it has shown positive growth in both technological advancement and the societal benefits derived from EO investments. Also, Brazil evolving space governance measures and policies for revamping the country’s space program fosters confidence in their long-term investment in Earth Observation innovations.

Our solutions align with Brazil’s Earth observation sector’s quest to accelerate the EO downstream market for societal benefits, evidenced in their high investment in EO educational programs and innovation like the recent launch of AMAZONIA-1. The EO downstream ecosystem top drivers in Brazil are environmental and resources monitoring, change detection among others.

Brazilian EO sector players thus require solutions that easily facilitate multi-data sources for analytics which we provide. They need to consult data or diverse dimensions and from different sources for deeper insights. ARLAS framework eliminates any challenges linked to silos by merging the data regardless of their dimensions. This provides a powerful tool that will help the Brazilian EO stakeholders to quickly explore Copernicus data and others.

Eureka is the world’s biggest public network for international cooperation in R&D and innovation, present in over 45 countries. 

If you are part of the Brazil Earth Observation sector, you can register here to attend the event where our representative will be happy to meet you.

Our team is also available to provide a demonstration of how our solutions can help you go further, faster.


Gisaia proposes HAKIKA, a systematic solution to build public transport data banks and geoanalytics for effective public transit planning and management.

May, 19th 2022


In many cities in Africa, public transportation is only licenced by public authorities then run independently by private entities. This means that often, authorities cannot ensure effective services to travellers. Private owners, while providing a valuable service, tend to prioritise  their business interests first. However, they would all benefit from an optimised and well managed transport network .

Authorities want to ensure fair mobility with services reaching as many travellers as possible, while service providers seek fast routes and filling times to optimise their fuel and expenses for positive returns.Therefore, although travellers in Africa want to go far, and fast, like everywhere in the world, they are stuck in the middle with uncertainties at many levels: they cannot confidently say how far they have to walk to catch a bus, when it will show up or how long the journey will take to their destination.

However, these challenges have solutions: Public transport authorities, when equipped with the right tools, can ensure reliable services for the travellers.

They can act as bridges between the service providers and the users to ensure equitable distribution of services.

Among the many indicators on public transport efficiency, there are three key ones. Frequency.  Accessibility.  Reliability. How can authorities assess and gauge them especially in a complex public transport network: many routes, many operators with diverse transport modes?

Gisaïa developed to help address this very challenge. is a timely solution which offers public transport decision-makers the opportunity to get a global view of the system in place. It merges, realigns and visualises public transport data that is often in silos, over time and space.

Africities presents us with the possibility to meet the relevant public transport stakeholders: authorities, operators and researchers who can take advantage of our project HAKIKA, to develop a robust public transport data bank and explore these data for decision making. HAKIKA is a word in Kiswahili that means: ‘to be sure’. 


In the era of big data, Africa is not left behind. From health to education and even technology, in both public and private sectors, Africa is not shy to build digital knowledge bases and consult them for decision making.

But only a few African cities have a public transport data bank, and in fact most cities lack it. This rich resource would not only inform authorities in African cities of their public mobility situation, but it could also allow them to proactively avoid errors made in western countries before they started consulting data ( underserved areas, long waiting times, poor connection between intermode changes, among others) when designing their networks and services. 

Most African cities are young and currently experiencing rapid growth in population, And in Africa, more than anywhere else, city travellers rely on public transportation to meet their social and economic needs. A trend that should be encouraged because shared transportation is shown to have many benefits for both travellers and the environment. The global push to get more people to use shared transportation can be guided in Africa, as it is in other places, by data-based analytics.

The HAKIKA project proposed by Gisaia offers: 

  1. A systematic approach to generate data.
  2. A reliable system to transform the data to global standards
  3. A scalable solution,, to put this data insights into the hands of decision makers

To generate the data, HAKIKA relies on a robust device developed by CLS. 

CLS is a company that provides tracking technology for various uses: animals, ships, road vehicles and more. Their solution is based on satellite technology to guarantee systematic and uninterrupted data collection. 

To transform the data, Gisaia leverages its data engineering and data scientists team : they use machine learning to produce algorithms to standardise the data. This step gives the data points generated the capacity to be extensively analysed and to provide valuable insights on the service offer.

But data banks are only useful when exploited.



The value of generating and maintaining a public transport data bank is to use it for service optimisation for effective public mobility. 

HAKIKA’s third axis is based on, a powerful geoanalytics cloud solution built on the latest big data technologies.

This allows users anywhere, anytime to explore, visualise and analyse massive and diverse public transit data. Authorities can, for instance, use it to quickly identify bottlenecks. They can also identify underserved, or overserviced areas, and take action to ensure more efficient and equitable services.

Contact us to book a demo in advance


Gisaia was at Autonomy Paris presenting to authorities, operators and researchers for public transport data-backed decision making.

March 24, 2022

Unlocking data silos for a global viewPublic Transport Optimisation Grounded on Data

All the investment that has been made in public transportation can only be maximised if residents believe that they can rely on the system. But how can authorities and operators measure this? Gisaïa developed to help address this very challenge. is a timely solution which presents public transport decision-makers with the opportunity to not only study current network challenges but to also go back in time and learn from service gaps. merges, realigns and visualises public transport data that is often in silos, over time and space for a global view of the entire network. 

 Autonomy Paris presented us with the possibility to meet the relevant public transport stakeholders: authorities, operators and researchers. 

Responding to Travellers Needs

COVID-19 triggered many questions on the future of public mobility. While we are witnessing changes in lifestyle that also impact mass transit,  we know that the surest way to understand these changes lies in rigorous monitoring and analysis. This is where data plays a key role. Now more than ever, public transport decision makers need data-backed decisions.

Effective public transportation is marked by user appreciation which is indicated by; increased uptake, service endorsement and reduced personal vehicles use. This is easily achieved once flow bottlenecks are eliminated and access is promoted. 

Shared mass transportation is of course one of the sustainable means of travelling. When it works well, everyone benefits. We know that we are offering an important piece of the puzzle to present a global view of the data, leading to tangible solutions. is built upon open-source technology making it easily customisable and extendable. It does not come with fixed analytics dashboards. Users can create new indicators based on their needs. In France, it is UGAP certified for easier acquisition. 

Public Transport Optimisation Grounded on Data can be used anywhere in the world where public transport data lies in silos to support data-backed decisions for optimised public transport services.

So, Autonomy Paris, an international mobility event, was not only a place to showcase our solution, we also wanted to use this opportunity to learn about public transport needs and trends. The event allowed us to contribute to the big picture, addressing effective public transportation for travellers of diverse needs. 

The future of efficient, reliable and appreciated public transportation lies in harnessing the power at hand; looking deeply at the information nested in the voluminous data generated everyday, in the public transport sector. 

If you stopped by our booth, you probably had a chance to exchange with the team and get a live demo of If you did not and would love to, get in touch and we will give a demonstration of how you can make the most of your data to optimise public transportation.

Contact us to book a demo in advance

at Paris Space Week

Gisaia will be at Paris Space Week on March 14 - 15, ready to meet and show you how ARLAS Exploration and ARLAS-EO help accelerate Earth observation data exploration and uptake.

February 03, 2022


Unlike most sectors, Earth Observation sector players do not have many international events that unite stakeholders to explore industry trends. Paris Space Week is one of the few events that bring together many stakeholders from the spatial industry: upstream and downstream markets. Here, participants get to showcase and learn about the latest innovations besides discussing the future of the industry.

This year, Paris Space Week is expecting over 1000 attendees drawn from diverse countries and backgrounds; government, academia, and the private sector. The event presents diverse opportunities for attendees to network through different activities set in their agenda. Gisaïa uses this opportunity to keep up with the industry trends and meet with potential users of our solutions for geo big data analytics.

The Earth observation industry is quickly growing in significance and in size. This is partly due to the increase in new sensors and constellations deployed. They are also of a wide variety and when coupled with innovation in the ground segment, they generate data that is processed and can be distributed widely. The application of artificial intelligence to derive richer analytics and support change detection capabilities helps to reduce the “time to market” from academia to society, for evidence-based decision making. 

EO data’s significance in drawing valuable intelligence to benefit the society is without question. But, the increased data generation is not matched in consumption. That is why Gisaia provides solutions that helps EO sector players to quickly put their data in the hands of those who need it. We will be at the Paris Space Week, 2022 on March 14 -15 where we look forward to interacting, learning and sharing how the Earth observation downstream market growth can be quickly accelerated.

Showcase all available EO data in one place with ARLAS-EO

If you are an Earth Observation provider, you most probably get your products and data from multiple sources. Sometimes the data comes from in-house constellations plus secondary suppliers. Your users may need to access this data from the different sources for a single project. Providing this data on a single platform not only facilitates smooth exploration of the data, it also ensures quick retrieval.   

ARLAS-EO allows you to showcase all your EO data in one place. With the multi-collection feature, users can easily select the data source and even more interesting, select multiple sources to view the available data all at once. Also, for EO data providers, this helps centralise maintenance, support and even supply, eventually limiting costs linked to operations

Now more than ever, data platforms need to be intuitive and responsive besides being informative.

Data-driven evidence with ARLAS Exploration

Gisaia also provides ARLAS-Explo upon which ARLAS-EO  is built. It facilitates geospatial intelligence. Built upon big data frameworks, it scales with your data eliminating the challenges posed by a growing data archive. Arlas-Explo allows EO data users to get deeper insights from their collection, supporting data-driven evidence. You can carry out analytics on various datasets at the same time by easily cross-linking them. This helps save on the time it takes to move from one view to the next or one platform to another. 

ARLAS-Explo is versatile as demonstrated in various applications leveraging Earth observation data: telecommunication, fleet management, insurance, maritime intelligence, among other change detection subjects. Whether you are working on sustainable management of resources, security, or business optimisation linked to Earth observation, our solutions built upon the ARLAS stack are an important brick to support data-driven decision making.

Come meet us at Paris Space Week, booth number S8 where our team will be waiting to hear about your mission and give you live demonstrations of both ARLAS-EO and ARLAS-Explo.

Contact us to book a demo in advance


As Part Of A Research And Development Contract

  3 February 2022, Media Release

Media release, 3 February 2022

In a quest to expand its smart city initiatives, Montpellier Méditerranée Métropole has chosen a consortium that includes Gisaïa among 10 others consortia in a smart city initiative for digital technology at the service of the territory’s project. It is a one year experimental work for the metropolis to offer new services to the city.

The competitive call for projects received over 30 innovation applications covering diverse themes: mobility, culture, energy, the environment, living in the city, digital inclusion and IOT. 

Gisaïa, ICIA Technologies, and Patrick Gendre, who is a mobility expert, proposed the “Open Mobility Dashboards” project.

This project responds to the Montpellier Méditerranée Métropole mobility plans for 2030. The Métropole is looking to develop mobility indicators that will support data-backed decision making to improve travel for its citizens., our public transportation data analytics solution will support the initiative in developing public transport indicators. The Open Mobility Dashboard will also include mobility indicators for pedestrians and cyclists.

The projects were selected based on the the following criteria

  1. The service: level of utility, added value,
  2. The innovative nature of the project
  3. Potential economic benefits: visibility and development potential of the project
  4. The ability to be experimented (the quality of the protocol and the monitoring of uses)
  5. Interoperability and reuse
  6. The overall coherence of the project in terms of sustainable development, resilience and contribution to the quality of life of citizens

In France, Montpellier, joins Tisseo collectivities in using for public transportation decision making. supports public transport operators, authorities and researchers in ensuring that their decisions are founded on data. Various business departments in the Montpellier Méditerranée Métropole will provide their expertise to improve feedback and knowledge of the application contexts of the services developed by the winners.

Request for a demonstration of here.

GET IN TOUCH For more details

 About Gisaïa:

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

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

Gisaia IN


  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.

For more details contact

 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.


If we understand it, we can fix it

July 27, 2020

4.2 million people die every year. 

That number of deaths is linked to outdoor air pollution as recorded by the World Health Organisation (WHO). When you include indoor pollution, the number goes up to 7 million people every year. 

While most of these deaths are in developing and middle-income countries, major cities in developed countries still record higher than recommended levels of substances linked to dangerous pollutants.

So, what are these pollutants and how do they affect people?

The OpenAQ database that is used in ARLAS’s – also an open-source framework- demonstration, collects data from 12,000 stations spread across 93 countries. The stations monitored by OpenAQ record diverse air pollutants; NO₂, SO₂, NO₃, BC, PM₂.₅, PM₁₀, and CO₂.

NO₂, Nitrogen Dioxide, is produced in abnormal quantities by human activities like burning of fuel, motor vehicle emissions, and power plants. It is toxic and easy to inhale, known to trigger asthma.

The PM₂.₅ is also a killer pollutant. Simply referred to as ‘Particulate Matter’, it is composed of solid particles and liquid droplets. It is highly inhalable causing many health problems, especially linked to the heart.

PM₁₀ is four times bigger than PM₂.₅ but just as dangerous because it can also be easily inhaled.

The trouble with air pollution is that it affects even those who didn’t create it. The big question would then be, how to trace the danger in the air and prevent exposure to these deadly agents? While many answers speak to long-term strategies, the immediate response involves aggressive monitoring and warning systems that give individuals the ability to make decisions that protect them. 

Having reliable air pollution monitoring tools like ARLAS ensures that everyone in the decision-making chain is well equipped to act.



The world ground to a halt and immediately opened the skies – quite literary in some locations – to the possibilities of tackling air pollution. The near-global lockdown courtesy of COVID-19 made it apparent for ‘pollution effects believers and non-believers’, that the Earth had been choking.

It was no surprise to see pollution levels go down as mobility reduced and non-essential factories’ tracks go on a standstill, cutting pollutants by significant numbers. 

China for example is on record as the highest contributor to CO₂ levels globally. During their lockdown, their CO₂ emissions went down by about 25%, equivalent to 150,000 tons. 

To put that into perspective, that is nearly five times what  Angola emits yearly.

We tested the theory of human contribution to air pollution. Using ARLAS, we compared March – April 2019, March – April 2020, and June 2020 data. This period represents, before and during COVID-19 for the same periods and just after the lockdown was lifted in some countries.

Focusing on provinces around Beijing, ARLAS shows  PM₂.₅ levels reducing by about 30% over the period when the lockdown is ongoing. It is clear from this demonstration that reduced human activity also reduced the volume of pollutants released in the air. There is also a significant reduction in PM₁₀ levels. 

Some activities that are traditionally low contributors to pollution like home heating and data centres increased their pollution contribution as human activities migrated home and online. This kept some pollutants volumes high.



On ARLAS, a dive down to specific stations reveals a more localized picture. 

Based on the dangers associated with PM₂.₅ and PM₁₀, the WHO advises that exposure to these two agents should be kept below 20μg/m3 for  PM₁₀  and  10 μg/m3  for PM₂.₅. Some locations record levels that are over five times higher than the recommended values. 

Residents of New Delhi benefited from a 56% drop in PM₂.₅ emissions between 26 April – 7 May, 2020, compared to the same period in 2019.

But, it was still, way above the recommended threshold.

Comparision of the PM₂.₅ emissions over New Dehli city between 26 April – 7 May 2019 (mean of 95.8μg/m3)  and 26 April – 7 May 2020 (mean of 43.4 μg/m3)

NO₂ emissions also have an adverse effect on health especially when higher than 100μg/m3 on an hourly average. Between January and August 2019, Paris experienced levels above 100μg/m3 85 times.  In 2020, it was cut down 21 times to only 4 measures of similar quantities that were recorded at the end of June, just after the lockdown.

NO2_New Delhi
Zoom on the legend Over the City of Paris :
Above: Distribution of numbers of measures of quantities of NO₂ emissions. Here, you see a selection of emissions above 100μg/m3.
Below: Distribution of the average of NO₂ emissions over time (In the 1st semester of 2019, there are 13 peaks. For the same period in 2020, only 1 peak). 


Strict monitoring of air pollution is a growing phenomenon with more stations being set up to facilitate this. The European Union region has stringent policies on pollution which have resulted in lower deaths linked to air pollution. France for instance has reduced air pollution linked deaths by half in only 25 years.

Pollution data gives the ability to analyse it and fix it. 

An ecosystem of monitoring and prevention would ideally save many lives: different players acting from different angles, easily and quickly guided by data. 

To start, policymakers like scientists and regulators, require needlepoint precision to determine their actions.

Travel companies that issue alerts on destinations based on pollution levels could help many at-risk groups to make informed choices – for example, people with preexisting pulmonary conditions. Could this lead to cheaper insurance premiums for travellers to/during low pollution zones and periods? Or even better, saving lives.

Individuals may also want to proactively stay connected to pollution information within their localities or elsewhere they intend to visit. Access to mobile applications that have credible up to date data would put pollution-fighting power in their hands like; where to find a job or buy a home amongst other human habitation decisions.

WHO is keen to have more stations set-up because the more air pollution data is collected, the clearer the picture. The OpenAQ currently collects up to 588 million air quality measurements. This is big data that ARLAS can quickly and easily process for Air pollution analysis.



The irony of the COVID-19 pandemic is that within the initial limited time of confinement – mid March – end of May 2020 – more lives may have been saved from the direct effects of exposure to pollution than were lost due to COVID-19 during the same period. 

Scientists are already linking higher critical COVID-19 impact amongst people who were previously exposed to high levels of pollution.

All efforts towards air pollution monitoring are expected to help reduce premature deaths linked to toxic air. If you are working in monitoring, policy setting or warning mechanisms around pollution, check out our demo to get a glimpse of Arlas at work on OpenAQ pollution data.

Get in touch with us at if you would like to discuss tools and services to get you acting faster.



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