Gisaia and Survintel Announce Strategic Partnership to Advance Ground Deformation Monitoring for Predictive Maintenance

Gisaia and Survintel have entered into a strategic partnership aimed at amplifying the impact of their combined expertise in geospatial data and analytics.

This collaboration builds on a history of positive engagement and reflects both companies’ shared commitment to innovation and delivering data-driven solutions to address real-world challenges. They aim to support cities and civil engineering firms in monitoring ground deformation to spot issues for early detection and immediate action. This will help save lives and valuable resources, while prolonging the life of key infrastructure.

“Our partnership with Gisaia makes our services more scalable and easier to deploy for customers globally. ARLAS® framework empowers our customers to harness the full value of insights derived from Earth Observation data. With the advanced tools provided by the ARLAS platform, complex patterns become easier to interpret.” Maud van Ees, Survintel Co-founder and Director

By joining forces, Gisaia and Survintel aim to optimize their technological strengths, development efforts, and accelerate the delivery of advanced solutions to the market. Their proactive approach directly supports the European Union’s (EU) ongoing initiatives to foster collaborative innovation within the EU’s technology sector.

“Survintel has unique expertise and experience in large-scale infrastructure stress assessment. Their ability to lead innovation in their domain aligns with our quest to explore new spaces to deploy our ARLAS® framework. We are thrilled to find a partner who shares in our push for excellence and positive customer experiences.” Laurent Dezou, Gisaïa Co-Founder and CEO

The combination of Gisaia’s geospatial big data expertise and Survintel’s specialized algorithms promises to unlock new possibilities for leveraging geospatial data to address critical societal and environmental challenges.

Both teams look forward to supporting data-backed decision making for critical infrastructure monitoring and management.

Who We Are

Survintel, based in Amsterdam, is specialised in predictive maintenance. Their comprehensive geological analyses based on EO data allows infrastructure managers to reduce their maintenance costs, increase safety and improve their risk management.

Gisaia excels in building scalable geoanalytics data analytics platforms and geo data science.Their clients range from public to private institutions, startups to innovative legacy companies leveraging space and other geospatial data.

For more information contact:

Survintel – maud@survintel.com

Gisaia – dolphine.rambaud@gisaia.com

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Gisaia and Survintel Announce Strategic Partnership

Gisaia and Survintel Announce Strategic Partnership to Advance Ground Deformation Monitoring for Predictive Maintenance Gisaia and Survintel have entered into a strategic

WELCOME TO ARLAS-BUILDER

build interactive dashboards for Geospatial Analytics ARLAS-Builder is a comprehensive studio environment that we specifically designed for the creation and customisation

build interactive dashboards for Geospatial Analytics

ARLAS-Builder is a comprehensive studio environment that we specifically designed for the creation and customisation of interactive ARLAS dashboards. Here, users can set up the exploration, visualisation, and analytics of extensive volumes of geospatial data without requiring any coding expertise.

Through its intuitive, no-code interface, ARLAS-Builder lets you configure all essential elements of a dashboard, including the integration of diverse geographic layers, the arrangement of insightful widgets, and the overall aesthetic and functional characteristics that define the user experience.

BEFORE YOU START: INGEST YOUR DATA

Your journey with ARLAS starts with you choosing how you would like to deploy ARLAS, in steps meticulously outlined in the ARLAS quick start guide; you can deploy it on infrastructure of your choice or use ARLAS-Cloud, our ARLAS SaaS offer. Then, you can ingest your data, a process detailed comprehensively here. We recently simplified this process through the ARLAS Command Line Interface (ARLAS-CLI) as shown in this step-by-step guide is readily available here.

Once your data is successfully integrated into ARLAS, ARLAS-Builder provides an array of features that allow you to define precisely how users will interact with this information through the dashboards you create.

EASILY BUILD DASHBOARDS FOR DIVERSE USERS AND USAGES

ARLAS-Builder offers users a rapid and straightforward method for customising the various parameters, visual appearance, and interactive behaviors associated with your geospatial data. Within this studio, you have complete control over defining how you, your collaborative team, and other designated users will engage with the information. This flexibility allows for the creation of sophisticated dashboards for users with advanced technical proficiencies, as well as simplified interfaces designed for end-users with varying levels of technical competence who primarily require easily digestible information. A series of analytics widgets like metrics of data, donuts and powerbars are also proposed for configuration in the ARLAS-Builder

It only takes a couple of minutes to configure these diverse user experiences and for different geo data types from multiple sources.

Below, we dive into ways in which you can configure some of the widgets on ARLAS-Builder. Widgets like the map layers that determine users’ understanding of the geographical information or the data table which helps display tabular data, or even the display of images like Earth observation objects.

Example of aggregated price map layer and analytic widgets

Example of property borders map layer and a data table

CREATING MAP LAYERS WITH ARLAS-BUILDER

CREATING WIDGETS WITH ARLAS-BUILDER

CREATING A DATA TABLE WITH ARLAS-BUILDER

GET PRACTICE WITH ARLAS-BUILDER

To gain practical experience and a deeper understanding of how to initiate projects with ARLAS-Builder, you can follow the detailed guide provided in one of our comprehensive tutorials available here. These tutorials offer hands-on exercises and real-world examples to accelerate your proficiency with the platform.

Claim A Free ARLAS-Cloud Access

We recently opened up free access to ARLAS-Cloud.

This is a unique opportunity for you to establish your own ARLAS-Cloud account and fully explore the extensive capabilities of the ARLAS suite. We encourage you to take advantage of this offer to test ARLAS’ analytical power with your own datasets and discover its potential for your geospatial data projects.

Our commitment to providing a seamless user experience is reflected in our continuous efforts to introduce new and enhanced features to ARLAS. To stay informed about the latest developments, feature additions, and improvements, we invite you to follow our blog for regular updates. This is your direct channel to stay abreast of the evolving capabilities of the ARLAS stack.

Whether you have inquiries regarding our various demos, require clarification on specific functionalities, or are seeking expert support for your geospatial big data projects, please do not hesitate to reach out. We are here to assist you in maximizing the value you derive from ARLAS and to ensure the success of your geospatial initiatives.

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Explore Machine Learning Predictions with ARLAS

Interactive Visualisations and Exploring
Geospatial Data Science ML Prediction Results

Find a reliable tool to Explore Your ML Predictions

Our data science team added the Xview dataset in ARLAS. This is a training set with manually annotated open data used as reference. This dataset contains images from complex scenes around the world, annotated using bounding boxes. The Xview challenge aims to apply computer vision to the growing amount of available imagery from space. The goal is to understand the visual world in new ways and address a range of key applications.

We use this dataset to demonstrate how easily ARLAS helps you explore the results of a prediction algorithm and share the visualised outcome.

Transform complex datasets into clear, concise and accessible visualisations

Where location and spatial relationships are important, data visualisation is an indispensable skill. It helps geospatial data scientists to share their findings effectively to decision-makers, stakeholders, and the general public. By creating maps, charts, and other visual representations of geospatial data, data scientists can bridge the gap between raw data and meaningful insights, facilitating informed decision-making and problem-solving.

The existing tools available to geo data scientists are not sufficient to support their need to effectively communicate and share their results and discoveries. Even if data scientists are used to plot graphs with tools like Jupyter notebooks and Matplotlib, it is not that easy to create interactive dashboards with options to filter on multiple datafields.

There are also challenges that come with geospatial big data volumetry. And, when the number of elements to display are massive, the classic tools perform poorly, forcing data scientists to manually compute data aggregations. Also, sharing complex dashboards can be challenging and require deployments that data scientists prefer to avoid.

This lack of adequate tools often forces geospatial data scientists to take on the extra task of building custom dashboards from the ground up, solely for the purpose of showcasing their data findings in a comprehensible manner. This additional responsibility can be time-consuming and divert valuable resources away from core data processing tasks.

Showcase The End Results To Technical and Non-Technical Audiences

Using a powerful tool to present your results will help you bridge the gap between the results produced by data scientists and the final products required by end-users. You can concentrate on showcasing the end results with a vision on what the end-user product will look like.

Here, we highlight ARLAS’ flair in supporting geospatial data scientists in their efforts to highlight valuable insights from geo big data. We will provide a demonstration of how ARLAS can be used to thoroughly explore and analyze the outcomes generated by a prediction algorithm. We will delve into the visualisation power of ARLAS and show how it helps data scientists to share their work with non-technical audiences, to interpret and understand complex prediction results.

Explore Your ML Experiments Results At Scale

ARLAS provides a comprehensive understanding of your machine learning model’s results, allowing you to interact with all data fields regardless of the dataset’s size. By providing interaction with all fields, ARLAS helps you to explore relationships, detect patterns, and uncover insights that might be hidden in large and complex datasets.

The dashboards in ARLAS provide insights into the clarity of your predictions results. Simply generate the predictions and input the data into ARLAS to share them with your team and if ready, potential users.

We ingested the 600k XView Dataset detected objects (object identifier, source image identifier, object type, bbox geometry, timestamp…) in ARLAS and created a dashboard to explore those results.

Below are three examples highlighting how ARLAS supports geospatial data scientists to get clarity and share insights from their ML prediction results.

Point #1

Geo distribution of detected object: quickly see where you detected objects

ARLAS allows you to interactively compute map aggregations that refine dynamically with zoom level. The density map layer provides an instant visualization of object locations.

At the bottom of the application, a timeline offers an aggregated view of when objects were detected. On the left, an overview of object types is displayed, while on the right, a preview of corresponding images is available.

As you zoom in on an area, the density map progressively refines until individual object shapes (bbox geometry) become visible. You can also apply map filters, such as circular selection, to focus on specific locations.

Further zooming reveals objects color-coded by type, which are displayed directly on the map.

Here you notice that you can easily change the basemap to better align with your data.

Check out our short video on how to change basemap layers .

Point #2

Focus on certain types (planes)

The image below highlights only planes and their global distribution.

On the left-side widget panel, you can observe the distribution of different object types and apply filters to focus on specific ones. Filtering by plane type instantly updates the map, showing their locations.

By zooming in, you get a more detailed view of where most planes have been detected.

A further zoom reveals their exact positions. The corresponding images are displayed on the right panel, which can be expanded to show more images.

Once again, the basemap has been adjusted, as demonstrated here.

Point #3

Visualisation of images

Now, let’s focus on the image visualization tool on the right side of the dashboard.

ARLAS seamlessly interacts with image archives, displaying images without requiring local storage, thus reducing data duplication and keeping the application lightweight.

The image metadata (identifier, bbox extent, type, timestamp…) are ingested in ARLAS. This allows the system to retrieve and display images as thumbnails or in a larger format without storing the actual image.

An object can reference multiple images. Here the cropped object itself and the entire Earth Observation image where it has been detected.

The ARLAS image viewer lets you browse these images directly within the application. Additionally, ARLAS offers a download module to retrieve image files.

Bonus Point: Share your results with your team in a “ready-for-production” tool

ARLAS makes it easy to securely share dashboard access with your team or designated users as explained here.

Get Started with ARLAS-Cloud

ARLAS gives you an in-depth view of how your ML prediction performs. It lets you interact with all the fields of your data irrespective of your dataset’s volume.

Easily exploring your ML predictions results allows you to focus on either improving the models or envisioning the best ways to share the patterns that would be otherwise impossible to discern, with others who may not be familiar with data science; decision-makers or other actors that use the insights that you have extracted to take concrete actions.

You can start exploring your ML prediction results right away on ARLAS-Cloud which also allows you to collaborate smoothly. Get in touch with us and we will help you set up an account. Register for a free account to test ARLAS capabilities.

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Gisaia and Survintel Announce Strategic Partnership

Gisaia and Survintel Announce Strategic Partnership to Advance Ground Deformation Monitoring for Predictive Maintenance Gisaia and Survintel have entered into a strategic

WELCOME TO ARLAS-BUILDER

build interactive dashboards for Geospatial Analytics ARLAS-Builder is a comprehensive studio environment that we specifically designed for the creation and customisation

Imagine effortlessly managing user roles and permissions within your organisation. With ARLAS-IAM, administrators can easily categorise users, ensuring precise control over data access. Gain peace of mind knowing that sensitive data remains secure while promoting seamless collaboration across teams and external stakeholders.

In our evolution of ARLAS, we have added user management functions with ARLAS Identity and Access management (IAM). ARLAS-IAM is fully entrenched in all ARLAS deployment; on-premises, or on ARLAS-Cloud, eliminating the need to connect multiple tools to a single service. Now, ARLAS users don’t need to link to third-party user management tools, unless they choose to do so.

When developing ARLAS-IAM we took three main aspects of data access management into account:

Account Inventory: to oversee data access, application usage and irregularities like wrong login
Compliance visibility: to ensure proper service and user provisioning. What can be accessed ? Is it the right level of access?
Analytics and Automation: admin insights on user logs and password changes.

This article takes you through the functions within ARLAS-IAM that support effective user access and management. We’ll take you through some scenarios below on potential usage but to start you off, here are some definitions used in ARLAS-IAM.

DEFINITIONS

Organisation: This is an entity grouping people collaborating together, like a company or a university. By default an organisation is characterised by an internet domain name, like gisaia.com because it covers its users with their email address. An organisation can be created only by a user having its email address containing the internet domain name. An organisation has one or several designated owners.

An organisation owns data, referred to as a ‘Collection‘ in ARLAS. A Collection is a homogeneous dataset composed of json entries with location and temporal dimensions.

A collection belongs to one and only one organisation and can be shared as a “read only” permission to other organisations. An organisation can make their collection public. If marked as public, everyone sees it but can not alter it. An organisation may own several collections depending on the diversity of their projects.

To manage data access, the owners of the organisation decide which “Users” gain access to different data collection depending on which “Group” they belong to.

A “Group” is a set of “Users”. We use groups to share data collections. We also give a group a list of “Permissions”. A permission is a rule that defines what the group (and so the user who belongs to the group) can see : the collection, the fields in the collection, and which data with an ARLAS filter.

A User is a person that is identified on ARLAS by their email address. Users have Roles assigned based on their functions. They can belong to one or more organisations within ARLAS. These users are linked to roles and groups with different access levels and permissions. The owner invites users to join the organisation.

A Role is ascribed to a User telling them which functions are available to them for their organisation within ARLAS. There are four roles: User, Editor (labelled “Dataset” on ARLAS-IAM), Builder and Owner. A person can have multiple roles.

When creating the organisation, the Owner is the designated administrator, who then assigns roles.

An Editor manages data collections. They add new data, fill in information on the data and remove redundant collections.

A Builder determines how the data will be explored by configuring the dashboards.

When the data has been set, a User can then access the ARLAS Exploration Web User Interface; explore the data collection, run an analysis on data, download results or share results link with other users.

A person who is only assigned the User role can not edit the collection, build dashboards or access other admin roles. Finally under usage, the owner sets up Groups based on usage and permissions.

A Group has permission to access a collection of data. They may or may not be allowed to see a collection. It may be a partial or full view of the data contents.

ROLES AND DATA ACCESS POSSIBILITIES

In the age of data intelligence, security is of utter importance. Who can access your data? When and if they logged in? And are there irregularities with the way your accredited users access information? Our team carried these same questions into building the functions of account inventory in ARLAS-IAM and provide these options for you to share your data:

Control Access within the same organisation; you have groups and you share with groups. Note: one group corresponds to “access all in the org”
Share with a specific or several organisations; open to all from the selected organisation
Open to the public: anyone can access the data collection

We came up with a company, “SomeCompany”, for demonstration purposes. Let’s take an example of this company’s usage of ARLAS-IAM. SomeCompany builds solutions for land monitoring and change detection. They signed up with Gisaia to use ARLAS-Cloud services to build their applications to provide insights to different stakeholders; policy makers, researchers, and even some business entities. Jane is in-charge of the database user management.

Assigning Roles

Jane is listed as the Organisation owner of SomeCompany and can therefore access ARLAS-IAM to add users and allocate them roles as defined in the second image below. Jane could designate herself or someone else to carry out multiple roles.

Max is allocated the role of Builder and User to access what he builds. He knows what aspects of the data are to be explored and sets up the right dashboard and Widgets. Once these are ready, Lucy who is now the designated “Dataset”, adds the data and makes sure that the rest of her team who are designated as users can now start exploring the data. The rest of the team as designated users.

Now, we can dive into the different ways that SomeCompany can manage data access with their team and external partners.

Managing Data Collections

Example 1: Control data access within the same organisation

By default, a data collection is private and only the owner and designated group can see it. Jane creates groups; SomeCompany, which is created by default by the system, Marketing and Communication. She adds her colleagues to the relevant groups. They can see all data collections under their organisation. Those assigned to Marketing or Communication groups also have permission to access insights from the data collections open to their groups.

Example 2: Share with a specific or several organisations

Jane’s colleagues want to grant access to their solutions to various clients from the different organisations that they work for or collaborate with.

They can provide access through different ways;

Give partial access to a data collection: City A is an organisation that collaborates with SomeCompany on a project on land use. Jane creates a new group, “Land Policy” and invites users from the City A to join it. SomeCompany has nationwide insights on land use but as City A only needs to access data over their official boundaries. Jane masks part of the data collection on land use separating City A insights from the rest. She sets permission to access this collection over City A’s geographical zone to the “Land Policy” group. They can only see data insights over the area of interest. But, people from SomeCompany could access the entire collection because the filter is not applied to their groups.
Open full collection access to an external organisation: SomeCompany has a specific product for land use evolution for real estate markets. It offers this service to Company B and others with similar needs. The data collection with these insights is labelled “Real Estate Evolution.” Jane creates a group and calls it “Real Estate Analysts” and adds users from diverse real estate companies. As there is no filter on this collection, all the permitted group members can access the whole collection on “Real Estate Evolution”.

Example 3: Open data to everyone

City K has commissioned SomeCompany to build them a solution for monitoring water levels for their locality. This is a service that City K wants to provide to different departments. Jane creates a data collection on “Water Levels” and makes it public. This collection is visible to City K users and its stakeholders who easily access it as they don’t need to be part of a group to access it. Other users outside City K can also view the insights by having the link to the data collection. No login required here.

In this case, Jane makes sure that users’ access is limited to their permissions. This not only protects their data but also ensures that users focus on what is important to them. When viewing data on ARLAS hub, they only get to see the collections they are allowed to access. Like, SomeCompany users have access to all the collections as highlighted below.

Each group only sees the collection that they are permitted to access.

Finally, ARLAS-IAM ensures that it has a reliable user log to track activities on their application.

ARLAS-IAM simplifies the account creation process for Jane. The data parameters —public or private collection — are defined, and the organisation domains provided, all permitted users who need to login can automatically create accounts or change passwords. Jane only gets a notification when it happens and can verify if all is fine.

TEST ARLAS

Are you new to ARLAS? Check out our public demos here and explore diverse data collections from different geospatial data sources highlighting ARLAS’ capacity in massive data visualisation, exploration and analysis. If you would like to test your own data on ARLAS, join this waitlist, and we will notify you in the coming weeks when we launch our free offer of ARLAS-Cloud accounts.

You can also get in touch with us to discuss your projects and together we can explore if ARLAS is the right fit for you.

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Gisaia and Survintel Announce Strategic Partnership

Gisaia and Survintel Announce Strategic Partnership to Advance Ground Deformation Monitoring for Predictive Maintenance Gisaia and Survintel have entered into a strategic

WELCOME TO ARLAS-BUILDER

build interactive dashboards for Geospatial Analytics ARLAS-Builder is a comprehensive studio environment that we specifically designed for the creation and customisation

Gisaia's ARLAS-EO Expands Global Reach with New Contracts in Africa, Europe, and South America

Gisaia is proud to announce the expansion of ARLAS-EO into three new countries across three continents. Two private contracts and one public tender have this quarter helped with the delivery of three Earth Observation (EO) catalogues to esteemed space agencies in Europe, South America, and in Africa, to Morocco’s space agency, the Centre Royal de Télédétection Spatiale (CRTS).

These newly deployed platforms powered by ARLAS-EO are designed to ensure the easy and reliable distribution and access to Earth Observation products. As Gisaia’s turnkey solution for developing robust EO data catalogues, ARLAS-EO stands out for its scalability, customisable features, and swift deployment capabilities.

With ARLAS-EO, users benefit from a seamless and efficient way to manage their EO data ensuring they have the right tools to support their mission. This milestone underlines Gisaia’s commitment to providing innovative solutions that meet the diverse needs of the global space community.

For more information about ARLAS-EO and Gisaia’s work in Earth Observation web platform development, please get in touch with us.

Contact Information: info@gisaia.com

Do you provide market insights services to real estate agents?

If you build real estate market evaluation tools for agents or the public, this article aims to show you how you could leverage ARLAS. ARLAS is our solution for developing robust geoanalytics solutions to augment your service and guarantee your end users like real estate agents, reliable market insights.

While most evaluation tools can show you the average property price area of interest, most solutions do not answer specific questions or give historical sales information on particular properties like:

What is the average price per square metre in my area of interest for say, apartments only?
How many buildings of a certain size were sold in my area of interest during a specific period?
If one wants to buy an independent house of about 100 spm under 350,000€, where should they start looking?
What are the trends in pricing? Are they rising, stagnant or falling?

These, and other market insights are easy to derive from ARLAS. We explore some of them below in this article.

We have integrated real estate transactions data from the French Open Data platform captured above. This open dataset encompasses detailed information on property transactions across diverse regions in France.

We focus on data from Haute-Garonne (south west of France) to illustrate ARLAS’s excellent capacity to enrich real estate market assessments as highlighted below. The data we select captures transactions between January 2014 to November 2023. Nearly a 10 year period that can allow us to carry out in depth historical exploration. During this period, we have over 300,000 transactions to explore.

In ARLAS, we unearthed additional valuable insights that cannot be extracted through conventional approaches from the government platform.

Query #1 : Imagine that your client has just moved into the region and has no specific location demand, but give you their budget, size and type of property that they are looking for. Like, an independent house of about 100 spm for under 350,000€ in any neighbourhood in Toulouse.

Where should I start looking?

As you can see in the map below, some neighbourhoods have less to offer for our search. This quickly gives a clue on where to concentrate your efforts.

Query #2: How many apartments of a certain size were sold in my area of interest during a specific period? In our exploration below, we query the month of July for the years 2021, 2022 and 2023. We can then compare the results from the different periods to break down the aggregated data from the source.

We focus on houses with a surface between 110 sqm and 125 sqm in Toulouse. We see that the average price in 2021 and 2022 for roughly the same surface area, about 112 m2 around €4400 per square metre. 2022 is slightly higher by 70 Euros per square metre. The price per square metre drops by about 400 Euros per square metre in 2023. This means that the house that was going for about 500 thousand Euros in the two previous years, has lost a market value of about 50 thousand Euros.

Query #3: A seller wants to know how to price their property. They seek a benchmark for their offer. For example, how many properties of a certain price range were sold in my area of interest during a specific period?

Let’s try a search of the number of houses in the Tournefeuille area with prices ranging between €380,000 and €440,00 , in the 2O22. We end up with 38 houses averaging €658 per square metre and can see the spread of the price evolution per square metre over that year.

ARLAS facilitates these exploration and analytics in a simple and fluid manner.

Query #4

Say you want to offer a view of pricing trends of a given area: Have prices gone up? Down? Are they static? ARLAS also facilitates this analytics from the historical data, going as far back as the data recorded.

In this case we explore Balma. We see that even though there is a slow down in transactions. The price per building’s square metre has barely changed in the last 10 years, compared to Rouffiac-Tolosan, a neighbourhood just around the corner from Balma.

Links to results

ARLAS also allows you to filter based on a perimeter tied to a point of interest. In the example below, we choose a school in Balma as our point of interest. We then set a radius of two kilometres and explore the number of houses sold in the last two years. We can even highlight the latest transaction to get more details.

ARLAS’s analytics capacity supports unlimited exploration of all the dimensions present in the data. As long as the data carries the information, you can extract it to enlighten your clients on:

How to price their property
What they can get for their budget
When to take advantage of the market

And even nuggets of insights like which neighbourhoods have fewer ownership succession, a sign of satisfaction.

Fun facts!

According to this data, most transactions are carried out on Fridays! Why? Maybe to go into the weekend in celebration.

It is interesting to see that some of the deals took place over the weekends. Because, albeit small, some transactions took place on Saturdays and even Sundays!

And, July and December take the top spots for highest transactions. Real estate agents will tell you that most families in France opt to move in July as the onset of the summer school break allows them to settle in before a new school year. Why December? Something to investigate.

Let’s talk

You already know that tools can’t completely replace human sentiments and experience, but it sure does help to back your counsel with data. With ARLAS, your real estate analytics solution could be more versatile, offering in depth analysis to your clients.

If you have other data sources that you would like to explore alongside property transactions data, please get in touch with us to discuss this. ARLAS’s offers you the valuable advantage of drawing from diverse sources of data.

INTRODUCING PUBLIC GRASP CATALOGUE BUILT BY GISAIA

GRASP GLOBAL'S CUTTING-EDGE PRODUCT CATALOGUE FOR ADVANCED AIR QUALITY MONITORING

25 May 2023, Media Release

Lille – Toulouse: 25 May, 2023 GRASP Global has launched its “Public GRASP Products Catalogue” for businesses and organisations tracking and studying air quality, the company announced today. The platform offers advanced remote sensing products that enable the forecasting of hazardous air pollution events and estimation of past exposure. The catalogue also includes data sets that can be used to help understand climate change. The catalogue was built by Gisaia, a geospatial big data technology company.

The Public GRASP Products Catalogue includes a visualisation tool to explore the data. The products in the catalogue are created from satellite data from a variety of European instruments, which are then processed into advanced remote sensing products by GRASP.

The GRASP processing system is unique in its capability to combine data from multiple space sensors as well as incorporate data from ground instruments. This allows for the study of specific areas with high precision.

COMING SOON!

The catalogue will soon include data from GRASP’s own GAPMAP-0 space instrument, which was launched on April 22, 2023. This marks the first time a commercial space instrument has been deployed for the characterization of particulate air pollution in cities and communities.

When completed, the GAPMAP series of instruments will comprise a constellation of ten advanced cubesats that will collect 100 times more data globally than traditional air quality instruments already in space.

GAPMAP products will be available to the public alongside other existing products in the catalogue, providing users with valuable information about the Earth’s surface, clouds, and atmospheric aerosols.

MONITORING FOR EFFECTIVE MEASURES

Air pollution is among the greatest environmental risks to public health, according to the World Health Organisation. Outdoor air pollution is estimated to have caused 7 million premature deaths worldwide in 2019.

GRASP facilitates the use of satellite measurements to establish whether particles are likely man made (industrial and vehicular emissions) or from natural sources such as wildfire smoke or desert dust and whether they are produced locally or transported from more distant locations.

Thus the Public GRASP Products Catalogue can be a critical tool to determine the sources and downwind areas affected by air pollution. This provides a significant weapon for the public and policymakers to fight for improved air quality by holding the producers responsible.

“The GRASP software makes it possible to use the satellite measurements to establish whether particles are likely produced by industry or swept in from natural sources such as wildfire smoke or desert dust. This is useful for governments, cities, and even companies who want to monitor air pollution and design effective mitigation measures,” said Dr. David Fuertes, GRASP Global’s co-founder and CEO.

The catalogue is linked to the existing GRASP Open platform, which currently has around 1,200 users from the scientific community in 64 countries. It is built upon ARLAS-Server, an open-source technology brick for big data visualisation and exploration. Catalogue users can easily and quickly find relevant products through spatial as well as temporal filters. It is run on Gisaia’s ARLAS-Cloud service.

“We are honoured to be part of a mission that helps to address one of the world’s greatest challenges. We thank GRASP for choosing our technology and trusting our team with this assignment,” said Laurent Dezou, Gisaia’s CEO.
“The long term strategy is to keep developing the tool, adding analytics capabilities thanks to the power of ARLAS,” added Dr. Fuertes.

GRASP Global Services supports worldwide efforts to monitor the health of people and the planet. Space agencies, governments, city authorities, and companies that wish to effectively track air pollution are invited to explore the publicly available catalogue.

For more details

To learn more about the Public GRASP Products Catalogue, contact GRASP Global at office@grasp-sas.com.

Additional RESOURCES

Additional Resources:
Follow GRASP Global and Gisaïa for updates:
Follow GRASP Global on Twitter, LinkedIn
Follow Gisaïa on Twitter, LinkedIn

About GRASP Global

GRASP Global is a start-up company that employs a variety of tools to monitor and understand the Earth’s atmosphere and surface.

These tools include the development and deployment of spaceborne cubesats, ground instruments, and the GRASP algorithm. The GRASP algorithm provides unique capabilities to combine data from multiple instruments to provide advanced products for air quality and climate change.

GRASP has more than a decade of experience in developing hardware and software for space applications and has worked with the largest international space agencies. The company has been engaged in the development of the operational algorithms for generating surface and aerosol products for Sentinel-4, 3MI, and CO2M future ESA and EUMETSAT missions.

GRASP designs and manufactures ground instruments that are deployed in global networks by NASA and the SPARTAN network.

Recently, GRASP launched the first in a series of cubesats designed to monitor the atmosphere for air quality and climate applications.
For more information about GRASP Global, visit www.grasp-sas.com

About Gisaia

Gisaïa is an expert in geospatial intelligence with experience in facilitating the rapid deployment of technology solutions for deeper geospatial and big data analysis.

Our ARLAS framework® is built to serve efficiently: every function is developed to not only support the ease of operations but also deliver expected results.

Gisaïa offers geospatial technological expertise to players in various sectors. Using ARLAS®, Gisaia supports users in developing products and services for diverse use-cases.

Some of Gisaïa’s references in the spatial industry include: Airbus Defence and Space, the French Space Agency, the French Public Research Institution, IRD and GRASP Global.

For more information about Gisaia visit: www.gisaia.com and ARLAS: www.arlas.io

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

CLIMATE CHANGE AND TURTLES MIGRATION

DATA ANALYTICS

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.

WORKING WITH MULTIPLE COLLECTIONS OF MASSIVE GEO DATA

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.

You may also want to check out this article on data analytics of storks migration.

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.

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.

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.

REVELATIONS FROM THE DATA

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.

ARLAS EXPLO FOR GEOANALYTICS

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.

REQUEST FOR DEMO

contact@gisaia.com

ARLAS-EO at
"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
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

WHY PARIS SPACE WEEK

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

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CLIMATE CHANGE AND TURTLES MIGRATION

DATA ANALYTICS

we NEED ACCURATE INFORMATION To preserve endangered species

WORKING WITH MULTIPLE COLLECTIONS OF MASSIVE GEO DATA

REVELATIONS FROM THE DATA

ARLAS EXPLO FOR GEOANALYTICS

REQUEST FOR DEMO

ARLAS-EO at "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.

Gisaia 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.

Interactive Visualisations and Exploring
Geospatial Data Science ML Prediction Results

ARLAS-EO at
"Eureka Meets the Atlantic"

Gisaia
at Paris Space Week