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VorteX-io is an innovative French company at the cutting edge of hydrology, offering a range of high added-value decision-support services. These include preventing flood and drought risks, improving water resource management and optimising the operation of hydraulic infrastructures. Within two years, vorteX-io will offer the first river forecasting service for the whole European continent based on the creation of digital twins of watersheds. To this end, vorteX-io is currently deploying a network of several hundred of its own innovative remote sensing devices across Europe as part of the WHYLD project, co-funded by the European Innovation Council (EIC).

vorteX-io is developing the first European database for real-time, high accuracy, hydrological data, aiming to monitor all European watercourses, with a focus on the 7,000 flood risk areas identified by the European Commission. Their user-friendly data marketplace, Maesltrom, allows customers to visualize real-time watercourses and access features like archived data, rainfall estimates, water flow, and a predictive indicator for flood risk. The data is obtained through low-cost, plug-and-play hydrological stations and artificial intelligence algorithms. This solution addresses the lack of large-scale hydrological data and provides a cost-effective, scalable solution for stakeholders.

The solutions provided by vorteX-io contribute to EU priorities such as risk prevention, civil protection, and climate disaster prognosis. They also support the construction of an accurate and connected European network for collective resilience. The actions of vorteX-io align with UN Sustainable Goal 13, which focuses on addressing climate change and its impacts, and strengthening resilience and adaptive capacity to climate-related hazards and natural disasters in all countries.

vorteX.io’s unique selling point is hydrological data as a service, with two differentiators: micro-stations that measure and transfer water parameters in real-time, and a scalable distribution model through a self-subscription service on an all-inclusive SaaS platform. The innovation includes a proprietary sensor, the micro-stations, providing real-time hydro-meteorological parameters, and a non-binding turnkey business model. The level of innovation is high, as the availability of an “anytime/anywhere” hydrological database through a SaaS service is new in the hydrology domain. The type of innovation includes a new lightweight and scalable in-situ measurement system based on space technology transfer.

The WHYLD project, led by vorteX-io and co-financed by the European Innovation Council, aims to meet the increasing need for real-time monitoring of hydrological surfaces in Europe, particularly in France where over 8,000 cities lack such solutions. The project could serve as an enabler for more precise predictive analysis regarding climate change impacts, such as floods and droughts. The initiative is expected to strengthen Europe’s technological sovereignty in preventing hydrological hazards.

The need for hydrological measurements is growing worldwide due to increasing flood risk awareness and climate change. The proposed WHYLD project, a first-of-its-kind SaaS service, aims to address the lack of hydrological data with a network of micro-stations providing real-time measurements. This innovation, already tested in France and Europe, is a strategic technology for all countries threatened by floods.

The total addressable market (TAM) for this service is expected to reach $23.46 billion by 2027. The company aims to capture 1% of the serviceable available market (SAM), estimated to be $2.4 billion by 2027. The company faces competition but has a unique value proposition and a business model that differentiates it from its competitors. The company’s strengths include a scalable product, an experienced team, and a unique business model. However, it also has weaknesses such as a prototype service and a lack of skills in marketing and sales.

About WHYLD

To address the worldwide rise in climate-change driven devastating flood events, project WHYLD at vorteX-io aims to demonstrate the benefits of a large-scale real-time hydrometeorological database towards assisting governments in effective flood risk management.
Backed by the EIC Accelerator Programme, project WHYLD involves the development and deployment of an innovative and intelligent river forecasting service. This is made possible thanks to proprietary algorithms and hardware inspired by satellite technology: vorteX-io’s Micro-stations that can measure data such as water surface height and velocity, with ongoing R&D for river flow and rainfall rate parameters.
The project’s initial phase involves large-scale deployment of this non-binding turnkey service in two pilot countries, France and Croatia, eventually covering all of Europe and flood-risk areas globally.

Seven European partners, including offshore renewable energy and Earth Observation specialists, have teamed up for the BLUE-X project. Together, they will develop a satellite-based decision support tool to accelerate offshore renewable energy deployments. This is the first ​​blue energy Horizon Europe project funded by the EU Agency for the Space Programme (EUSPA).
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Seefeld, Prague – Feb. 2024 –  (…) Blue renewable energy sources such as offshore wind, offshore solar, wave and tidal energy have a high and mostly unused potential in times of a changing global energy policy. In 2020, the European Commission set ambitious targets of 300 GW of offshore wind and 40 GW of ocean energy across all the European Union’s sea basins by 2050. However, achieving these calls for thorough and time-consuming MetOcean, geophysical and environmental campaigns. 

‘Increasing the share of blue energy is a key building block for reaching the goals of the Green Deal.Scaling up the use of Copernicus satellite data can support the rapid and fact-based decision-making needed’, says Fiammetta Diani, Head of Market Downstream and Innovation at EUSPA, reminding that BLUE-X aims to help the acceleration of the energy transition in the European Union. 

For upscaling offshore renewables, BLUE-X will provide key data useful for all five steps of the blue energy lifecycle: site assessment, planning, construction, operations, and decommissioning. ‘Our mission is to give valuable decision-support to offshore renewable energy projects. We aim for a tool that is easy to use and offers quick access to key data on the coastal areas in question,’ states Kim Knauer of EOMAP, leading the BLUE-X consortium. 

Six use cases across Europe will ensure a BLUE-X solution in line with the user demands of offshore renewable energy developers and providers. These cases will cover various blue offshore energy sources. 

The consortium partners include the Dutch Marine Energy Centre (DMEC, The Netherlands), EOMAP (Germany), Fórum Oceano (Portugal), INESC TEC (Portugal), Inyanga Marine Energy Group (France/UK), Politecnico di Torino (Italy) and Wave for Energy (Italy).

Please access the complete joint press release + an infographic and free imagery at this LINK.

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Developing Next Generation EO Applications

The Company

RSS-Hydro is a Luxembourg-based company developing innovative water and climate risk solutions to help public sector and industry clients achieve impact in the SDGs for a better and safer future. We are regularly carrying out a number of research projects with national and international partners to develop science-based commercial solutions using the newest advances in Earth Observation (EO) and space technologies and combine this with numerical modeling and novel computing architecture. This allows us to push current state-of-the-art capabilities to the next level, so we can build next generation products  and offer them today.

One of our flagship EO products that has now entered its commercialization phase, is FloodSENS, an AI-based algorithm that creates valuable flood impact maps even from clouded satellite images to support the financial risk industry as well as the international disaster response community.

Motivation for FloodSENS

The value of satellite sensors  for mapping and monitoring floods has long been established and oftentimes during flood disasters satellites represent the only means of obtaining actionable information. Over just the last couple of years, under a strong climate risk signal and economic insecurities, we have witnessed disastrous flood events, spanning across unprecedented spatial and temporal scales. These disasters trigger humanitarian crises, massive asset damage and very often result in large displacements of people that cause security and conflict concerns. During such large events that are unmeasurable and cannot be monitored with traditional field equipment, satellites are one of the only means to gather crucial information for mitigation, response and recovery. Although SAR (Synthetic Aperture Radar) may be the obvious choice for detecting flooding during adverse weather conditions, the present archive of available SAR images is still rather small to infer trends about flood risk and climatology of floods whereas optical satellite sensors of various resolutions have been around for more than 50 years now, albeit hampered by cloud cover during floods. Nonetheless, there are many examples of flood disasters where optical satellite sensors have been providing cloud-free or partially clouded imagery.  

The FloodSENS Algorithm

It is clear that overcoming the cloud cover issue in optical flood images would certainly be beneficial since it would allow a higher number of impacted areas to be mapped and it would also greatly extend the archive of flood maps from satellites, thereby providing better information to both the flood response sectors, including defense intelligence, as well as the financial risk industry, particularly the insurance and reinsurance sectors. To this end, RSS-Hydro has gathered needs and requirements from both the humanitarian and reinsurance sectors, and together with the UN WFP and Willis Towers Watson (WTW) as stakeholders, has joined ESA’s InCubed program to  develop a Machine Learning (ML) application, called FloodSENS, to extract flooded area in optical flood images, whether cloud-free or partially clouded.

Example tiles of FloodSENS from different regions in the world.

The ML model is based on the well-known U-Net architecture and uses Sentinel-2 (S-2) flood images and derivative layers from digital elevation models relating to topography and waterflow to map flooding even below partial cloud cover. The algorithm further employs a squeeze and excitation network to extract information about the importance of the different input layers. During the project, FloodSENS was trained on a large number of expertly labelled S-2 flood images across different biomes, events and locations to ensure acceptable transferability, which is to become an important part of RSS-Hydro’s IPR of FloodSENS. Internal application testing and validation shows, unexpectedly, varying degrees of performance and accuracy. Overall, on average, FloodSENS performs at least as well as any robust and calibrated traditional band ratio index (>90% correct prediction), and in some cases outperforms such, and even maps below low cloud cover and correctly includes flood impact areas from dried out areas by following debris lines.

Moving to the Next Level of Implementation

Many exciting developments are happening in the new space sector, from smallsat SAR or thermal sensor constellations and satellite video to in space data relay systems  and even in-orbit computing platforms. 

Our growing international partner network, in which EARSC plays an important role, allows us to play a major part in these exciting developments with our EO applications that we thrive to improve with potential stakeholders in an agile development process.

One of our latest exciting developments includes the partnership we have with NVIDIA. We use various geospatial datasets with NVIDIA’s scene-generation platform Omniverse to build realistic 3D animations of disaster impact that help increase resilience of vulnerable communities.

A snapshot scene from our own 3D animation creation using the Omniverse platform of the flooding in the coastal areas of Mozambique caused by the massive tropical cyclone Idai in March 2019, mapped with FloodSENS 

From these partnerships and collaboration we, at RSS-Hydro, ensure that our products can seamlessly grow in value-add and market share potential. 

“Together, let’s shape the future of the global EO market”

Contact

Visit our website: https://rss-hydro.lu/ 

Follow us on social networks: LinkedIn Twitter

Email us at: info@rss-hydro.lu 

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Yield prediction for arable crops in Germany based on Sentinel-1 SAR data 

For a farm in Thuringia/Germany, we have developed a model based on historical yield data and  ESVI (enhanced SAR Vegetation Index) that enables the yield to be reliably estimated.  Sentinel-1 satellites deliver data regularly every 12 days worldwide with a resolution of 20x20 m.  The ESVI is based on this data and can be seen as a proxy for fresh biomass.  SAR data are independent of daylight and atmospheric disturbances and are therefore ideal for time series analysis. In this evaluation, we used yield and satellite data from all years 2018 - 2022.  

Data: Zonal statistics were created for all individual plots, all years, and for each acquisition date,  which resulted in the mean value, the maximum, and the minimum being extracted from the respective ESVI data sets.  

This data was then compiled in data tables and compared with the yield data for the respective years. Yield data results from weighing logs captured after harvest.  

Method: For the modeling of the yield values, 5 consecutive ESVI datasets were used. The same formula was used for all cultures and all years.  

The formula depends on a weighting table for each crop-type, which is valid for all years.  Depending on the weather conditions, the start of the season may be delayed by a few days.  The model allows a corresponding adjustment if it becomes apparent that the growth phase in the current year will start much earlier or later than in ‘normal’ years.  

The evaluation in the graphic below, for one plot and one year, shows a slightly thicker green line. It represents the mean value of the individual plot. The line is flanked by 3 other lines showing the mean value over all plots of the same crop-type and the same year. In addition, you see the simple standard deviation around the mean as a confidence interval. The two gray bars indicate the period for which the yield modeling was calculated in this example (early March – mid-April). 

On the right-hand side you can see the modeled yield values for the individual plot (minimum, mean  and maximum) in the bar chart and below that the harvest date, the measured yield and the moisture content in the grains. 

Results: The tables for every single year can be seen in Annexes 2-6. In the header of each table you can see the acquisition dates on which the last of the 5 succeeding ESVI datasets were used for yield modelling. The table shows the deviation of the modeled yield/ha from the measured yield/ha.  A value of 104 in this table means, that the model overestimated the yield by 4 %.  

In the first line of each table you see the deviation of the modeled yield from the mean ESVI value over all plots of the same crop-type and the same year. 

Plots with relatively low yield values but high ESVI values were massively overestimated. However, it  is also possible that the assignment of yield per plot data, in the logistic chain or the weighing log  was not always without errors. On average over the entire farm, the results are surprisingly good.  

In the following table you see an aggregation over all five years. The result from all years, all plots  and all varieties show a result close to 100% compared to the measured yield. Yield prediction can be done from early April and with high reliability from early May to mid of June. 

Overall, it can be said that the model seems to be stable over the years. Even if individual plots stand  out as outliers, the average over all plots of one crop type is close to the measured yield.  

The standard deviation indicates, that yield prediction between early and mid May, for each single  crop and over all crop-types deviates from the real yield in 2 from 3 years by less than 10 %.  Only in 1 from 20 years it can be expected, that the predicted yield deviates by more than 20%.  

Based on this data, a farmer can plan at an early stage how much storage capacity is needed or how  much of his predicted yield he can sell in advance into the market.  

Various applications based on yield estimation can be seen in crop insurance and agricultural  trading.  

Considering the current situation in Ukraine, a reliable and early yield forecast would be important  for the entire logistics chain right down to the end consumers. 

Implementation: The software enterprise ESRI built an application where the algorithm for the yield  prediction is already onboarded. The tool allows the farmer to select single plots or all plots of one  crop-type to calculate the yield prediction for the actual year, based on the map product ESVI.  Customers who register for that service have as well access to other map products from the same  sensor like SWI (SAR Water Index), which is useful to monitor drought condition in cropland.  Within ArcGIS online they can easily apply seasonal and annual change detection on-the-fly. 

The data will be actualized according to the acquisition schedule of Sentinel-1 with a delay of less  than 48 hours and is later available in the online platform ArcGIS Imagery

iMap| software that moves us 2/2

Download the PDF version:

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UP42 brings STAC to its storage

Over the past few years, the geospatial industry has grown significantly, and with it, the number of image providers and data volume. Most users have probably faced a few data management challenges along the way. Data standardization, or the lack of it, in particular, is often cited as a major barrier to the widespread adoption of geospatial data.

SpatioTemporal Asset Catalog (STAC) is a specification designed to establish a standard in exposing geospatial data and simplify data management for providers, developers, and users. It allows searching across multiple providers for geospatial assets which share a common structure and set of metadata. UP42 has further strengthened its commitment to the STAC standard by integrating STAC in UP42 storage and ensuring a common metadata format across providers.

STAC might seem like another data standard at first. It is not. First introduced in 2021, it has improved and grown in popularity significantly. Many of the biggest providers of remote sensing and spatiotemporal data use the STAC specification. STAC has a well-designed, standard format, it is user-centric, as well as searchable, crawlable, and indexable. STAC allows users to easily search for, acquire, and analyze geospatial data from multiple providers and sources. This is extremely important because a lack of standardization often leads to low availability of plug-and-play integrations. STAC enables a strong foundation for exploring data from different providers, as well as for building a robust backend, downstream integrations, web portals, visualization, and more.

UP42 provides a STAC API endpoint for data searches in UP42 storage. The assets in storage can be data from catalog or tasking orders. This ensures a consistent metadata format across all providers, and with standardized metadata, customƒfers can more easily integrate data into their pipelines. For example, if someone places a tasking order, they will automatically get the respective metadata for the tasked assets in the API shortly after the raw asset is delivered to storage, so theƒy can immediately work with it.

STAC is organized into the following structure.

  • STAC catalog is a top-level object that provides a linking structure for grouping other STAC objects.
  • STAC collection is a UP42 asset resulting from a completed tasking or catalog order. It groups related items and aggregates their summary metadata. It contains STAC items.
  • STAC item is an individual scene in a STAC collection with a unique spatiotemporal extent, such as tiled images, images with different acquisition times, stereo pairs, or tri-stereo triples with different angles and acquisition times. Different spatiotemporal extents produce different STAC items. A STAC item contains STAC assets.
  • STAC asset is a geospatial feature of a STAC item, its quicklook, or metadata file. For example, multispectral and panchromatic products of an image acquired by an optical sensor are different STAC assets.
  • STAC extension is a JSON schema with additional parameters available for a specific STAC object. Extensions differ based on data availability, sensor, and constellation.

More information is available in our technical documentation.

In addition to introducing STAC and enabling STAC-compatible endpoints for all geospatial assets, UP42 has introduced a new set of functionalities to UP42 storage. Users can now reflect their internal asset structure by customizing assets with their own titles and tags. They can also search for a specific asset property (e.g., order ID, title, or asset name) or create a consolidated view of their assets through sort and filter operations for collection, producer, tags, title, and source (catalog or tasking orders) - on the workspace and account level.

We will continue to play a key role in supporting our partners and customers on their journey with STAC. We will also continue our commitment to implementing more cloud-native geospatial data formats such as standardized COGs, exposed through STAC, enabling streaming capabilities and access to subelements of UP42 assets, as well as unifying file format and directory structure. Expect more updates from us soon.

Download the PDF version:

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New Horizon Europe project ‘EvoLand’ sets off to develop new prototype services for the Copernicus Land Monitoring Service

Munich, January 24, 2023

A consortium under the lead of VITO officially launched the new Horizon Europe project EvoLand (Evolution of the Copernicus Land Service portfolio) in Leuven (Belgium) on the 17th and 18th of January 2023. EvoLand will develop and test new and innovative methods and algorithms and implement a bundle of candidate Copernicus Land Monitoring Service prototypes. This will be realised by integrating novel EO and in-situ data with latest Machine Learning techniques to continuously monitor the status, dynamics and biomass of the land surfacefocussing on five key thematic domains – agriculture, forest, water, urban and general land cover.

GAF AG is part of this new research and development project that includes 10 key-players of the EU Space industry from 5 EU countries. The project will be running for the next 3 years.

Why EvoLand?

Since 2011, the Copernicus Land Monitoring Service (CLMS) provides core products for the monitoring of status and changes in vegetated and non-vegetated land cover/land use state and characteristics, biophysical variables, water and cryosphere conditions. Currently CLMS needs to advance in phase with the new Copernicus 2.0 programme, the evolving user needs, global challenges and technical capabilities, meanwhile maintaining the existing core Copernicus information products and services. This is a challenge which requires a more efficient workflow enabled by automation, harmonisation, further integration of the products across existing components (at local / pan-European/ Global level), modularity of the software and data products, and the increased use of Artificial Intelligence to provide real and near real time data processing.

EvoLand – Evolution of the Copernicus Land Service portfolio – addresses these needs in a comprehensive way through a well-designed process, developing innovative methods, algorithms and candidate CLMS prototypes to monitor the status and changes of land use/land cover and various land surface characteristics at high spatial and temporal resolution.

How is the project going to achieve its objective?

EvoLand aims to develop eleven next-generation CLMS product candidates by integrating innovative approaches in data fusion, Machine Learning, continuous monitoring and biomass mapping, as well as through the integration of novel EO and in-situ data. In addition, the project will analyse policy, data and infrastructure requirements for the prototype services, interact with the relevant Entrusted Entities (European Environment Agency [EEA], Joint Research Centre [JRC]) and consult other main Copernicus Land stakeholders and users.

Present at the kick-off meeting in Leuven on the 17th and 18th of January 2023, EEA and JRC representatives congratulated the consortium on this project and expressed their great interest in closely following the process and eagerly await the results, collaborating on the evolution of the CLMS.

During the demonstration phase of EvoLand, candidate services will be prototypically implemented over larger regions, focusing on key thematic domains such as agriculture, forest, water, urban and general land cover. They will be regularly analysed to ensure their fit for purpose, policy support potential, technical and innovative excellence, and operational readiness. The ambition of EvoLand is to support the Entrusted Entities by dedicated research, providing tangible proof of the evolution potential of the CLMS in terms of improved information content, quality and timeliness, and enable well-informed and facts-based decision-making on the future of the CLMS. A strategy to transferring these prototypes into operational CLMS services will therefore also be proposed as part of the project outputs.

The following eleven CLMS prototype candidates have been selected:

  • Forest: continuous forest monitoring, forest disturbance mapping, forest biomass mapping;
  • Agriculture: cover crop type mapping, cropland/grassland gross primary production monitoring, small landscape features mapping;
  • Water: improved water bodies mapping;
  • Urban: automated land use mapping of urban dynamics, continuous imperviousness monitoring;
  • General land cover: continuous mapping of land surface characteristics, on-demand land cover mapping.

The team behind EvoLand

EvoLand brings together a unique consortium of 10 partners from 5 European countries providing all the professional knowledge required for a successful achievement of the ambitious project goals and objectives. Led by VITO, an independent research organisation from Belgium, it involves also CESBIO (France), CLS (France), CNES (France), DLR (Germany), Evenflow (Belgium), GAF AG (Germany), IIASA (Austria), Joanneum Research (Austria) and Sinergise (Slovenia). In terms of expertise, the EvoLand consortium includes CLMS specialists, key CLMS service providers and leading research institutes in all methodological developments. The team also involves partners experienced in supporting the Copernicus in-situ component, driving innovative in-situ data collection, cloud infrastructure and mass data processing, as well as effectively engaging stakeholders.

The team met for the first time at the kick-off meeting in Leuven with first discussions and decisions on the immediate steps, approach of certain tasks and further cooperation. “I am very proud of the competencies within the team and look forward to working closely together,” said the Scientific coordinator of the project Dr Ruben Van De Kerchove, VITO.

Image: Kick-off Meeting of EU Consortium of the new Horizon Europe project EvoLand (Evolution of the Copernicus Land Service portfolio) in Leuven, Belgium, on the 17th and 18th of January 2023

Credits: GAF AG

For further information:

Project PartnerGAF AG
email: copernicus@gaf.de Communication ManagerMr Asaf Covo, Evenflow
email: communications@evenflow.eu  		Project CoordinatorDr Ruben Van De Kerchove, VITO
email: ruben.vandekerchove@vito.be

About GAF AG

GAF was founded in Munich in 1985 as the first German company with a focus on applied remote sensing. It is now one of the leading commercial geoinformation service providers in Europe in the area of earth observation. As part of the e GEOS/ Telespazio group of companies, GAF offers an extensive service portfolio that, in addition to direct satellite data reception and distribution, includes advanced analysis techniques, AI processes and the tailor-made development of geoinformation and software systems, platforms and consulting solutions. The thematic areas of specialisation for public and private clients worldwide include land monitoring, natural resources management, water and environmental monitoring, agriculture and forestry, mining, emergency management and infrastructure security. GAF is also one of the most experienced European service providers in the EU/ESA Copernicus programme, with many years of service implementation for the Copernicus Land Monitoring Service, the Emergency Management Service, and the Security and In-Situ Service Components.

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A number of further economic benefit studies were published over the last couple of months in 2022 within the Sentinels Benefits Study. Oil spill in the Mediterranean looks at how the CleanSeaNet (CSN) service operated by the European Maritime Safety Agency (EMSA) helps national authorities monitoring their waters.CSN uses Sentinel-1 and other data to identify potential oil slicks and possible polluters and, in a matter of minutes, transfer this information to the national competent authorities such as SASEMAR in Spain and Transport Malta. The increased risk of detection and successful prosecution is deterring ship’s captains from washing out their tanks and oil spills were reduced by up to 65% in the last 10 years.

Water Quality Management in Finland looks at how Sentinel-2 and Sentinel-3 data are being used to monitor water bodies in Finland. These measurements allow the environmental institute of Finland and regional environmental agencies, known as ELY Centres, to monitor the quality of water in lakes throughout their region to a degree that is not possible using traditional in-situ water sampling and testing. Using satellite data is especially helpful in a country like Finland where the large amount of water bodies would imply enormous associated costs for authorities should they have to use traditional monitoring methods across the whole country. Sentinel data therefore helps authorities to improve water quality at a lower cost, which in turn improves the quality of life for citizens, aids in the protection of biodiversity and helps to ensure environmental sustainability.

Next to these and other in-depth case studies, we published a visual summary report that provides a great overview of the learnings and conclusions we have been able to draw from our case studies so far. The report also gives a comprehensive picture of all the associated benefits derived from the use of Copernicus Sentinel data!

Besides our case analyses, we organised three sector workshops to engage with public sector stakeholders and to better understand the uptake of Sentinel data in these areas. The targeted sectors were road infrastructure management, forest management and water quality management. The workshop results can be accessed at our SEBS website!

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More and more often we see satellite images on media around the world. This is because they offer a clear and honest vision of the Earth to understand and observe events such as climate change, drought, floods, temperatures, volcanic activity. 

Social media has accustomed us to sharing and re-sharing photos and videos, making some places suddenly famous because they are 'instagrammable', can be replicated form anyone with some “special filter” to make each picture unique. Earth Observation satellite images have become equally acceptable in recent years, but maintaining a natural 'objectivity'. 

MEEO developed the ADAM platform to facilitate accessing a large amount of geospatial environmental data. Besides traditional commercial land scientific applications, ADAM turned to be the optimal tool to speed up the creation of media contents: satellite images, pollution information, climate data and trends can be easily extracted, elaborated within the platform and distributed to the media.

From remote sensing expertise to communication, MEEO started the activity of ADAM in the social media world with a small internal team: from 2018 to now the satellite images have travelled from Twitter, Instagram, Facebook, LinkedIn and recently also Mastodon. 

(heatwave in India and Pakistan (April, 29th 2022)

As seen by Copernicus Sentinel3 LST (Land Surface Temperature). The tweet got more than 6 Millions of Impressions and 118 K of Engagements. This image was used by climate activists, political personalities and a lot of media all over the World (ex. The Washington Post).

From the web to the classic media (Newspaper and Television Broadcast) the step is short!

In 2020 MEEO started a collaboration with the Italian National Broadcast RAI: since then a lot of satellite images have entered in the Italian’s houses. With different communication guidelines, contents are created for the regular weather reporting (in close cooperation with the RAI Meteo team), but also to explain phenomena such as volcanic eruptions, extreme events, drought, climate change, coastal erosion. It’s great to see how many common people can understand and are able to interpret satellite images that until few years ago were only pertinent to the scientific community. The use of Adam's images in the children's section of the television program ‘Green Meteo’, broadcast on RaiGulp, was a great success.

This growing communication activity of MEEO is made up of constant comparison, research and study. Performance results of the social media accounts are rewarding, as well as seeing the use of the products by the scientific community and the approach and growing interest by the general public, also by the children. 

ADAM is the optimal link between the large amount of data generated every day by Copernicus, ESA and NASA, and the media community.

( 3D view of the La Palma volcano - Sentinel-2 image true color enhanced with short wave infrared channel, September 30th 2021)

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UP42 has had an interesting journey since our founding in 2019, and 2022 was especially exciting. But before we dive into those updates, let’s start with a short introduction. UP42 is a platform and marketplace that provides API-first access to a wide range of geospatial data and analytics. We’re on a mission to simplify access to satellite imagery, expand the earth observation market, and make it easier than ever to access data and extract insights at scale. 

With this in mind, we launched the UP42 data platform, a one-stop-shop for all geospatial data needs, to overcome the challenges of ordering data from different providers. The UP42 catalog hosts archive data from providers such as Airbus, Capella Space, 21AT, Near Space Labs, and more. We’ve got optical, radar, and elevation data integrated into our platform. Here’s why this matters: 

● Instead of contacting or setting up accounts with a range of providers, which often means different compliance and maintenance needs, multiple contracts, and integrations, our users get one single interface (available via API, SDK, or console). Here, users can search for and order data, get price estimates and updates, and download their data, independent from data host capabilities. 

● Users can also activate automated status updates with webhooks, saving further time and effort. 

● The best part is: image previews are available for all products, and real-time requests in the catalog show which products are immediately available. 

It’s easy. Customers simply define order parameters (AOI, certain cloud coverage, start/end date) and integrate all the products they need. The same set of parameters

applies to all providers in an order, so adding a new product takes about 3 seconds: just enter the data product ID and the ID of the image needed for the order. This is especially important for projects that need a highly customized solution and a combination of different temporal, spectral, and spatial resolutions. Complete projects on time and within budget, further benefiting from an AOI-based pay-as-you-grow pricing model, with no volume commitments. 

And if a user can’t find the right archive data, they can task a satellite to capture the area needed. This gives the freedom to define exact parameters, collect the perfect image, and support informed decision-making. Last year, we introduced our streamlined tasking interface and brought all tasking actions together in one place. Whether placing a new tasking order or monitoring an existing request, the whole tasking process can now be managed within the platform. Below, we’ve broken down the tasking process into steps. 

● Place an order: choose a collection, draw an AOI, fill out the form, and submit ● View order status: view order AOI, status, and pending actions 

● Review feasibility: when the feasibility study is ready, review and choose from the options 

● Pay and activate: view and confirm the price to pay and activate the order ● Track progress and download your assets: when assets are ready, download directly from order details 

The UP42 tasking platform gives direct access to satellites from Airbus, Capella Space, ICEYE, and BlackSky, so users can benefit from different types of data and resolutions.

We’re always ready to process urgent requests. When dealing with budget constraints and a tight window, or simply needing to task satellites from different providers, UP42 can help. 

Acquired archive or tasked imagery can easily be processed externally, or even better: on our platform, where users can analyze data, enabling easy collaboration with team members. Data will be delivered to UP42’s cloud-based storage, where it’s available for further processing. Users can combine data from multiple providers, augment SAR with optical data and vise-versa, combine data with 3rd party analytics capabilities, or even bring their own data and algorithms. 
Data is also available for integration with other ecosystems, such as QGIS or Esri. Existing Esri users can immediately access UP42 storage and projects directly from their ArcGIS Pro account. 
And what does 2023 hold for us? Expect even more providers on our data and tasking platforms with Head Aerospace, Hexagon, and Satellogic, to name just a few. We’ll also be introducing an even better discovery experience on our catalog, along with multiple AOI support for tasking, STAC & one set of common geospatial metadata, processing capabilities for ARD, and much more.

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Aspia Space, the leading satellite data intelligence provider, today announces two new appointments as it expands its team to meet growing demand for its pioneering Earth observation data intelligence service.

New Director of AI division

Co-founder and managing partner Mike Smith joins Aspia Space in a permanent role as Director of its AI division.  With a background in applying deep learning techniques to astrophysics, earth observation and medicine, Mike is a Fellow of the Royal Astronomical Society and a twice former recipient of The Alan Turing Institute’s Enrichment Scheme.

Mike co-developed the ground-breaking AI algorithm and deep generative learning code that removes cloud cover from our Earth observation imagery with Aspia Space’s founder and managing partner Professor Jim Geach while working together at the University of Hertfordshire.

Mike Smith commented: “I’m eager to keep Aspia Space at the cutting-edge of AI research, and very excited to begin working full time with such a talented group of people.”

New Director of Product

Sarah Fairbairn joins Aspia Space as Director of Product. Sarah recently completed her Masters in Data Science with Artificial Intelligence at the University of Exeter and picked up the Bright Future Student Knowledge Exchange Award for her outstanding project work. Prior to studying for her Masters, Sarah held the roles of R & D Director and Product Discovery Manager at LumiraDx Care Solutions, a medtech software company.

Sarah Fairbairn commented: “I am delighted to be joining the amazing team at Aspia Space.  Earth observation is such an exciting area, and I am looking forward to working with the team to expand Aspia Space's data intelligence offerings to create even more impactful and insightful products."

Commenting on the appointments, co-founder and managing partner Professor Jim Geach said: “Mike has already played a huge role in co-developing our ClearSky service and I’m excited to be working with Mike again as we continue to develop cutting-edge AI solutions for our clients.”

“Sarah’s experience in Product research and development and her passion for data science and finding simple solutions to complex challenges will be a huge asset to the business as we continue to scale over the coming months.”

About Aspia Space:

Founded in 2021 and based in Cornwall, UK, Aspia Space is a company pushing the limits of Earth Observation data. With over two decades of expertise in image and spectral analysis rooted in astrophysics, and bringing to bear cutting-edge deep learning techniques, Aspia Space is innovating in downstream Earth Observation analysis. We are passionate about deriving meaning from remote sensing data, unlocking value and gaining insights that others cannot. This allows our customers to make better decisions based on reliable intelligence, leading to improvements in efficiency, productivity and sustainability.