SciTransfer
Organization

MUNDIALIS GMBH & CO KG

German geospatial SME providing Earth observation data infrastructure and applied spatial analytics for agriculture, health, and environmental monitoring.

Technology SMEenvironmentDESMENo active H2020 projects
H2020 projects
3
As coordinator
0
Total EC funding
€378K
Unique partners
47
What they do

Their core work

Mundialis is a German geospatial technology SME specializing in Earth observation data processing, geospatial analysis, and large-scale environmental data infrastructure. They build tools and platforms that make satellite imagery and environmental datasets accessible for applications ranging from agricultural policy modeling to disease outbreak surveillance. Their technical strength lies in connecting remote sensing and geospatial computing backends with user-facing analytical tools, serving as a technology provider within research consortia tackling complex environmental and health challenges.

Core expertise

What they specialise in

Geospatial modeling for agricultural policysecondary
1 project

Contributed to BESTMAP, which uses agent-based and biophysical modeling to assess rural policy impacts on ecosystem services.

Epidemic intelligence and environmental health monitoringemerging
1 project

Participated in MOOD, applying big data and geospatial approaches to monitor outbreak events linked to environmental and climate changes.

Big data processing and cloud computing for geodataprimary
2 projects

Both openEO and MOOD require large-scale data processing capabilities, indicating sustained expertise in geodata cloud infrastructure.

Evolution & trajectory

How they've shifted over time

Early focus
Earth observation infrastructure
Recent focus
Applied geospatial health and agriculture

Mundialis entered H2020 in 2017 focused on core Earth observation infrastructure through openEO, building open-source tools for satellite data access. From 2019 onward, they shifted toward applying their geospatial capabilities to domain-specific challenges — agricultural policy modeling (BESTMAP) and disease surveillance (MOOD). This trajectory shows a company moving from pure infrastructure provision toward applied geospatial intelligence in food systems and public health.

Mundialis is expanding from EO data infrastructure into applied domains like epidemic intelligence and agri-environmental modeling, making them increasingly relevant for interdisciplinary projects that need geospatial analytics as a foundation.

Collaboration profile

How they like to work

Role: infrastructure_providerReach: European16 countries collaborated

Mundialis operates exclusively as a participant, never leading consortia — consistent with their role as a specialized technology provider embedded in larger research teams. With 47 unique partners across just 3 projects, they work in large consortia (averaging ~16 partners per project), suggesting comfort with complex multi-partner coordination. Their broad partner base indicates they are sought after for their specific technical capabilities rather than relying on repeat partnerships.

Despite only 3 projects, Mundialis has built a remarkably wide network of 47 unique partners across 16 countries, reflecting their role as a versatile geospatial technology provider that integrates well into diverse European consortia.

Why partner with them

What sets them apart

Mundialis occupies a distinctive niche as a geospatial SME that bridges the gap between raw Earth observation data and domain-specific applications in agriculture and health. Unlike pure research groups, they bring production-grade open-source software engineering to consortia. Their ability to contribute meaningfully to projects as different as satellite data APIs, agricultural policy simulation, and disease surveillance demonstrates unusual versatility for a small company.

Notable projects

Highlights from their portfolio

  • openEO
    Foundational open-source project creating a standardized API for Earth observation cloud platforms, with broad community adoption beyond the project itself.
  • MOOD
    Largest funding share (EUR 212,815) and an ambitious cross-domain effort linking environmental/climate data to infectious disease outbreak monitoring.
Cross-sector capabilities
Food & agriculture (geospatial policy modeling)Health (epidemic intelligence, environmental disease drivers)Space (Earth observation data processing)Digital (open-source cloud platforms, big data)
Analysis note: Profile based on only 3 projects. The company's core geospatial identity is strongly supported by the openEO project, but their domain expertise in health and agriculture may reflect consortium needs rather than deep in-house specialization. No website URL was available in the data to verify current service offerings. Keywords like 'GRASS GIS' and 'actinia' are inferred from the company's known open-source ecosystem but not explicitly present in project data.