DLR - Earth Observation Center - Projekte: Data Science

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Anwendungen und Projekte: Data Science

ELK (EmissionsLandKarte) (12/2021 – 12/2024)

This DLR impulse project deals with the compilation of consistent emission inventories of gas and particles for the transportation sector, the energy production and related sectors for 2019. The inventories will be made available to both DLR internal as well as to external users and compatible to other inventories generated to support the Intergovernmental Panel on Climate Change (IPCC).

	EVOLAND (01/2023 – 12/2025) 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.
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	MultiMiner (01/2023 – 06/2026) The Multi-source and multi-scale Earth Observation and novel machine learning methods for mineral exploration and mine site monitoring (MultiMiner) project develops novel data processing algorithms for efficient utilization of EO technologies for mineral exploration and mine site monitoring.
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	SURF (07/2022 – 06/2025) (07/2022 – 06/2025) In the last decade, flood events have cost more than US$100 billion, with more than 100,000 people being killed, and 1 billion left without a home worldwide. A quick response can substantially reduce the damage caused by floods.
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RepreSent (04/2022 – 05/2023)

Supervised Deep Learning techniques in EO often depend on labelled data. It would be expensive and time-consuming to obtain such labels for the massive amount of EO data that the Sentinel satellites of the Copernicus program have been gathering. In order to effectively exploit this abundant pool of data, the project “RepreSent” will therefore harness the power of non-supervised learning.

	WeMonitor (04/2022 – 04/2025) Detecting landslides and illegal waste dumps showcase a chance for Earth observation missions. This can be done most effectively with Artificial Intelligence methods that are trained to detect spatio-temporal surface anomalies – a task that also brings challenges.
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ML4Earth (01/2022 – 12/2024)

The Technical University of Munich (TUM) has been working closely with IMF in the past years to establish European’s largest research capacity when it comes to applying Machine Learning in Earth observations. Starting in 2022, we will launch a national ML4Earth center of excellence with high visibility. We will tackle fundamental methodical challenges in AI4EO at the international level and their application to the European mission of a Digital Twin Earth.

IMonitor (10/2021 – 09/2024)

In recent years, Earth observation (EO) has entered the era of Big Data. Satellite imaging, combined with other data sources (e.g. financial trends, soil information, weather patterns), currently enables us to monitor the Earth almost on a daily basis. Frequent revisits of the same spot on the ground have opened up the possibility for fine-level, task-specific change detection monitoring and understanding applications.

AI4GNSS-R (10/2021 – 09/2024)

The high level of signals transmissivity during severe weather as well as the low-cost, low-mass and lowpower GNSS-R receivers operating on low-Earth orbiting satellites, are some of the advantageous characteristics of this technique. Conventional GNSS-R retrieval algorithms rely on the parametric regression approaches inverting observables to the geophysical data products. These models are developed based on simplifying assumptions due to the complexity in the physics of signals scattering from ocean surface.

AI4EO (06/2021 – 08/2024)

08 July 2021
Agriculture is a crucial factor in climate change. It is not only a cause, but at the same time also massively affected by such changes. Adaption in the agricultural sector is vital. The project ‘Artificial Intelligence for Earth Observation’ (AI4EO) has announced a competition to obtain with AI methodology more precise agriculture data based on data from Europe’s Sentinel satellites.

OpenSearch@DLR 2.0(01/2023 – 12/2026)

Data provide the raw materials for research, innovation and business in the 21st century. Open, unbiased and transparent access to information is therefore a basic prerequisite for the free development of a digital society. However, internet searches have become highly monopolised. This has an impact on access to information and knowledge, hinders scientific research and industrial activities, and is detrimental to Europe’s digital sovereignty.

	AI-CORE (11/2019 – 06/2023) Im November 2019 startet mit AI4CORE ein von der Helmholtz Gesellschaft gefördertes Projekt zur Implementierung von Methoden der künstlichen Intelligenz in der Kryosphärenforschung.
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Helmholtz AI (04/2019 – 06/2024)

In the Helmholtz Artificial Intelligence Cooperation Unit (HAICU) the Helmholtz Association of German Research Centres is setting up an innovative network for applied artificial intelligence (AI). HAICU will design, implement, and distribute AI methodologies for such purposes as analysing complex climate, energy, transportation and health systems. In this network the German Aerospace Center (DLR) in Oberpfaffenhofen near Munich was awarded the contract for the "HAICU Local" unit in the aviation, aerospace and transportation research fields.

MuDS (03/2019 – 02/2025)

While the volume, complexity, and real-time requirements of scientific data across all areas of research - in particular in the data-rich Helmholtz environments - are strongly increasing, experts in data science are rare or do not even exist. We therefore face an urgent need for training the next generation of scientists at the interface of data science and various domain sciences.

So2Sat (2017 – ...)

In 2050, it is expected that three fourths of the world’s population will live in metropolises. This transition will inherently alter the physical dimensions and configurations of cities at all scales, which is a fact presenting an enormous challenge to urban planners and logisticians. Yet, our understanding of urbanization at these scales is primarily based on United Nations population figures, but these statistics do not provide information on the distribution, pattern, and evolution of the built environment. For example, the knowledge of the spatial evolution as well as the posulation density of informal settlements such as slums or refugee settlements in many mega-cities is far from sufficient for a sustainable planning.

QSP Big-Data-Plattform (6/2018 – 12/2021)

The focus of the project "Big Data Platform" (BDP) is a methodical "data science" platform for cross-focus analyses of heterogeneous, distributed data. For this purpose, Big Data and cloud computing technologies are addressed. In addition to its use in DLR's big data applications, the platform supports users from industry, government and the public in building complex value chains and creates new opportunities for action and planning, for example in the areas of mobility, environment and security.

Anwendungen und Projekte: Data Science

ELK (EmissionsLandKarte) (12/2021 – 12/2024)

EVOLAND (01/2023 – 12/2025)

MultiMiner (01/2023 – 06/2026)

SURF (07/2022 – 06/2025)