EO4HumEn+

The number of forcibly uprooted people rises every year, reaching 59.5 million at the end of 2014. Even higher records can be expected for 2015. Humanitarian action as one part of the response to this situation relies on targeted, up-to date and reliable information to handle this challenge.

Z_GIS has been developing Earth-observation (EO) based solutions for the humanitarian sector within the currently ongoing FFG-ASAP 9 project EO4HumEn, and regularly provides geospatial information products to Médecins sans Frontières (MSF).

During the close collaboration with the humanitarian sector we learned the following lessons:

1. A large number of refugees and internally displaced persons (IDPs) worldwide lives in camps as addressed by these current projects, but more than half of the world’s refugee population under UNHCR mandate lives in urban settings.
2. Monitoring of environmental resources like groundwater or wood reserves is a concern of humanitarian actors, but the current EO-based solutions are seldom automated and therefore do not tap the full potential of the immense amounts of data provided by the Sentinels and other satellite missions.
3. The increasing demand for a diversified product portfolio and the growing user community allows for a market orientation of the service.

The aim of the proposed project EO4HumEn+ therefore is to extend the established service portfolio to the diversifying needs of the humanitarian community (including ICRC, the Austrian Red Cross, SOS Children’s Villages, MSF and Groundwater Relief), pursuing, inter alia, the following extensions as compared to its precursor project: improving the capabilities of EO methods to provide realistic population estimations in urban areas, and implementing highly automated routines to better exploit the data stream of the Sentinel missions.

The project is supported by a strong commitment in efforts and technical contribution from DLR. Specifically, the project aims at:

• fully automated pre-classification of built-up areas
• semi-automated identification of informal settlements vs. existing settlements
• object-based DTM generation for extraction of 3-D living space in urban areas
• development of an integrated OBIA approach for population estimation in urban areas
• integration of template matching into OBIA dwelling extraction routines, including the detection of destroyed tukuls
• implementation of a hybrid image understanding strategy using knowledge-based pre-classifiers for inland water body monitoring and land use / land cover
• demonstration of the potential of Sentinel-1-based DInSAR for groundwater management in humanitarian crisis situations

The innovative character of this 24-month project is manifold. Technologically, the combination of automated pre-processing, pre-classification, and object-based techniques allows for a large degree of knowledge-based, transferable information extraction routines.

From a societal point of view, this service development responds to one of the biggest global societal challenges today, and provides tools to deploy humanitarian action in a more efficient, reflected, and effective way.

The recently founded spin-off Spatial Services GmbH accompanies the project by providing marketable technical solutions for the generated products to assure service provision on a long-term perspective.

Coordinator

University of Salzburg Department of Geoinformatics - Z_GIS

Project partner

• Deutsches Zentrum für Luft- und Raumfahrt e.V. (DLR)
• Austrian Red Cross
• Spatial Services GmbH

University of Salzburg Department of Geoinformatics - Z_GIS
Dr. Stefan Lang
Hellbrunnerstrasse 34
A-5020 Salzburg