Suitability of mixed-satellite-sensor derived 3D data for rapid urban population estimation in crisis situations.

Short Description

Starting point / motivation

With more than half of the world’s population living in towns and cities, urban areas get more and more into the focus of humanitarian relief organisations such as ICRC, Médecins sans Frontières (MSF), or SOS Children’s Villages.

A key information required for almost any intervention is an estimation the population numbers for the towns and cities where these organisations operate in. As census data are usually not available or outdated, population numbers have to be estimated by alternative methods such as remote sensing.

Contents and goals

Z_GIS has provided geospatial information products on population numbers based on Earth observation data mostly of refugee and IDP (internally displaced person) camps to MSF since 2011. In the currently ongoing FFG-ASAP 12 project EO4HumEn+, Z_GIS and partners explore the population estimation in urban areas, next to a wide range of further EO-applications for the humanitarian sector.

Experience showed that stereo optical imagery is often not available in the image archives of the data providers, and can often not be acquired rapidly, because of a high competition for the satellites, especially over crisis regions. On the other hand, single images of different sensors are – especially in disaster situations – often available or can be tasked much easier.


This exploratory project aims at overcoming this limitation by testing the fitness-for-purpose of digital surface models (DSM) derived from image pairs of mixed satellite sensors, for example a combination of WorldView and Pléiades images from different acquisition dates, for rapid population estimation (mono-temporal) as well as 3D change analysis (multi-temporal, e.g. damage detection in the context of natural disasters).

Specifically, the project will test the limitations in terms of differences in image resolution, observation geometry, lighting conditions, and seasons. While the quality of these DSMs is expected to be lower than that of models derived from images acquired specifically for DSM production, their usability for population estimation is unclear.

Therefore, also the modelling of population estimations in sparse data situations is envisaged. Urban population estimation can benefit greatly from the use of 3D data to assist the classification of buildings vs sealed surfaces, and to allow an estimation of the 3D living space by inclusion of building heights. Population estimation also requires additional knowledge or assumptions on the use of the buildings.

This project will define realistic test scenarios for the availability of such information, from very limited spot checks to detailed information as available in cadastres or OpenStreetMap (in Europe). 

The DSMs will be compared to Airborne Laser Scanner data and DSMs from dedicated optical stereo images; the population estimations will be validated against (rastered) census data. Salzburg (Austria) and Port-au-Prince (Haiti) will be used as test areas, for which data from previous projects are available. Aleppo (Syria) will be used as a third test case. DLR has ample experience with the production of DSMs, while UNIGE-GRID and AIT have a proven track record in the estimation of population numbers.

Expected results

The project will foster the scientific cooperation between Austrian and Swiss research institutions as well as the DLR. It will further increase the visibility of Austria as an innovation leader in the field of EO-services for the humanitarian sector, and it will represent a contribution of Switzerland to global challenges, in line with the Swiss Space Implementation Plan (SSIP).

Project Partners


Universität Salzburg Fachbereich Geoinformatik – Z_GIS

Project partner

  • Austrian Institute of Technology GmbH (AIT)
  • Deutsches Zentrum f. Luft- und Raumfahrt e.V. (DLR)
  • University of Geneva/GRID-Geneva (UNIGE/GRID)

Contact Address

Universität Salzburg 
Department of Geoinformatics – Z_GIS
Dr. Lorenz Wendt PhD
Hellbrunnerstrasse 34
A-5020 Salzburg