Tag Archives: JRC

Humanitarian UAV Missions During Balkan Floods

The Balkans recently experienced the heaviest rains in 120 years of recorded weather measurements, causing massive flooding and powerful landslides. My colleague Haris Balta, a certified UAV pilot with the European Union’s ICARUS Unmanned Search & Rescue Project (and a member of the Humanitarian UAV Network, UAViators), was deployed to Bosnia to support relief efforts. During this time, another colleague, Peter Spruyt from the European Commission (DG JRC), was also deployed to the region to carry out a post-disaster needs assessment using UAVs.

Image: Flood in Bosnia and Herzegovina

Haris, who also works at the intersection of robotics and demining, was asked by the Government of the Federation of Bosnia and Herzegovina to identify the location of mines displaced due to the major flooding and mudslides. As it turns out, some mines were displaced as far as 23 kilometers. When the flood waters subsided and villagers returned, most were unaware of this imminent danger. Haris used a rotary-wing UAV (the quadcopter pictured below) and logged some 20 flights (both manual and autonomous) at more than a dozen locations.

ICARUS Quadcopter

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The purpose of these flights was to capture imagery that could be used to identify displaced land mines and to analyze the effects of landslides on other explosive remnants of war. Haris and team created 3D maps from the imagery and used geo-statistical modeling to try and determine in which direction land mines may have been displaced. The imagery also provided valuable information on dyke-breaches and other types of infrastructure damage.

Meanwhile, my colleague Peter from DG JRC (who is also a member of the Humanitarian UAV Network) flew a light fixed-wing UAV in five locations to support damage and needs assessments in close collaboration with the World Bank and the UN. According to Peter, both local and regional authorities were very supportive. Some of the resulting images and models of landslide areas are depicted below, courtesy of DG JRC (click to enlarge).


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I just introduced Peter and Haris since they weren’t aware of each other’s respective efforts. If you’re participating in humanitarian UAV missions, please consider sharing you work with the Humanitarian UAV Network by posting a quick summary of your mission to the Network’s Operations page; even a one-sentence description will go a long way to facilitate information sharing.


See Also:

  • Humanitarians in the Sky: Using UAVs for Disaster Response [link]
  • Crisis Map of UAV/Aerial Videos for Disaster Response [link]
  • Using UAVs for Search & Rescue [link]
  • Debrief: UAV/Drone Search & Rescue Challenge [link]
  • Crowdsourcing Analysis of UAV Imagery for Search/Rescue [link]
  • Check-List for Flying UAVs in Humanitarian Settings [link]

How Crowdsourced Data Can Predict Crisis Impact: Findings from Empirical Study on Haiti

One of the inherent concerns about crowdsourced crisis information is that the data is not statistically representative and hence “useless” for any serious kind of statistical analysis. But my colleague Christina Corbane and her team at the European Commission’s Joint Research Center (JRC) have come up with some interesting findings that prove otherwise. They used the reports mapped on the Ushahidi-Haiti platform to show that this crowdsourced  data can help predict the spatial distribution of structural damage in Port-au-Prince. The results were presented at this year’s Crisis Mapping Conference (ICCM 2010).

The data on structural damage was obtained using very high resolution aerial imagery. Some 600 experts from 23 different countries joined the World Bank-UNOSAT-JRC team to assess the damage based on this imagery. This massive effort took two months to complete. In contrast, the crowdsourced reports on Ushahidi-Haiti were mapped in near-real time and could “hence  represent an invaluable early indicator on the distribution and on the intensity of building damage.”

Corbane and her co-authors “focused on the area of Port-au-Prince (approximately 9 by 9 km) where a total of 1,645 messages have been reported and 161,281 individual buildings have been identified, each classified into one of the 5 different damage grades.” Since the focus of the study is the relationship between crowdsourced reports and the intensity of structural damage, only grades 4 and 5 (structures beyond repair) were taken into account. The result is a bivariate point pattern consisting of two variables: 1,645 crowdsourced reports and 33,800 damaged buildings (grades 4 and 5 combined).

The above graphic simply serves as an illustrative example of the possible relationships between simulated distributions of SMS and damaged buildings. The two figures below represent the actual spatial distribution of crowdsourced reports and damaged buildings according to the data. The figures show that both crowdsourced data and damage patterns are clustered even though the latter is more pronounced. This suggests that some kind of correlation exists between the two distributions.

Corbane and colleagues therefore used spatial point pattern process statistics to better understand and characterize the spatial structures of crowdsourced reports and building damage patterns. They used the Ripley’s K-function which is often considered “the most suitable and functional characteristic for analyzing point processes.” The results clearly demonstrate the existence of statistically significant correlation between the spatial patterns of crowdsourced data and building damages at “distances ranging between 1 and 3 to 4 km.”

The co-authors then used the marked Gibbs point process model to “derive the conditional intensity of building damage based on the pairwise interactions between SMS [crowdsourced reports] and building damages.” The resulting model was then used to compute the predicted damage intensity values, which is depicted below with the observed damage intensity.

The figures clearly show that the similarity between the patterns exhibited by the predictive model and the actual damage pattern is particularly strong. This visual inspection is confirmed by the computed correlation between the observed and predicted damage patterns shown below.

In sum, the results of this empirical study demonstrates the existence of a spatial dependence between crowdsourced data and damaged buildings. The results of the analysis also show how statistical interactions between the patterns of crowdsourced data and building damage can be used for modeling the intensity of structural damage to buildings.

These findings are rather stunning. Data collected using unbounded crowdsourcing (non-representative sampling) largely in the form of SMS from the disaster affected population in Port-au-Prince can predict, with surprisingly high accuracy and statistical significance, the location and extent of structural damage post-earthquake.

The World Bank-UNOSAT-JRC damage assessment took 600 experts 66 days to complete. The cost probably figured in the hundreds of millions of dollars. In contrast, Mission 4636 and Ushahidi-Haiti were both ad-hoc, volunteer-based projects and virtually all the crowdsourced reports used in the study were collected within 14 days of the earthquake (most within 10 days).

But what does this say about the quality/reliability of crowdsourced data? The authors don’t make this connection but I find the implications particularly interesting since the actual content of the 1,645 crowdsourced reports were not factored into the analysis, simply the GPS coordinates, i.e., the meta-data.

JRC: Geo-Spatial Analysis for Global Security

The European Commission’s Joint Research Center (JRC) is doing some phenomenal work on Geo-Spatial Information Analysis for Global Security and Stability. I’ve had several meetings with JRC colleagues over the years and have always been very impressed with their projects.

The group is not very well known outside Europe so the purpose of this blog post is to highlight some of the Center’s projects.

  • Enumeration of Refugee Camps: The project developed an operational methodology to estimate refugee populations using very high resolution (VHR) satellite imagery. “The methodology relies on a combination of machine-assisted procedures, photo-interpretation and statistical sampling.”


  • Benchmarking Hand Held Equipment for Field Data Collection: This project tested new devices for the collection for geo-referenced information. “The assessment of the instruments considered their technical characteristics, like the availability of necessary instruments or functionalities, technical features, hardware specifics, software compatibility and interfaces.”


  • GEOCREW – Study on Geodata and Crisis Early Warning: This project analyzed the use of geo-spatial technology in the decision-making process of institutions dealing with international crises. The project also aimed to show best practice in the use of geo-spatial technologies in the decision-making process.
  • Support to Peacekeeping Operations in the Sudan: Maps are generally not available or often are out of date for most of the conflict areas in which peacekeping personnel is deployed,  This UNDPKO Darfur mapping initiative aimed to create an alliance of partners that addressed this gap and shared the results.


  • Temporary Settlement Analysis by Remote Sensing: The project analyzes different types of refugee and IDP settlements to identify single structures inside refugee settlements. “The objective of the project is to establish the first comprehensive catalog of image interpretation keys, based on last-generation satellite data and related to the analysis of transitional settlements.”

JRC colleagues often publish papers on their work and I highly recommend having a look at this book when it comes out in June 2009:


Patrick Philippe Meier

Job: Satellite Imagery & Conflict Specialist

The European Union’s Information Support for Effective and Rapid External Action (ISFEREA) is looking for a conflict specialist post-doc researcher. I haven’t posted job openings before but this one from my colleagues at the Joint Research Center (JRC) is especially relevant to iRevolution’s focus.

Background: ISFEREA develops techniques for automatic image processing of digital images acquired via satellite platforms as well as methodologies to explore the links between conflict risk and the exploitation (and degradation) of natural resources such as minerals. In particular, very high resolution (VHR) sensors with meter and sub-meter spatial resolution are being tested for multi-spectral and multi-temporal analysis.

Applications fields are related to human security, conflict resource monitoring, post-disaster damage assessment, and analysis of human settlements, including temporary settlements and refugee camps

The candidate will conduct research on conflict risk modelling and links between natural resources and conflicts. She/he would contribute to:

  1. Collecting, organizing and analyzing all available data sources on conflicts, political tensions/crises, and some types of natural resources;
  2. Developing modelling scenarios and applying them to study the relationships between natural resources and armed conflicts as well as political instability.

The position presumes the will and the interest of the candidate to publish the results of his/her work in peer reviewed publications.

Requirements: University degree in political or social sciences; PhD degree in similar discipline or 5 years of relevant work experience, especially in conflict studies; good knowledge of at least one of the following three regions: African Great Lakes, Horn of Africa and Central Asia; good oral and written communication skills in English; team player and collaborative, proactive in research, capacity to learn and adaptability to stress.

Duration: 36 months

Applications Due: before 11 Jan, 2009 – 23:59:59 CET

Please follow this link for further information.

Patrick Philippe Meier