Build human-in-the-loop intelligent systems for the geolocation of social media images in natural disasters

Social media generate large amounts of almost real-time data which can turn out extremely valuable in an emergency situation, specially for providing information within the first 72 hours after a disaster event. Despite there is abundant state-of-the-art machine learning techniques to automatically classify social media images and some work for geolocating them, the operational problem in the event of a new disaster remains unsolved.
Currently the state-of-the-art approach for dealing with these first response mapping is first filtering and then submitting the images to be geolocated to a crowd of volunteers [1], assigning the images randomly to the volunteers.

The project is aimed at leveraging the power of crowdsourcing and artificial intelligence (AI) to assist emergency responders and disaster relief organizations in building a damage map from a zone recently hit by a disaster.

Specifically, the project will involve the development of a platform that can intelligently distribute geolocation tasks to a crowd of volunteers based on their skills. The platform will use machine learning to determine the skills of the volunteers based on previous geolocation experiences.

Thus, the project will concentrate on two different tasks:
• Profile Learning. Based on the previous geolocations of a set of volunteers, learn a profile of each of the volunteers which encodes its geolocation capabilities. This profiles should be unterstood as competency maps of the volunteer, representing the capability of the volunteer to provide an accurate geolocation for an image coming from a specific geographical area.
• Active Task Assigment. Use the volunteer profiles efficiently in order to maximize the geolocation quality while maintaining a fair distribution of geolocation tasks among volunteers.

On a first stage we envision an experimental framework with realistically generated artificial data, which acts as a feasibility study. This will be published as a paper in a major conference or journal. Simultaneously we plan to integrate both the profile learning and the active task assignment with the crowdnalysis library, a software outcome of our previous micro-project. Furthermore, we plan to organize a geolocation workshop to take place in Barcelona with participation from the JRC, University of Geneva, United Nations, and IIIA-CSIC.

In the near future, the system will generate reports and visualizations to help these organizations quickly understand the distribution of damages. The resulting platform could enable more efficient and effective responses to natural disasters, potentially saving lives and reducing the impact of these events on communities.
The microproject will be developed by IIIA-CSIC and the University of Geneva. The micro project is also of interest to the team lead by Valerio Lorini at the Joint Research Center of the European Commission @ Ispra, Italy, who will most likely attend the geolocation workshop which we will be putting forward.

The project is in line with "Establishing Common Ground for Collaboration with AI Systems (WP 1-2)", because it is a microproject that " that seeks to provide practical demonstrations, tools, or new theoretical models for AI systems that can collaborate with and empower individuals or groups of people to attain shared goals" as is specifically mentioned in the Call for Microprojects.

The project is also in line with "Measuring, modeling, predicting the individual and collective effects of different forms of AI influence in socio-technical systems at scale (WP4)" since it ecomprises the design of a human-centered AI architectures that balance individual and collective goals for the task of geolocation.

[1] Fathi, Ramian, Dennis Thom, Steffen Koch, Thomas Ertl, and Frank Fiedrich. “VOST: A Case Study in Voluntary Digital Participation for Collaborative Emergency Management.” Information Processing & Management 57, no. 4 (July 1, 2020): 102174.


– Open source implementation of the volunteer profiling and consensus geolocation algorithms into the crowdnalysis library.
– Paper with the evaluation of the different geolocation consensus and active strategies for geolocation
– Organization of a one day workshop with United Nations, JRC, University of Geneva, CSIC

Project Partners

  • Consejo Superior de Investigaciones Científicas (CSIC), Jesus Cerquides
  • University of Geneva, Jose Luis Fernandez Marquez

Primary Contact

Jesus Cerquides, Consejo Superior de Investigaciones Científicas (CSIC)