Emergency situations occur unpredictably and cause individuals and organizations to shift their focus and attention immediately to deal with the situation. When disasters become large scale, all the limitations resulting from a lack of integration and collaboration among all the involved organizations begin to be exposed and further compound the negative consequences of the event. Often in large-scale disasters the people who must work together have no history of doing so; they have not developed a trust or understanding of one another's abilities, and the totality of resources they each bring to bear have never before been exercised. As a result, the challenges for individual or group decision support systems (DSS) in emergency situations are diverse and immense. In this contribution, we present recent advances in this area and highlight important challenges that remain.

The Horizon 2020 interim evaluation (2017) indicates a steep increase in citizen engagement in European Union Citizen Science (CS) projects, with less than 1% in budgetary terms and minimal influence. Research findings attribute weak CS influence to the restriction of citizen actions to data collection, with minimal or no engagement in co-design, co-creation, data analysis, and elucidation of results. We design a participatory GIS and CS methodology aimed at engaging the citizens in the entire Earth Observation (EO) project cycle. The methodology also seeks to address previous CS project challenges related to data quality, data interoperability, citizen-motivation, and participation. We draw the high-level requirements from the SENDAI framework of action and the three pillars of active citizen engagement, as enshrined in Principle 10 of the Rio Declaration and the Aarhus Convention. The primary input of the methodology is the Haklay (2018) approach for participatory mapping and CS, and the Reed (2009) stakeholder analysis framework. The proposed methodology comprises of three main parts: system analysis, stakeholder analysis, and a six-step methodology. We designed the six-step methodology using an iterative and flexible approach, to take account of unforeseen changes. Future research will focus on implementing the methodology and evaluating its effectiveness in the Solotvyno Saltmine case study in Ukraine.

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In the immediate aftermath of a disaster, local and international aid organisations deploy to deliver life-saving aid to the affected population. Yet pre-disaster road maps and road transportation models do not capture disruptions to the transportation network caused by the disaster or the dynamic changes of the situation, resulting in uncertainty and inefficiency in planning and decision-making. The integration of data in near real time on the status of the road infrastructure in the affected region can help aid organisations to keep track of the rapidly shifting conditions on the ground and to assess the implications for their logistics planning and operations. In this paper, we present a rapid graph-theoretical reachability information system based on a combination of OpenStreetMap and open humanitarian data. The system supports logistics planning in determining road access to affected communities. We demonstrate the results of our approach in a case study on the 2018 earthquake in Papua New Guinea. Our findings show the reachability of affected communities depending on the actual status of the road network, allowing for the prioritization of targeted locations and the identification of alternative routes to get there.