Collaborative damage mapping for emergency response : the role of Cognitive Systems Engineering

Remote sensing is increasingly used to assess disaster damage, traditionally by professional image analysts. A recent alternative is crowdsourcing by volunteers experienced in remote sensing, using internet-based mapping portals. We identify a range of problems in current approaches, including how volunteers can best be instructed for the task, ensuring that instructions are accurately understood and translate into valid results, or how the mapping scheme must be adapted for different map user needs. The volunteers, the mapping organizers, and the map users all perform complex cognitive tasks, yet little is known about the actual information needs of the users. We also identify problematic assumptions about the capabilities of the volunteers, principally related to the ability to perform the mapping, and to understand mapping instructions unambiguously. We propose that any robust scheme for collaborative damage mapping must rely on Cognitive Systems Engineering and its principal method, Cognitive Task Analysis (CTA), to understand the information and decision requirements of the map and image users, and how the volunteers can be optimally instructed and their mapping contributions merged into suitable map products. We recommend an iterative approach involving map users, remote sensing specialists, cognitive systems engineers and instructional designers, as well as experimental psychologists

Extraction of flood-modelling related base-data from multisource remote sensing imagery

Flooding is one of the most destructive natural hazards, accounting for over a third of all disaster damage worldwide. In particular in less developed countries (LDCs) this is typically attributed to poor planning, lack of warning systems and limited awareness of the hazard. A number of flood risk models have been developed, but have as yet contributed little to mapping and quantifying the risk in LDCs, for several reasons. In addition to limited human and technical capacity, these models require considerable amounts of current spatial information that is widely lacking, such as landcover, elevation and elements at risk basedata. Collecting those with ground-based methods is difficult, but remote sensing technologies have the potential to acquire them economically. To account for the variety of required information, data from different sensors are needed, some of which may not be available or affordable. Therefore, data interchangeability needs to be considered. Thus we test the potential of high spatial resolution optical imagery and laser scanning data to provide the information required to run such flood risk models as SOBEK. Using segmentation-based analysis in eCognition, Quickbird and laser scanning data were used to extract building footprints as well as the boundaries of informal settlements. Additionally, a landcover map to provide roughness values for the model was derived from the Quickbird image. These basedata were used in model simulations to assess their actual utility, as well as the sensitivity of the model to variations in basedata quality. The project shows that existing remote sensing data and image analysis methods can match the input requirements for flood models, and that, given the unavailability of one dataset, alternative images can fill the gap.</p

Finite size effects on the phase diagram of a binary mixture confined between competing walls

A symmetrical binary mixture AB that exhibits a critical temperature T_{cb} of phase separation into an A-rich and a B-rich phase in the bulk is considered in a geometry confined between two parallel plates a distance D apart. It is assumed that one wall preferentially attracts A while the other wall preferentially attracts B with the same strength (''competing walls''). In the limit $D\to \infty$, one then may have a wetting transition of first order at a temperature T_{w}, from which prewetting lines extend into the one phase region both of the A-rich and the B-rich phase. It is discussed how this phase diagram gets distorted due to the finiteness of D% : the phase transition at T_{cb} immediately disappears for D<\infty due to finite size rounding, and the phase diagram instead exhibit two two-phase coexistence regions in a temperature range T_{trip}<T<T_{c1}=T_{c2}. In the limit D\to \infty T_{c1},T_{c2} become the prewetting critical points and T_{trip}\to T_{w}. For small enough D it may occur that at a tricritical value D_{t} the temperatures T_{c1}=T_{c2} and T_{trip} merge, and then for D<D_{t} there is a single unmixing critical point as in the bulk but with T_{c}(D) near T_{w}. As an example, for the experimentally relevant case of a polymer mixture a phase diagram with two unmixing critical points is calculated explicitly from self-consistent field methods