Abstract:In this study, a classification and performance evaluation framework for the recognition of urban patterns in medium (Landsat ETM, TM and MSS) and very high resolution (WorldView-2, Quickbird, Ikonos) multi-spectral satellite images is presented. The study aims at exploring the potential of machine learning algorithms in the context of an object-based image analysis and to thoroughly test the algorithm's performance under varying conditions to optimize their usage for urban pattern recognition tasks. Four classification algorithms, Normal Bayes, K Nearest Neighbors, Random Trees and Support Vector Machines, which represent different concepts in machine learning (probabilistic, nearest neighbor, tree-based, function-based), have been selected and implemented on a free and open-source basis. Particular focus is given to assess the generalization ability of machine learning algorithms and the transferability of trained learning machines between different image types and image scenes. Moreover, the influence of the number and choice of training data, the influence of the size and composition of the feature vector and the effect of image segmentation on the classification accuracy is evaluated.

In this study, we investigated the possibility to improve a new behavioural bioassay (Swimming Speed Alteration test-SSA test) using larvae of marine cyst-forming organisms: e.g. the brine shrimp Artemia sp. and the rotifer Brachionus plicatilis. Swimming speed was investigated as a behavioural end-point for application in ecotoxicology studies. A first experiment to analyse the linear swimming speed of the two organisms was performed to verify the applicability of the video-camera tracking system, here referred to as Swimming Behavioural Recorder (SBR). A second experiment was performed, exposing organisms to different toxic compounds (zinc pyrithione, Macrotrol MT-200, and Eserine). Swimming speed alteration was analyzed together with mortality. The results of the first experiment indicate that SBR is a suitable tool to detect linear swimming speed of the two organisms, since the values have been obtained in accordance with other studies using the same organisms (3.05 mm s(-1) for Artemia sp. and 0.62 mm s(-1) for B. plicatilis). Toxicity test results clearly indicate that swimming speed of Artemia sp. and B. plicatilis is a valid behavioural end-point to detect stress at sub-lethal toxic substance concentrations. Indeed, alterations in swimming speed have been detected at toxic compound concentrations as low as less then 0.1-5% of their LC(50) values. In conclusion, the SSA test with B. plicatilis and Artemia sp. can be a good behavioural integrated output for application in marine ecotoxicology and environmental monitoring programs.

We propose an integrated approach to estimating building inventory for seismic vulnerability assessment, which can be applied to different urban environments and be efficiently scaled depending on the desired level of detail. The approach employs a novel multi-source method for evaluating structural vulnerability-related building features based on satellite remote sensing and ground-based omnidirectional imaging. It aims to provide a comparatively cost-and time-efficient way of inventory data capturing over large areas. The latest image processing algorithms and computer vision techniques are used on multiple imaging sources within the framework of an integrated sampling scheme, where each imaging source and technique is used to infer specific, scale-dependent information. Globally available low-cost data sources are preferred and the tools are being developed on an open-source basis to allow for a high degree of transferability and usability. An easily deployable omnidirectional camera-system is introduced for ground-based datacapturing. After a general description of the approach and the developed tools and techniques, preliminary results from a first application to our study area, Bishkek, Kyrgyzstan, are presented.

Forecasting and early warning systems are important investments to protect lives, properties, and livelihood. While early warning systems are frequently used to predict the magnitude, location, and timing of potentially damaging events, these systems rarely provide impact estimates, such as the expected amount and distribution of physical damage, human consequences, disruption of services, or financial loss. Complementing early warning systems with impact forecasts has a twofold advantage: It would provide decision makers with richer information to take informed decisions about emergency measures and focus the attention of different disciplines on a common target. This would allow capitalizing on synergies between different disciplines and boosting the development of multihazard early warning systems. This review discusses the state of the art in impact forecasting for a wide range of natural hazards. We outline the added value of impact-based warnings compared to hazard forecasting for the emergency phase, indicate challenges and pitfalls, and synthesize the review results across hazard types most relevant for Europe. Plain Language Summary Forecasting and early warning systems are important investments to protect lives, properties and livelihood. While such systems are frequently used to predict the magnitude, location, and timing of potentially damaging events, they rarely provide impact estimates, such as the expected physical damage, human consequences, disruption of services, or financial loss. Extending hazard forecast systems to include impact estimates promises many benefits for the emergency phase, for instance, for organizing evacuations. We review and compare the state of the art of impact forecasting across a wide range of natural hazards and outline opportunities and key challenges for research and development of impact forecasting.

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Natural hazards such as earthquakes threaten millions of people all around the world. In a few decades most of these people will live in fast-growing, inter-connected urban environments. Assessing risk will, therefore, be an increasingly difficult task that will require new, multidisciplinary approaches to be tackled properly. We propose a novel approach based on different imaging technologies and a Bayesian information integration scheme to characterize exposure and vulnerability models, which are among the key components of seismic risk assessment.Keywords earthquake • vulnerability • risk management • urban planning • remote sensing • omnidirectional imaging • bayesian networks

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In risk assessment, the exposure component describes the elements exposed to the natural hazards and susceptible to damage or loss, while the vulnerability component defines the likelihood to incur damage or loss conditional on a given level of hazard intensity. In this article, we propose a novel adaptive approach to exposure modeling which exploits Dirichlet-Multinomial Bayesian updating to implement the incremental assimilation of sparse in situ survey data into probabilistic models described by compositions (proportions). This methodology is complemented by the introduction of a custom spatial aggregation support based on variable-resolution Central Voronoidal Tessellations. The proposed methodology allows for a more consistent integration of empirical observations, typically from engineering surveys, into large-scale models that can also efficiently exploit expert-elicited knowledge. The resulting models are described in a probabilistic framework, and as such allow for a more thorough analysis of the underlying uncertainty. The proposed approach is applied and discussed in five countries in Central Asia.