Spatial data and related technologies have proven to be crucial for effective collaborative decisionmaking in disaster management. However, there are currently substantial problems with availability, access and usage of reliable, up-to-date and accurate data for disaster management. This is a very important aspect to disaster response as timely, up-to-date and accurate spatial data describing the current situation is paramount to successfully responding to an emergency. This includes information about available resources, access to roads and damaged areas, required resources, and required disaster response operations that should be available and accessible for use in a short period of time. Any problem or delay in data collection, access, usage and dissemination has negative impacts on the quality of decision-making and hence the quality of disaster response. Therefore, it is necessary to utilize appropriate frameworks and technologies to resolve current spatial data problems for disaster management. This paper aims to address the role of Spatial Data Infrastructure (SDI) as a framework for the development of a web-based system as a tool for facilitating disaster management by resolving current problems with spatial data. It is argued that the design and implementation of an SDI model and consideration of SDI development factors and issues, together with development of a webbased GIS, can assist disaster management agencies to improve the quality of their decision-making and increase efficiency and effectiveness in all levels of disaster management activities. The paper is based on an ongoing research project on the development of an SDI conceptual model and a prototype web-based system which can facilitate sharing, access and usage of spatial data in disaster management, particularly disaster response.

ABSTRACT:In this article, the possibility of using artificial neural networks for road detection from high resolution satellite images is tested on a part of RGB Ikonos and Quick-Bird images from Kish Island and Bushehr Harbour respectively. Then, the effects of different input parameters on network's ability are verified to find out optimum input vector for this problem. A variety of network structures with different iteration times are used to determine the best network structure and termination condition in training stage. It was discovered when the input parameters are made up of spectral information and distances of pixels to road mean vector in a 3*3 window, network's ability in both road and background detection can be improved in comparison with simple networks that just use spectral information of a single pixel in their input vector.

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Automatic extraction of geospatial features has been the subject of extensive research in the past three decades. Here, an approach based on fuzzy logic and mathematical morphology is proposed, to extract main road centrelines from pan‐sharpened IKONOS images. In the IKONOS images, a standard deviation of 10 grey levels has been measured for the road classes. In the proposed fuzzy logic system, just one arbitrary pixel (up to a maximum of 3 pixels) provides an adequate initial value. Road identification requires neither the numbers of the classes nor the corresponding mean values; then, using advanced morphological concepts, the road centreline is extracted. The method is applied to pan‐sharpened IKONOS images of urban, suburban and rural areas around the Pyramids in Egypt, and Rasht City and Kish Island in Iran. The extracted road centrelines have an average error of 0·504 pixel and root mean square error of 0·036 pixel. The method is more accurate at road intersections and on curves than on straight sections of road. The extracted road is then used as a direct input to a geographical information system (GIS).

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