Remote sensing satellite imagery and risk management: image based information extraction

The introduction of high and very high resolution multispectral satellite imagery, characterized by ground resolution from one to few meters, has lead to a new perspective in processes of risk estimation, mitigation and management. In particular, the possibility to obtain in a very short time a wide-scene image of areas subjected to a crisis has become useful both for emergency management and for effective damage estimate. Examples of images in the immediate aftermath of natural disasters like earthquakes, hurricanes, floods, fires, tsunami, volcanic eruptions, etc, have been also globally distributed trough information media and web based image systems such as Google Earth. However, a concrete possibility to extract quantitative information from such kind of images is subject to several factors: first of all, accessibility in term of timing in image acquisition and in image delivering to stakeholders, followed by image quality (resolution, absence of clouds, geo-location, informative content, etc.), and finally by the information extraction process. After a review of the problem and of the current situation in terms of available data and applications, the paper focus on an approach for information extraction and quantitative image classification, mainly by object oriented analysis. One of the major objectives is to show the possibility to obtain the outlines and the count of the buildings for an area interested by a severe disaster, saving human time in image interpretation in procedures of risk management and in estimating rebuilding costs; the integration of information coming from existing building databases is also considered. The experiences show the potential of the method and promising results in the classification accuracy.