Development And Primary Application Of City Earthquake Disaster Loss Evaluation System Based On GIS And RS Technique

With the development of GIS and RS technique in recent years, GeographicInfOrmation System (GIS) and Remote Sensing (RS) have great applicationpotential on seismology. Earthquake disaster loss evaluation is one important GISand RS application fields of seismology.Ordinary GIS software provides us some simple function, but it can'tcompletely meet need of earthquake disaster loss evaluation, so second GISdevelopment about disaster loss evaluation is necessary for us.In this paper, author introduces and contrasts three kinds of seconddevelopment techniques of GIS, then recommend one GIS ActiveX control-MapObjects2.0. MaPObjects2.0 is the product of ESRI Co. American. It is oneconvenient control base on COM (Component Object Model) technique. Anthorstress introduces its struCture and mechanism. Based on the function ofMapObjects2.0, author implement some special GIS function aim at need ofearthquake disaster loss evaluation and developed earthquake disaster lossevaluation system about Beijing north- east area.This System has not only GIS function module but also some specialtyfunction module such as earthquake fatalness analysis module. disaster loss pre-estimate module and so on. Different module can implement different function. Thegreatest advantage of this system is that it has the general GIS function and somespecial earthquake disaster evaluation function. In addition, author particularlyintroduces some fatal technique nub, including how to implemeflt quickly creatingearthquake iatensity isoseismal function, making concerned shape file function,map layer cutting function and so on.Wth the development of remote sensing technique, high-resolution imageappears application potential in many fields, especially in field of Earthquakedisaster loss evaluation. In this paper, we have a test about getting buildinginfOrmation from high-resolution image.Research shows that different characteristic combination image mode hasdifferent classified result. By contrast classified results of fOur differentcombination mode, we can see that result joined by fusion image, building altitudeand teXure information has the best precision. Its high building classified precisionis 80%, middle and low building classified precision are all exceed the othersclassified results.