Developing Spatial Data Infrastructure to Facilitate Disaster Management

The role of spatial information and related technologies in disaster management has been well-known worldwide. One of the challenges concerned with such a role is access to and usage of reliable, accurate and up-to-date spatial information for disaster management. This is a very important aspect to disaster response as timely, up-to-date and accurate spatial information 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, required responding operations, etc., and should be available and accessible for use in a short period of time. Sharing information between involved parties in order to facilitate coordinated disaster response operations is another challenge in disaster management. This paper aims to address the role of Spatial Data Infrastructures (SDI) as a framework for facilitating disaster management. It is argued that the design and implementation of an SDI model as a framework and consideration of SDI development factors and issues can assist the disaster management agencies in such a way that they improve the quality of their decision-makings and increase their efficiencies and effectiveness in all level of disaster management activities. The paper is based on an ongoing research project in Iran regarding the development of an SDI Model for disaster management. This includes the development of a prototype web-based system which can facilitate sharing, access and use of data in disaster management and especially disaster response.May 200

A Robust Fuzzy Neural Network Model for Soil Lead Estimation from Spectral Features

Soil lead content is an important parameter in environmental and industrial applications. Chemical analysis, the most commonly method for studying soil samples, are costly, however application of soil spectroscopy presents a more viable alternative. The first step in the method is usually to extract some appropriate spectral features and then regression models are applied to these extracted features. The aim of this paper was to design an accurate and robust regression technique to estimate soil lead contents from laboratory observed spectra. Three appropriate spectral features were selected according to information from other research as well as the spectrum interpretation of field collected soil samples containing lead. These features were then applied to common Multiple Linear Regression (MLR), Partial Least Square Regression (PLSR) and Neural Network (NN) regression models. Results showed that although NN had adequate accuracy, it produced unstable results (i.e., variation of response in different runs). This problem was addressed with application of a Fuzzy Neural Network (FNN) with a least square training strategy. In addition to the stabilized and unique response, the capability of the proposed FNN was proved in terms of regression accuracy where a Ratio of Performance to Deviation (RPD) of 8.76 was achieved for test samples

AUTOMATIC ROAD GAP DETECTION USING FUZZY INFERENCE SYSTEM

Automatic feature extraction from aerial and satellite images is a high-level data processing which is still one of the most important research topics of the field. In this area, most of the researches are focused on the early step of road detection, where road tracking methods, morphological analysis, dynamic programming and snakes, multi-scale and multi-resolution methods, stereoscopic and multi-temporal analysis, hyper spectral experiments, are some of the mature methods in this field. Although most researches are focused on detection algorithms, none of them can extract road network perfectly. On the other hand, post processing algorithms accentuated on the refining of road detection results, are not developed as well. In this article, the main is to design an intelligent method to detect and compensate road gaps remained on the early result of road detection algorithms. The proposed algorithm consists of five main steps as follow: 1) Short gap coverage: In this step, a multi-scale morphological is designed that covers short gaps in a hierarchical scheme. 2) Long gap detection: In this step, the long gaps, could not be covered in the previous stage, are detected using a fuzzy inference system. for this reason, a knowledge base consisting of some expert rules are designed which are fired on some gap candidates of the road detection results. 3) Long gap coverage: In this stage, detected long gaps are compensated by two strategies of linear and polynomials for this reason, shorter gaps are filled by line fitting while longer ones are compensated by polynomials.4) Accuracy assessment: In order to evaluate the obtained results, some accuracy assessment criteria are proposed. These criteria are obtained by comparing the obtained results with truly compensated ones produced by a human expert. The complete evaluation of the obtained results whit their technical discussions are the materials of the full paper