Automated road network extraction from remotely sensed imagery can be a promising approach to efficient road databases creation, refinement and updating. However, due to the extreme complexity of an urban scene, road extraction in urban areas is challenging. This paper presents a new integrated approach to extract urban road networks from high spatial-resolution multi-spectral imagery. The prop...

aid uses, including fire stations, according to critical activities that are responsible, in comparison to other city services, are more important. identify areas where are located outside of the fire stations covered services can help urban planners to site selection of this important land use. the purpose of this paper is detection the areas that are beyond the services coverage of the fire s...

natural fire inflicting irreparable damage to rangelands and forest areas is cause changes in landscape ecology. the purpose of this research is comparison of artificial neural network (ann) and line regression (lr) models to predict of forest and rangelands fires to this end, the data consist fire burned area and fire were used weather data over a period of 7 years (2006-2012(.the result indic...

This paper is dedicated to post disaster road network verification and routing using High Resolution Satellite Imagery (HRSI) and Ant Colony Optimization (ACO) algorithms. By determination of damage degree to each road element using satellite information, a modified ACO algorithm is designed and applied to find best routes with respect to each road’s length and damage degree. The mentioned algo...

—We have developed a tabu search method to optimize the stand level harvest decision over intermediate time frames (10-30 years) within the context of a longer term hierarchical plan. In this paper, we focus on the objective function for our model. We are interested in the trade off between two main issues, one the cost of building roads to access the resource and the other the loss of forest p...

Aiming at the multi-depot heterogeneous vehicle routing problem under time-dependent road network and soft time window, considering fixed cost, window penalty cost transportation an optimization model of is established with objective minimizing distribution cost. According to characteristics problem, a hybrid genetic algorithm variable neighborhood search temporal–spatial distance designed. Cus...

This study explores a semi-supervised classification approach using random forest as a base classifier to classify the low-back disorders (LBDs) risk associated with the industrial jobs. Semi-supervised classification approach uses unlabeled data together with the small number of labelled data to create a better classifier. The results obtained by the proposed approach are compared with those o...