Nowadays satellite imagery uses for producing and updating the maps because of their capabilities. In recent years, IRS-P5 images was used for updating the maps with 1:25000 scale. Also VHR images like IKONOS2 and QuickBird2 can be used for updating cadaster maps based on manual transformation. At present With easier access to these images and also appearance of VHR imagery like GeoEye1 and WorrldView2 and extension of advanced algorithms create a good opportunity for making large scale maps and speed the updates.

It can be said that with using VHR images, updating maps is done better than making maps, so it’s in priority. But using satellite images and processing algorithms for making and updating large scale maps have some difficulties in preparing required layers in these kind of maps. Even GeoEye1 images that have 50cm spatial resolution, can’t prepare all of required layers. The main purpose in this theses is updating 1:2000 scale maps using GeoEye1 stereo image. Indeed we want to study the performance of these data for updating the maps with creating feature vector for image pixels instead of gray values and also using GeoEye1 stereo image instead of single vertical image. Our first assumption is that if we use GeoEye1 stereo image for new image instead of single vertical image, not only we can get higher precision for updating large scale maps, but also we can manage different height error and making shadows. For this purpose we used GeoEye1 stereo image. Our second assumption is that in updating large scale maps, GD-making of gray scales is no longer effective because our subject is referred to geometry of phenomenon. For this purpose, first all of features are extracted from image, then participate in GD-making and finally the most effective features in 3 groups are chosen and arranged with try and error that make a feature vector with independent members. In the beginning of work, first horizontal and vertical accuracy that required for large scale maps are reviewed, then the largest scale map that can be prepared with satellite images are selected (in this case is 1:2000) and finally the performance of GeoEye1 stereo images between 2006 to 2010 that used for building change detection and update 1:2000 scale maps are reviewed. Updating strategy for 1:2000 scale map that used in this theses has 5 stages: choosing data and pre-processing them, change detection, post-process the change detected results, assessment the change detected results and finally applying the results in maps. For these 3 stages; change detection, post-process the change detected results and assessment the change detected results; we written an algorithm based on differentiation of image pixels feature vector that detected building changes in 3 study regions, Additional pixels are eliminated and these changes detected by algorithm are compared with actual changes using confusion matrix and the results are showed In the form of Overall accuracy, producer accuracy and user accuracy. Accuracy Values obtained for change class in best condition for second region that was an area with low building density is 3.11%, 68.60% and 64.29%. But in third region that was an area with high building density, the acquired accuracies for changes class are 95.07%, 4.81% and 5.22%. Based on these results for change detection using GeoEye2 stereo images, suggested algorithm has necessary performance in the areas with low building densities. Also it proofs that gray scale deferential or any other image feature alone doesn’t perform well in change detection using VHR images. But using feature vector in GD-making is quite effective. And also we have been able to manage the error due to the height difference and shadows and introduce these parts to operator using stereo images.