The accentuated dynamics of the real estate markets of the last 20 years, determined that a large part of the territories in the immediate vicinity of the big urban centers, to change their category of land use, in an accelerated rhythm. Most of the time, the land use changes according to the market requirements, the predominantly agricultural lands being occupied by constructions with residential or industrial functions. Identifying these changes is a difficult task due to the heterogeneity of spatial databases that come from different real estate development projects, so determining and implementing new methods to track land changes are currently highly required. This paper presents a methodologically innovative index-based approach for the rapid mapping of built-up areas, using Landsat-5, Landsat-7, and Landsat-8 satellite imagery. The approach described in this study differs from other conventional methods by the way the analysis was performed and also by the thematic indices used in the processes of built-up area delineation. The method, structured in a complex model, based on Remote Sensing and GIS techniques, can be divided into three distinct phases. The first stage is related to the pre-processing of the remote sensing data. The second stage involves the calculation of the normalized difference vegetation index (NDVI), the modified normalized difference water index (MNDWI), and the bare soil index (BI) correlated with the extraction of all areas not covered by vegetation; respectively, the elimination from the result of all areas covered by water, bare land, or uncultivated arable land. The result of this stage is represented by a distinct thematic layer that contains only built-up areas and other associated territories. The last step of the model is represented by the validation of the results, which was performed based on statistical methods and also by direct comparison with field reality, obtaining a validation coefficient which is generally above 85% for any of the methods used. The validation process shows us that by applying this method, the fast mapping of the built-up areas is significantly enhanced and the model is suitable to be implemented on a larger scale in any practical and theoretical application that aims at the rapid mapping of the built-up areas and their evolutionary modeling.
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