## 1 Introduction
Last but not least, it is due to the fact that, in geoinformatics, software tools are currently so developed that they allow classification based on already validated scientific criteria. In terms of urban space, the most used indices to map built-up areas are: normalized difference built-up index (NDBI) [48], urban index (UI) [51], index-based built-up index (IBI) [36] and enhanced built-up and bareness index (EBBI) [1]. It is very important to specify that to extract specific land cover from an index resulting image, implies interactive threshold determination and repetitive attempts, which have significant implications in mapping accuracy, without the possibility of using the same values for two different images. It is commonly believed that assigning a suitable threshold value is difficult [39]. The wrong setting of thresholds will result in underestimation and overestimation of the observed land type, which leads to large errors and a poor accuracy. The urban growth and extracted urban built-up lands in the metropolitan Washington DC area, based on a NDVI different approach for establishing urban change, filtered with a land cover classification to minimize agriculture confusion, achieved an overall accuracy of 85% [1]. The normalized difference built-up index (NDBI) to automatically map urban built-up areas was proposed by [PERSON] et al. [45]. Therefore, the extracted built-up land information using the normalized difference built-up index is often mixed with vegetation noise, so the author had to further use the normalized difference vegetation index (NDVI) to filter out the noise. This proposed method tries to exploit the unique spectral responses of built-up areas and other urban-related land covers but, as the authors say, their approach is unable to separate urban areas from barren and bare land, due to its complex spectral features. The main problem with those indices is that they generate confusion between built-up areas and bare land, water bodies, and especially uncultivated arable land, due to their high complexity and similarity of spectral response patterns, especially in a mixture of pixels with heterogeneous objects [42]. For example, in the normalized difference built-up index (NDBI), index-based built-up index (IBI) and urban index (UI) images, the difference between the bare land, water bodies, uncultivated arable land, and built-up areas is very limited and the results are characterized by high uncertainties. There are many studies that demonstrated that the built-up land class cannot be efficiently enhanced using an index constructed of original multi spectral bands because the class has a heterogeneous characteristic [49]. Thus, this study aimed to develop a better method for built-up area delineation, using free satellite imagery, without training samples or countless attempts of self-defined thresholds. In this regard, we propose a new and very useful methodology for rapid urban area mapping in a semi-automatic manner, being able to distinguish built-up and other associated lands from bare or natural areas with minimal errors. This method was successfully tested in the first ring of Cluj-Napoca's Metropolitan Area, using Landsat imagery from 2000 to 2019. To verify the performance of the method, results have been compared with other urban-related remote sensing indices such as: normalized difference built-up index (NDBI), urban index (UI), index-based built-up index (IBI) and enhanced built-up and bare land index (EBBI), and the accuracy of the model have been also tested by multiple methods based on ground truth samples. The study results provide an alternative and more accurate remote sensing index-based method for mapping built-up and urban associated land, in a semi-automatic manner and reducing the subjectivity of the analyst.