## 1 Introduction
Approaches based on remote sensing imagery are very popular currently due to the extraordinary improvements over recent years, in terms of the quality of the data provided and also because of time and money efficiency; offering multiple mapping solutions for urban agglomerations. Remote Sensing is also used for more detailed object-based analysis by image segmentation. Recent technological advances in Geographic Information System (GIS) and image analysis offer the potential of using digital images to objectively quantify ground cover and composition of vegetation, in a repeatable and timely manner [34]. The satellite observations can provide globally consistent and repetitive measurements of the Earth's surface conditions, returning important data with different spatial, spectral, radiometric, and temporal resolutions. They are extremely useful for monitoring the spatial distribution and growth of urban built-up areas because of their ability to provide timely and synoptic views of land cover [35; 36]. Many researchers [37; 38; 39; 40; 41; 42; 43; 44; 45] have made use of remote sensing images to delineate the built-up areas and the majority of the approaches are based on indices or classification methods, using spectral information from satellite data. Still, the spatial and spectral variability of urban environments present fundamental challenges to obtain accurate remote sensing-based products for urban areas [41]. Land use in urban areas is very dynamic and it changes continuously, mainly due to the construction of new buildings, roads, and other infrastructures correlated with ongoing natural land take. The urban planners and decision-making actors require up-to-date land use information. Data acquisition by remote sensing sensors, integrated and further analyzed in Geographic Information System and Remote Sensing applications, has been characterized by the efficient and accurate manual extraction of the information. However, manual extraction of the information is very time consuming and requires qualified specialists; more than that, many applications do not require such a high level of detail, focusing on overviews, requiring a fast processing of numerous time series data. Therefore, to speed up this process, automatic or semiautomatic urban area delineation has become a necessity. With the automatic delineation of settlement boundaries densification, growth and sprawl, or shrinkage, can be examined in different studies on different scales [40]. Over the years, in the field of remote sensing, scientists have developed many spectral indices for different domains and applications. For example, related to vegetation, indices such as the normalized difference vegetation index (NDVI) [30; 46] and enhanced vegetation index (EVI) [47] have been developed in order to detect vegetation greenness and to rapidly map the distribution of vegetation or a variety of land surface conditions; water bodies have been extracted by using the normalized difference water index (NDWI) [48] and the modified NDWI (MNDWI) [49]; bare land have been highlighted using the normalized difference bare land index (NBLI) [42] or the normalized difference bareness index (NDBaI) [50]. A considerable number of techniques for automatically or semi-automatically mapping urban areas using satellite imagery have been developed, applied, and evaluated. These techniques can be broadly grouped into two categories: (1) those based on the classification of the input data, and (2) those based on spectral indices. This study will be focused on the second category due to the fact that the proposed methodology is focused on rapid mapping of the built-up area and, most of the time, as in the present case, this type of processing aims at the analysis of very large territories where the built surfaces are very fragmented and are characterized by a great diversity in terms of construction types, which makes it difficult and time-consuming to choose samples for supervised classification.