## 4 Discussions  
In this study, a new approach has been proposed in order to automatically delineate the built-up area in a way that can reduce the data redundancy and spectral confusion between land-use classes. Figure 6: Graph representing the comparison of built-up area values (hectares) between supervised classification, mean value of other sources, and study method. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No. **} & \multirow{2}{*}{**Source of Data**} & \multicolumn{6}{c}{**Built-Up Area (ha. )**} \\ \cline{3-8}  & & **2000** & **2005–2006** & **2010** & **2012–2013** & **2015–2016** & **2018–2019** \\ \hline
1 & I.N.S.S.E. & NO DATA & NO DATA & 5165 & 5177 & NO DATA & NO DATA \\
2 & E.E.A. Urban & \multirow{2}{*}{NO DATA} & \multirow{2}{*}{5378.3} & \multirow{2}{*}{NO DATA} & \multirow{2}{*}{6253.44} & \multirow{2}{*}{NO DATA} & \multirow{2}{*}{6809.90} \\  & Atlas & & & & & \\  & Copernicus & & & & & & \\
3 & Urban & \multirow{2}{*}{NO DATA} & \multirow{2}{*}{4705.92} & \multirow{2}{*}{5501.44} & \multirow{2}{*}{5597.32} & \multirow{2}{*}{5724.36} & \multirow{2}{*}{NO DATA} \\  & Databases (ESM, & Imperviousness) & & & & & \\
4 & Image & \multirow{2}{*}{4857.43} & \multirow{2}{*}{5593.38} & \multirow{2}{*}{6301} & \multirow{2}{*}{6363.93} & \multirow{2}{*}{6491.38} & \multirow{2}{*}{6731.22} \\  & classification & & & & & & \\
5 & Mean value & 48  
We believe that a fast and automatic method for built-up area mapping is very useful currently, and has important potential applications, especially in the activity sphere of territorial planning and regional development. For example, the resulting geometry and data can be very useful in terms of assessing the development potential of a territory, and multi-temporal observations are very useful for development directions, trend, and patterns analysis. The large-scale analysis of the results of the proposed model highlights the dynamics of areas occupied by construction, dynamics that can be traced to specific periods that overlap with the main periods of \"real estate boom\" and accentuated economic development in the suburbs of Cluj-Napoca. Real estate expansion is generally achieved despite the geomorphological constraints of the land, so if in the period 2000-2013, the main lands on which residential constructions were made were those from the Somesul Mic meadow, relatively flat lands and without too many geomorphological restrictions (Figure 7c,f); starting with 2016, the constructions extended in the hilly areas (Figure 7a,b,d,e), areas characterized by instability in terms of slope dynamics, requiring the implementation of complex stabilization techniques, which also involved a considerable increase in prices, affecting the entire real estate market. These observable approaches in the behavior of real estate developers are observed both in the case of residential developments and those aimed at economic projects (logistics, warehousing, retail, etc.). The analysis of the territorial development from the perspective of the increase in the built surfaces and the associated dynamics can be followed very well in the case of the built hearth of Floresti locality, it being the area with the highest dynamics in terms of built land in the entire area analyzed. Therefore, it is observed that a first phase of development, the real estate boom period that took place between 2005 and 2010, followed by the period between 2010 and 2016, a post-economic crisis period, with a development of constructions mainly in the areas without risk from a geomorphological and hydrological point of view and with a minimum cost to build, in the meadow of the Somesul Mic river (Figure 7f).