## 3 Results
### Accuracy Assessment  
The accuracy assessment was made by multiple quantitative methods so that the degree of trust can be clearly determined. The first method used was the traditional confusion (error) matrix [57], with the help of 500 randomly generated validation points, evaluated and codified accordingly for each year in question, based on a set of aerial imagery (Ortho-photos acquired in 2005 from Agency of Payments and Interventions for Agriculture--APIA, 2010 and 2012 from National Agency for Cadastre and Real Estate Advertising-ANCPI) and high-resolution images extracted from Google and Bing servers, covering the study area. By this method, the overall accuracy is calculated by the ratio between all correct predictions and the total number of the validation samples. The Kappa coefficient which is a measure of how the classification results compare to values assigned by chance, is also determined from the confusion matrix (Table 3). As can be seen in the table above, the multiannual overall accuracy in this particular area is above 85%, what we consider a good result, taking into account the heterogeneity of the territory in question. The second method initially involved a database creation with as many buildings markers as possible, corresponding to each year. For this step, multiple sources ware used: numerous GPS points of existing buildings, ground footprint of many buildings, measured in the field in topographic surveys (1996-2005), extracted from various urban development plans, or extracted from Open Street Map servers. The database created has been completed and corrected manually, based on high-resolution images. Thereafter, all the buildings footprints were converted into points and from a total database of more than 65,000 points, 1000 ground-truth sample points have been randomly selected for each year in question and introduced in the validation process (Figure 5). By this method, the overall accuracy for each year in question has been determinate by the ratio between correct prediction and total validation sample, based on a Boolean \"true\" or \"false\" prediction table (Table 4). Based on the fact that in this case, the ground-truth samples have been obtained from real buildings location, as expected, the results were slightly better than in the previous method. This is understandable, because this method only evaluates the prediction accuracy of the built-up areas, while in the matrix of confusion the prediction accuracy has involved in the final result, the evaluation of both built-up areas and all other land use classes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Overall Acc. (\%)**} & \multirow{2}{*}{**Kappa Coefficient**} & \multicolumn{3}{c}{**Producer Accuracy (\%)**} & \multicolumn{2}{c}{**User Accuracy (\%)**} \\ \cline{3-6}  & & & **Built-Up** & **Other** & **Built-Up** & **Other** \\ \hline
2000 & 88.40 & 61.15 & 94.42 & 63.15 & 82.76 & 85.42 \\
2005 & 88.00 & 72.80 & 96.86 & 72.53 & 92.96 & 86.03 \\
2010 & 85.20 & 70.40 & 98.80 & 71.60 & 98.35 & 77.67 \\
2013 & 84.60 & 67.63 & 94.43 & 71.36 & 90 The third method of validation implied a comparison between built-up area values obtained by this approach. This step is mandatory and necessary in all the GIS modeling analysis so that the final results are applicable in the specialized practice [67; 68; 69; 70], with real statistical area values, obtained either from competent institutions, independent sources or by supervised image classification (Table 5).