# Extraction of Visible Boundaries for Cadastral Mapping Based on UAV Imagery
## 4 Discussion
### Quality Assessment  
Bringing the scale and merge levels to the maximum resulted in some unextracted and fewer visible objects for the whole extent of the image. Although some of the visible objects were left unextracted, the maximum scale and merge level enabled the detection of a group of objects such as a group of tree boundaries, especially at GSDs of 50 cm and 100 cm (Figure 5\(f\),i). In both cases, the balance between completeness and correctness was hard to achieve. This issue was also reported in [26; 28]. For this reason, the filtering approach was applied. It was based on the minimum object size as well as on the total number of the objects, both defined based on the reference data. This allowed us to reduce the risk that some of the visible object boundaries remained unextracted as well as over-segmented. The optimal scale level of 80 and a merge level of 95, were chosen for all three GSDs, to investigate the impact of the same scale and merge level in different resampling. The selection was based on common scale and merge levels per GSD (Figure 6). However, this does not mean that the chosen scale and merge level provided the best object boundary extraction for each of the GSDs. For instance, for small GSDs, the correctness of extracted boundaries is higher at the maximum scale and merge levels (e.g., Figure 5c). For the same scale and merge level, the correctness grows significantly from a GSD of 25 cm to a GSD of 50 cm. The correctness for a GSD of 100 cm was almost equal to the one for a GSD of 50 cm. Considering that more optimal scale and merge levels were applicable for a GSD of 50 cm (Figure 6) and the difference insignificant when compared to the results obtained for a GSD of 100 cm, a GSD of 50 cm appeared to be better in detecting visible boundaries compared to the other two GSDs. The quantitative method applied for accuracy assessment to automatically extracted objects, filtered objects and to their simplifications, showed that there was no significant difference between extracted objects and simplified objects. This result indicates that the method applied for simplification can be considered appropriate, i.e., the original location of extracted objects was maximally maintained. Although there was no difference in accuracy assessment, the simplification of extracted (or filtered) objects is significant for proper cadastral mapping. Cadastral boundaries usually are defined by straight lines with fewer vertices. The percentage of suitable extracted boundaries (compared to reference data), for a scale level of 80 and a merge level of 95, resulted in 74% for the assessment of the completeness and 66% for the assessment of the correctness for the extracted object boundaries having a GSD of 50 cm. However, the filtering approach strongly influenced the accuracy assessment. For filtered extractions, the level of completeness was 67%, and the level of correctness was 77%. These results show that the filtering approach increased the correctness of automatically extracted boundaries, and it reaches almost 80% (Table 4). This was due to filtering out small object boundaries from the boundary map. The excluded small objects were mostly present in tree and built-up areas on the UAV orthoimage, i.e., only outlines of group objects were retained (Figure 7c,d). In road extractions, the achieved values for extractions are around 85% for correctness and around 70% for completeness to be of real practical importance [26; 34]. Such percentages can hardly be achieved by the workflow developed for automatic delineation of all visible boundaries since the morphology of cadastral boundaries is usually more complex and not all cadastral boundaries are visible, unlike road boundaries. The accuracy assessment was based on the manually delineated boundaries, which were defined as reference data (Figure 1b). The visible boundaries were manually delineated on the ground truth UAV orthoimage.