# Extraction of Visible Boundaries for Cadastral Mapping Based on UAV Imagery
## 2 Materials and Methods
### Visible Boundary Delineation Method and Workflow
#### 2.3.2 Visible Boundary Delineation Workflow  
The visible boundary delineation workflow (Figure 2) consists of four main steps. In the following, each workflow step is described in detail with additional comments based on our own preliminary studies aiming to understand and justify the selection of the parameters and algorithms used. The first and second steps were implemented in ENVI 5.5 image analysis software [46] by using the ENVI FX [47] tool. The other steps were implemented using QGIS [48] and GRASS [49] functions. 1. _Image pre-processing:_ The first step is resampling the UAV orthoimage. The UAV orthoimage was resampled from 2 cm to lower spatial resolutions--25 cm, 50 cm and 100 cm ground sample distances (GSD). The selected GSDs allowed the identification of the impact of different GSDs on the results of automatic boundary extractions. The pixel average method was used for resampling the UAV orthoimage as it provides a smoother image. In addition, further resampling methods (nearest neighbor and bilinear) were tested and did not provide significant differences in the number of automatic object boundary extractions--at higher scale and merge levels of the ENVI FX algorithm. The resampling step was also applied in [26], to make transferable the investigated method to a UAV orthoimage for cadastral mapping purposes. In addition, extracting objects from a UAV orthoimage of lower spatial resolution is computationally less expensive. 2. _Boundary detection and extraction:_ The ENVI FX module was applied to each down-sampled UAV orthoimage. The detection and extraction of visible boundaries from the UAV orthoimage was based on the ENVI FX scale and merge level values. The texture kernel size was set to default,i.e., 3. In addition, further object extractions were tested at the highest texture kernel size and no differences in the number and locations of extracted objects were identified. Scale level values ranged from 50 to 80 and merge level values from 50 to 99. The initial incremental value for both scale and merge levels was 10. In cases where a jump in the total number of extracted objects was detected the incremental value was dropped for both scale and merge levels. In order to identify the optimal scale and merge values for the detection and extraction of visible objects for cadastral mapping, all possible range values of scale and merge combinations were tested. For each extraction information about the total number of extracted objects and processing time was stored. This resulted in 50 boundary maps for each resampled UAV orthoimage. The boundary map consisted of extracted objects (polygon-based), which were in digital vector format. 3. _Data post-processing:_ The process included two steps: (i) the filtering of extracted objects, and (ii) the simplification of extracted objects. (i) The minimum object area and the total number of objects identified in the reference data (Figure 1b) were used to determine optimal scale and merge levels. The minimum reference object area was 204 m2, and the total number of objects was 68. All extracted objects that were smaller than the minimum object area from reference data were filtered out (removed). The total number of remaining objects was compared with the total number of objects from the reference data and the tolerance of +/- 10 objects was set--those parameters that produced numbers of objects that were closest to those found in the reference data, i.e., within defined tolerance, were deemed optimal. The boundary maps from which smaller objects were removed were labeled as filtered objects. The output of filtered objects consisted of holes, i.e., due to polygon-based geometry of objects, which were mostly present either in the forest or individual trees and are of less relevance for cadastral applications--a boundary between adjacent objects belongs to both.