# Extraction of Visible Boundaries for Cadastral Mapping Based on UAV Imagery
## 1 Introduction
### Visible Boundary Detection and Extraction for Cadastral Mapping  
Automatic feature extraction methods from images acquired with high-resolution optical sensors have already proved to be useful for the extraction of boundaries of linear features such as roads and rivers [30; 31; 32; 33; 34], and to a much lesser degree, they have also been explored for the purpose of cadastral boundary delineation. A recent study from [PERSON] et al. [22] provides an overview of computer vision methods that might be applicable in the land administration domain for automatic detection and extraction of object boundaries from images acquired with high-resolution optical sensors. Additionally, the general workflow for automatic detection and extraction of visible object boundaries for UAV-based cadastral mapping is provided [22]. The general workflow consists of (i) image pre-processing, (ii) image segmentation, (iii) line extraction, (iv) contour or boundary generation, and (v) image and/or boundary post-processing. Image pre-processing usually includes image conversions, such as resampling or tiling, in order to fit the requirements of a chosen computer vision method. Image segmentation refers to the process of dividing a digital image into non-overlapping objects, which represent homogeneous areas [35]. The third workflow step is the extraction of lines or edges from the segmented images [36]. The next step, contour generation, refers to the extraction of a closed object outlines in the image. In computer vision, they are usually defined as object boundaries, which are derived from connecting edges or lines. An 'object boundary' should encompass an 'object' in an image,and due to this, both terms are used synonymously in this study. In cadastral applications, objects are usually defined as polygon-based spatial units. The final step, post-processing, includes interventions on the image such as vectorization and/or simplification of automated extraction of objects [26; 37]. However, only a limited number of studies have investigated the automatic extraction of objects from images acquired with high-resolution optical sensors for cadastral boundary delineation. The work by [PERSON] and [PERSON] [38] tested [PERSON] and [PERSON] edge-detection algorithms for the extraction of visible cadastral boundaries from high-resolution satellite imagery (HRSI). In addition, the Hough Transform feature extraction method was used to connect edges and to identify straight lines. The visual presentation of the results showed that the proposed approach can detect agricultural land boundaries, but there were no quantity measures on quality assessment. [PERSON] et al. [28; 29] investigated the use of an object-based approach, namely the multi-resolution segmentation (MRS) and estimation of scale parameter (ESP) to extract visible cadastral boundaries from HRSI. An object-based approach refers to the extraction of object outlines based on a grouping of pixels with similar characteristics and is applied to high-level features which represent shapes in an image [22]. The accuracy assessment in [PERSON] et al. [28] was pixel-based, and the detection quality in terms of error of commission and omission for MRS were 75% and 38%, respectively. For ESP, the error of commission was 66% and the error of omission 58%. The localization quality for MRS was 71%, whereas it was 73% for ESP, within a 41-200 cm distance from the reference boundaries. Another case of the automatic extraction of visible boundaries based on HRSI is described in [PERSON] et al. [27]. The study explored the potential of mean-shift segmentation for the extraction of visible cadastral boundaries. The mean-shift segmentation algorithm is a QGIS open source plugin [27]. The object-based measures were applied for the accuracy assessment. Within a buffer distance of 2 m, the percentage indicated the correctness was 34%, while for the completeness it was 83% [27]. The extractions with mean-shift segmentation were closed object boundaries (polygon-based) in vector format and topologically correct.