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In order to transcend the challenge of accelerating the establishment of cadastres and to efficiently maintain them once established, innovative, and automated cadastral mapping techniques are needed. The focus of the research is on the use of high-resolution optical sensors on unmanned aerial vehicle (UAV) platforms. More specifically, this study investigates the potential of UAV-based cadastral mapping, where the ENVI feature extraction (FX) module has been used for data processing. The paper describes the workflow, which encompasses image pre-processing, automatic extraction of visible boundaries on the UAV imagery, and data post-processing. It shows that this approach should be applied when the UAV orthoimage is resampled to a larger ground sample distance (GSD). In addition, the findings show that it is important to filter the extracted boundary maps to improve the results. The results of the accuracy assessment showed that almost 80% of the extracted visible boundaries were correct. Based on the automatic extraction method, the proposed workflow has the potential to accelerate and facilitate the creation of cadastral maps, especially for developing countries. In developed countries, the extracted visible boundaries might be used for the revision of existing cadastral maps. However, in both cases, the extracted visible boundaries must be validated by landowners and other beneficiaries.

The land administration system, providing a mechanism to support the management of real properties, is one of the most crucial infrastructures of any country. This infrastructure is needed to support planning and implementation of land-related policies, and in general to support human decisions and activities in our environment. The fundamental part of a modern land administration system is the land cadastre. Over the last decade, the demand for three-dimensional (3D) cadastre has increased significantly worldwide. The physical and legal complexities of the built and natural environment prompt new concepts and definitions of real property units in order to meet the demands of a today's society and to balance private and public spatial interests. Particularly in urban areas, including other areas with intensive human interventions into space, there is a tendency to use space above and below the Earth's surface, above and below structures, etc. Complex 3D objects cannot be defined and registered as cadastral objects in the traditional 2D land cadastre and represented in a 2D cadastral map. For this purpose, next to the land cadastre, in 2000, Slovenia introduced the building cadastre. From the juridical point of view, the current cadastral system is not sufficient for all 3D situations. In this paper, we discuss upgrade possibilities of data models of the land cadastre and building cadastre to introduce a unique 3D real property cadastre in Slovenia. We believe that the data available in the current cadastres will significantly contribute toward the 3D real property cadastre and 3D graphical representation of cadastral data; nevertheless, some additional data are needed. The minimum data required could be provided already through the current cadastral procedures.

This paper presents a completely automatic processing chain for orthorectification of optical pushbroom sensors. The procedure is robust and works without manual intervention from raw satellite image to orthoimage. It is modularly divided in four main steps: metadata extraction, automatic ground control point (GCP) extraction, geometric modeling, and orthorectification. The GCP extraction step uses georeferenced vector roads as a reference and produces a file with a list of points and their accuracy estimation. The physical geometric model is based on collinearity equations and works with sensor-corrected (level 1) optical satellite images. It models the sensor position and attitude with second-order piecewise polynomials depending on the acquisition time. The exterior orientation parameters are estimated in a least squares adjustment, employing random sample consensus and robust estimation algorithms for the removal of erroneous points and fine-tuning of the results. The images are finally orthorectified using a digital elevation model and positioned in a national coordinate system. The usability of the method is presented by testing three RapidEye images of regions with different terrain configurations. Several tests were carried out to verify the efficiency of the procedure and to make it more robust. Using the geometric model, subpixel accuracy on independent check points was achieved, and positional accuracy of orthoimages was around one pixel. The proposed procedure is general and can be easily adapted to various sensors.

n this article, a method for the automatic detection of power lines in a horizontal xy plane from airborne and terrestrial laser scanning data is presented. The workflow is composed of four main steps: pre-processing with classification of a point cloud, filtering of the point cloud, the detection of points on the power lines by applying the Hough transform (HT), and vectorisation of power line locations and their intersections. In the pre-processing step, most of the points that are not representing power lines are eliminated via classification of the point cloud. We present our filter, which reduces the number of points in the point cloud further and thus accelerates data processing and increases the reliability of processing in the next steps. We detect the points on the power lines with the HT on the vector points in the xy plane. The final track of the power lines is derived from the straight segments computed by the method of the least squares from the points that HT recognised on the power lines. The results are assessed visually and via relative comparison of the computed intersections coordinates with the reference data manually extracted from the filtered point cloud. The proposed method detects almost all power lines in the test area for both data sets

A point cloud is the result of laser scanning; in the case of terrestrial laser scanning, the point cloud is composed of points scanned from one or more positions. To register these points into one point cloud, so-called tie points are needed; these may be object points (natural targets) or selected stabilized targets (artificial targets). Spherical targets are often used as artificial targets; these must have their centre coordinates and radius determined. The centre coordinates of a sphere are calculated on the basis of scanned points on the spheres’ surface. This paper presents two procedures for determining the best reflection region on the sphere to determine its parameters, and the procedure for determining the optimal distance between the scanner and sphere.The best reflection area on the sphere is determined in two ways. The first is based on minimizing the difference between sphere radii when, in the adjustment process, the radius of the sphere is treated as a known and unknown quantity. The second is based on the standard deviation of the sphere’s centre coordinates at the independent determinations of sphere parameters from randomly chosen scanned points on the sphere surface. For each of the spheres, the best ratio between the laser beam footprint area and the target surface area is calculated for the optimal combination of scanning distance and region. For the best combination of scanning distance and region, we chose the one with the smallest standard deviation of the sphere centre coordinates.

The article addresses automatic building extraction from IKONOS images in suburban areas. In the proposed approach, we used a stereo pair of IKONOS images. Automatic photogrammetric methods of image matching were used to generate a digital surface model DSM and a digital elevation model. In further processing, single-image methods were used. The orthophotos of individual bands were created. The initial building mask was generated from the calculated normalized DSM nDSM. The calculated normalized difference vegetation index and the road data extracted from the existing topographical database were used to remove vegetation and traffic surfaces. The mask was further improved with our own combination of methods based on non-linear diffusion filtering, unsupervised classification, colour segmentation and region growing. The final mask was vectorized using the Hough transform. Compared with a reference building database, 83.2% of the buildings in the test area were detected using the proposed approach with a quality percentage how likely a building pixel produced by an automatic approach is correct of 49.46.

The update of topographic databases is an important task fir organizations that maintain them. Building data are one of the important data types in topographic databases. The article describes a method for automatic building extraction from digital surface model and multispectral orthophoto and the use of extraction results for the building change detection in the topographic database. The initial building mask was created from the normalized digital surface model (nDSM). Vegetation was eliminated from the building mask using a modified vegetation index calculated from the infrared orthophoto and also considering the shadow index and the nDMP texture. The final building mask was vectorised using Radon transform, The results of the automatic building extraction were compared to the building cadastre and the actual situation on the ground. The automatic method detected 94.4% of all buildings in the area. We concluded that the described method is appropriate for capturing of the building data for the topographic database in scales 1: 10 000 and smaller. Automatic change detection results (completeness 93.5% and correctness 78.4%) indicate that the described method is appropriate for building change detection.

Topographic data, an important part of the National Spatial Data Infrastructure, have been given serious attention since Slovenia's independence. It has become publicly available, updated and harmonised. Financing of topographic data in Slovenia has significantly decreased over the previous decade. As a result, the present status is far from acceptable or expected. The current status of the topographic data, as well as its quality and usability for potential users is discussed in this article. The overview starts with basic source data, aerial surveys, photographs and orthophotos. The quality of orthophotos largely depends on the DTM quality. Topographic data is nowadays organised in thematic datasets (geographical names, building cadastre, etc.) or joined in datasets of different levels of accuracy and details. The status in Slovenia is compared to those in some neighbouring and other comparable countries.

High-resolution satellite imagery is being increasingly employed for large-scale topographic mapping, and especially for updating spatial databases. GeoEye-1 is one of the latest in a series of commercial high-resolution earth observation satellites. The paper presents a method for building detection from GeoEye-1 imagery. The initial building mask was created with supervised classification based on the object approach. In the next steps the building mask was enhanced employing, unsupervised classification and region growing. The final results of the building detection are evaluated.