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.
COBISS.SI-ID: 8835681
The literature generally agrees that improved accessibility has a positive effect on housing values. The motorway constructions in north-eastern Slovenia, completed between 2002 and 2018, significantly improved the accessibility of less developed areas. In this article, we examine how the new motorway has affected the housing values in the mostly rural north-eastern part of Slovenia. We analysed the distance decay effect of amenities and disamenities of motorway links and ramps as well as bus stops, train stations, and railway links on housing prices. In addition to the transport infrastructure variables, we analysed numerous other structural and environmental variables. We estimated three different spatial hedonic regression models: the spatial autoregression model, the spatial error model, and the spatial autocorrelation model. The results indicate that none of proximities to the bus stop, train station, or motorway link have had a significant effect on housing value. However, the rail track proximity exhibited a significant negative association with price. Furthermore, the effect of distance to the nearest motorway ramp on housing value is significant: it is stronger for closer distances (i.e. starting from 500 m) and decreases up to 6 km, while the effect of distances below 500 m is insignificant. Our results also show that bigger dwellings, more rooms, and the presence of a balcony, elevator, central auto heating system, new installation, and air conditioning as well as an apartment in a single house, are associated with a higher housing price. On the contrary, selling the dwellings together with a parking place and basement reduced the average price of the housing property. Comparing our results to the literature, we proved our hypothesis that the negative effect of a motorway is less pronounced in rural areas compared to urban areas and that the positive effect of accessibility is stronger in rural areas and extends over a wider geographical area.
COBISS.SI-ID: 29051139
The main goal of our research was to design and implement an innovative target that would be suitable for accurately registering point clouds produced from unmanned aerial vehicle (UAV) images and terrestrial laser scans. Our new target is composed of three perpendicular planes that combine the properties of plane and volume targets. The new target enables the precise determination of reference target points in aerial and terrestrial point clouds. Different types of commonly used plane and volume targets as well as the new target were placed in an established test area in order to evaluate their performance. The targets were scanned from multiple scanner stations and surveyed with an unmanned aerial vehicle DJI Phantom 4 Pro at three different altitudes (20, 40, and 75 m). The reference data were measured with a Leica Nova MS50 MultiStation. Several registrations were performed, each time with a different target. The quality of these registrations was assessed on the check points. The results showed that the new target yielded the best results in all cases, which confirmed our initial expectations. The proposed new target is innovative and not difficult to create and use.
COBISS.SI-ID: 8860513
The paper presents a method for mapping fluvial gravel bars based on Sentinel-2 and Landsat imagery. The proposed method therefore uses spectral signal mixture analysis (SSMA) because its results allow the development of land cover fraction maps for surface water, gravel, and vegetation. The method is validated on a spatially heterogeneous mountainous area in the upper Soča river basin in north-west Slovenia, Central Europe. Unmixing results in highly accurate fraction maps with MAE of around 0.1. Gravel fractions are mapped the most accurately, indicating that the approach can be used successfully for fluvial gravel bar mapping. Endmember sets selected automatically perform slightly worse (MAE higher by at most 0.05) than sets selected manually based on high resolution reference data. Both Sentinel-2 and Landsat imagery can be used for accurate mapping with differences between the two remote sensing systems within 0.05 MAE. For the study area, the SSMA-based soft classification method is more accurate for land cover mapping than a Spectral Angle Mapping-based hard classification. The method is promising for an effective use in other cases where highly accurate subpixel information is needed, because it is able to detect small-scale changes that could go unnoticed with hard classification mapping.
COBISS.SI-ID: 35062531
This study is focused on indoor navigation network extraction for navigation applications based on available 3D building data and using SFCGAL library, e.g. simple features computational geometry algorithms library. In this study, special attention is given to 3D cadastre and BIM (building information modelling) datasets, which have been used as data sources for 3D geometric indoor modelling. SFCGAL 3D functions are used for the extraction of an indoor network, which has been modelled in the form of indoor connectivity graphs based on 3D geometries of indoor features. The extraction is performed by the integration of extract transform load (ETL) software and the spatial database to support multiple data sources and provide access to SFCGAL functions. With this integrated approach, the current lack of straightforward software support for complex 3D spatial analyses is addressed. Based on the developed methodology, we perform and discuss the extraction of an indoor navigation network from 3D cadastral and BIM data. The efficiency and performance of the network analyses were evaluated using the processing and query execution times. The results show that the proposed methodology for geometry-based navigation network extraction of buildings is efficient and can be used with various types of 3D geometric indoor data.
COBISS.SI-ID: 21687043