Austrian Cadastre still in use – Example proceedings to determine the legal status of land property in southern Poland

Bacior, Stanisław

doi:10.1016/j.landusepol.2023.106740

Cited by 6 publications

(11 citation statements)

References 52 publications

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“…In this research, unlike previous studies [6,9,11], changes in a base map were identified by machine learning algorithms in an intelligent and automatic method, which increases the speed and accuracy of the urban map enrichment process. Moreover, relocating changed parcels automatically and updating them with the help of a more up-to-date map reduces human errors and the time required to enrich urban maps.…”

Section: Discussionmentioning

confidence: 99%

“…Moreover, relocating changed parcels automatically and updating them with the help of a more up-to-date map reduces human errors and the time required to enrich urban maps. In previous studies [6,9,11], enrichment has often been performed manually, visually and case by case, which is very time-consuming compared to the proposed model. The presented framework plays a very important role in reducing the cost and time spent by organizations in preparing urban maps or enriching urban cadastral maps by the quick and accurate use of available maps from other organizations.…”

Section: Discussionmentioning

confidence: 99%

“…After modeling all the parcels and detecting the changes in the whole area, the parcels from other maps that correspond to the changed parcels in the base map are placed in the base map by the affine transformation algorithm after calculating the affine parameters from unchanged neighbor parcels for each changed parcel, then the topologic errors are removed so that the base map is intelligently and automatically enriched. In this research, unlike previous studies [6,9,11], changes in a base map were identified by machine learning algorithms in an intelligent and automatic method, which increases the speed and accuracy of the urban map enrichment process. Moreover, relocating changed parcels automatically and updating them with the help of a more upto-date map reduces human errors and the time required to enrich urban maps.…”

Section: Discussionmentioning

confidence: 99%

“…Land information is required for different LAS components, which are land development, land use, land value and land tenure [5]. Urban cadastral maps as a part of land information are large-scale maps that contain dimensions, shapes, spatial relationships and different attributes of information about land parcels [6].…”

Section: Introductionmentioning

confidence: 99%

“…Map enrichment has been performed using different methods and data and for different usages. In some research, historical and analogue cadastral maps have been used to determine ownership boundaries and in the renewal of cadastral maps [6,9,11]. Data and map integration are other methods for map enrichment.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Smart Urban Cadastral Map Enrichment—A Machine Learning Method

Hajiheidari,

Delavar,

Rajabifard

2024

IJGI

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Enriching and updating maps are among the most important tasks of any urban management organization for informed decision making. Urban cadastral map enrichment is a time-consuming and costly process, which needs an expert’s opinion for quality control. This research proposes a smart framework to enrich a cadastral base map using a more up-to-date map automatically by machine learning algorithms. The proposed framework has three main steps, including parcel matching, parcel change detection and base map enrichment. The matching step is performed by checking the center point of each parcel in the other map parcels. Support vector machine and random forest classification algorithms are used to detect the changed parcels in the base map. The proposed models employ the genetic algorithm for feature selection and grey wolf optimization and Harris hawks optimization for hyperparameter optimization to improve accuracy and performance. By assessing the accuracies of the models, the random forest model with feature selection and grey wolf optimization, with an F1-score of 0.9018, was selected for the parcel change detection method. Finally, the detected changed parcels in the base map are deleted and relocated automatically with corresponding parcels in the more up-to-date map by the affine transformation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Smart Urban Cadastral Map Enrichment—A Machine Learning Method

Hajiheidari,

Delavar,

Rajabifard

2024

IJGI

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Detecting changes in green and blue spaces: Modeling based on statistical approach

Noszczyk

2023

Ecological Indicators

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Geodata in Science – A Review of Selected Scientific Fields

Apollo,

Jakubiak,

Nistor

et al. 2023

ASP.FC

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Aim of the study: Today, completely new forms of geo-information systems are becoming increasingly popular. New technological possibilities allow these systems to be adapted to the requirements and needs of societies. This paper is an extensive literature review of the use of geodata in various scientific fields, in STEM (science, technology, engineering, and mathematics) disciplines. However, as there is no universal agreement as to which disciplines are included in the STEM, the authors also included related fields such as geography or transport. Material and methods: Already a preliminary analysis of the Web of Science Core Collection database has shown that geodata is used very widely, in every scientific discipline analysed (with varying degrees of sophistication). The main objective of the paper is to provide a comprehensive analysis of the usage of geodata in five areas: bathymetric measurements, satellite geodata, aerial retrieved geodata, levelling networks, and GIS data. Results and conclusions: The results showed the increasing availability of data that can contribute to a better understanding and management of our planet. Geodata as a tool is overly broad and general, and it is used or might be used in every discipline of science.

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Austrian Cadastre still in use – Example proceedings to determine the legal status of land property in southern Poland

Cited by 6 publications

References 52 publications

Smart Urban Cadastral Map Enrichment—A Machine Learning Method

Smart Urban Cadastral Map Enrichment—A Machine Learning Method

Detecting changes in green and blue spaces: Modeling based on statistical approach

Geodata in Science – A Review of Selected Scientific Fields

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