Polish Cadastre Modernization with Remotely Extracted Buildings from High-Resolution Aerial Orthoimagery and Airborne LiDAR

Wierzbicki, Damian; Matuk, Olga; Bielecka, Elżbieta

doi:10.3390/rs13040611

Cited by 26 publications

(21 citation statements)

References 56 publications

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“…Lack of data currency is a frequent phenomenon and they have to be modernised to maintain their adequate quality. One should not forget the rapid technological progress [15][16][17][18][19][20], related to measurement capabilities, which makes data on the plot area and individual methods of land use more accurate. New technologies enable adding another (third) cadastral data dimension [21][22][23][24][25].…”

Section: Discussionmentioning

confidence: 99%

“…Cadastral data quality has been studied extensively [10][11][12][13][14][15][16][17][18] and it covers a wide range of topics. Data necessary to update cadastral data are increasingly often acquired using cuttingedge technologies [13,[15][16][17][18][19][20], including unmanned aerial vehicles (UAV). The issue of data transformation from 2D to 3D has been widely dealt with [21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…The real estate tax revenue accounts for part of the commune budget. Although an analysis of cadastral data has been dealt with in many pieces of scientific research in recent years [13][14][15][16][17][18][19][20], more studies are needed as they have a direct and indirect impact on socio-economic processes and methods of land use, etc. [8,34,35].…”

Section: Introductionmentioning

confidence: 99%

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Coherence of Cadastral Data in Land Management—A Case Study of Rural Areas in Poland

Kocur-Bera

Frąszczak

2021

Land

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The cadaster functions laid down in the law should guarantee the safety of one’s rights. The reliability of the data gathered in the cadaster affects decisions concerning specific real estate or taken within the sphere of economic management. The legislation often requires the use of cadastral data, which makes it necessary to keep it up-to-date and coherent with the situation in the field. The effects of a lack of coherence may impact public finances and land management. Maintaining high-quality cadastral data is time-consuming and expensive. This study analysed the data coherence between the state in the field and cadastral documents. The analysis was based mainly on the information about the area of a plot and land use. The coherence index showed that the differences between registers and the state in the field range from 30% to 80%. This can be changed by comprehensive data modernisation, which can be facilitated using modern technology. Given the diverse use of cadastral data and the global trends in cadaster development and implementation of the third dimension, the currency and reliability of cadastral data become particularly important.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Coherence of Cadastral Data in Land Management—A Case Study of Rural Areas in Poland

Kocur-Bera

Frąszczak

2021

Land

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“…Towards the latter part of the 2010s, spurred on by developments in artificial intelligence (AI) and machine learning, the idea of automating updates (and even initial data capture) via automated feature extraction techniques gained substantial research and development (R&D) focus [91][92][93][94][95][96]. Experimentation and piloting demonstrated the potential of the approach, including boundary change detection.…”

Section: Fit-for-purpose Land Administration Era (2010s Onwards)mentioning

confidence: 99%

Land Administration Maintenance: A Review of the Persistent Problem and Emerging Fit-for-Purpose Solutions

Bennett

Unger²,

Lemmen

et al. 2021

Land

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A contemporary review of land administration, from the perspective of systems maintenance, is provided. A special emphasis is placed on emerging fit-for-purpose land administration solutions. The research synthesis uses reputable sources from the contemporary era. Results show the challenges of maintaining land administration systems and the data held are long recognized. The 1970s–1980s gave the issue impetus as data and processes moved from paper-based and manual to digital and automated. The 1990s recognized concerns on maintenance, albeit as a secondary issue: system establishment was the primary concern. The 2000s placed more emphasis on more holistic sociotechnical systems but, again maintenance was supplementary. The fit-for-purpose era deliveres a vast range of new social and technological innovations; however, scaled and sustainable implementations still struggle with system maintenance. From the findings, a consolidated model for analyzing maintenance problems and solutions at jurisdictional level is developed. Maintenance of a land administration system can be understood by identifying the level of change, method for change, components to change, and options for what to change to. The United Nations-endorsed Framework for Effective Land Administration is then used to identify specific maintenance challenges and available solutions. It is suggested that due to the scope and size of what can be considered maintenance issues, there exists no single solution—instead the country should identify its persistant maintenance problems, and the most appropriate solution set from the suite of available options. Emerging solutions and challenges include ensuring interlinkage to maintenance of spatial planning, land valuation, and marine administration; exploiting survey data ‘back capture’ initiatives; supporting grassroots IT; and giving serious attention to cybersecurity concerns.

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“…In addition, a software-based module, ENVI deep learning, has recently been developed to simplify and perform deep learning procedures with geospatial data. The number of studies that have tested its potential is very small [31]; in particular, it has not been sufficiently explored for the detection of visible cadastral boundaries from UAV imagery.…”

Section: Deep Learning For Cadastral Mappingmentioning

confidence: 99%

Deep Learning for Detection of Visible Land Boundaries from UAV Imagery

2021

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Current efforts aim to accelerate cadastral mapping through innovative and automated approaches and can be used to both create and update cadastral maps. This research aims to automate the detection of visible land boundaries from unmanned aerial vehicle (UAV) imagery using deep learning. In addition, we wanted to evaluate the advantages and disadvantages of programming-based deep learning compared to commercial software-based deep learning. For the first case, we used the convolutional neural network U-Net, implemented in Keras, written in Python using the TensorFlow library. For commercial software-based deep learning, we used ENVINet5. UAV imageries from different areas were used to train the U-Net model, which was performed in Google Collaboratory and tested in the study area in Odranci, Slovenia. The results were compared with the results of ENVINet5 using the same datasets. The results showed that both models achieved an overall accuracy of over 95 %. The high accuracy is due to the problem of unbalanced classes, which is usually present in boundary detection tasks. U-Net provided a recall of 0.35 and a precision of 0.68 when the threshold was set to 0.5. A threshold can be viewed as a tool for filtering predicted boundary maps and balancing recall and precision. For equitable comparison with ENVINet5, the threshold was increased. U-Net provided more balanced results, a recall of 0.65 and a precision of 0.41, compared to ENVINet5 recall of 0.84 and a precision of 0.35. Programming-based deep learning provides a more flexible yet complex approach to boundary mapping than software-based, which is rigid and does not require programming. The predicted visible land boundaries can be used both to speed up the creation of cadastral maps and to automate the revision of existing cadastral maps and define areas where updates are needed. The predicted boundaries cannot be considered final at this stage but can be used as preliminary cadastral boundaries.

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Polish Cadastre Modernization with Remotely Extracted Buildings from High-Resolution Aerial Orthoimagery and Airborne LiDAR

Cited by 26 publications

References 56 publications

Coherence of Cadastral Data in Land Management—A Case Study of Rural Areas in Poland

Coherence of Cadastral Data in Land Management—A Case Study of Rural Areas in Poland

Land Administration Maintenance: A Review of the Persistent Problem and Emerging Fit-for-Purpose Solutions

Deep Learning for Detection of Visible Land Boundaries from UAV Imagery

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