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Remote Sensing for Land Administration 2.0

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 40718

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Special Issue Editors

Dr. Mila Koeva

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Guest Editor

Department of Urban and Regional Planning and Geo-Information Management, University of Twente, Drienerlolaan 5, 7522 NB Enschede, The Netherlands
Interests: 3D land information; photogrammetry and remote sensing; UAV; 3D modeling and visualization/digital twins
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Dr. Rohan Bennett

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Guest Editor

Department of Business Technology and Entrepreneurship, School of Business, Law and Entrepreneurship, University of Technology, Hawthorn, VIC 3122, Australia
Interests: ICT4D; land informatics; digital business
Special Issues, Collections and Topics in MDPI journals

Dr. Claudio Persello

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Faculty of Geo-Information Science and Earth Observation (ITC) of the University of Twente, Department of Earth Observation Science, P.O. Box 217, 7500 AE Enschede, The Netherlands
Interests: remote sensing; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue will contribute to the rapidly growing discourse on the use of remote sensing to Land administration (LA). Contemporary LA systems incorporate the concepts of cadastre and land registration. Conceptually, LA is part of a global land management paradigm incorporating land administration functions such as land value, land tenure, land development, and land use. The implementation of land-related policies integrated with well-maintained spatial information reflects the aim set by the United Nations to deliver tenure security for all (Sustainable Development Goal target 1.4, amongst others). In response to the global challenges of urbanization, complex urban infrastructure, innovative methods for data acquisition, processing, and maintaining spatial information are needed. Current technological developments in remote sensing and spatial information science provide enormous opportunities in this respect. Over the past decade, the increasing usage of unmanned aerial vehicles (UAVs), active remote sensing techniques as LiDAR and RADAR, resulted in data with high spatial, spectral, radiometric, and temporal resolution. Moreover, significant progress has also been achieved in automatic image orientation, surface reconstruction, scene analysis, change detection, classification, and automatic feature extraction with the help of artificial intelligence, spatial statistics, and machine learning. These technology developments, applied to LA, are now being actively demonstrated, piloted, and scaled. Therefore, building from the popularity of the earlier ‘Remote Sensing for Land Administration’ Special Issue in Remote Sensing, the specific focus of this Special Issue is exploring the usage and integration of emerging remote sensing techniques and their potential contribution to the domain of land administration. In this sense, the ‘2.0’ in the title refers both to this Special Issue being the second volume on the topic, and also the next-generation requirements of LA including demands for 3D, indoor, and real-time land data and information.

Topics may include but are not limited to the following:

Comparisons of remote sensing techniques for 2D and 3D data acquisition, processing, modelling, and analysis in support of land tenure mapping, land valuation, taxation, etc.
Design and testing of techniques for feature extraction/boundary delineation from remotely-sensed data sources (including semi-automated methods, algorithm design, and object-based approaches)
Reviews of leading scientific advances in data integration and utilization for 2D and 3D land administration

Dr. Mila Koeva
Dr. Rohan Bennett
Dr. Claudio Persello
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Land management
Land information
2D and 3D land data
Feature extraction
Machine learning for LA
Artificial intelligence for LA
UAVs
Land tenure (cadastre) mapping
Land registration
Land valuation and taxation
Land use planning and development

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Published Papers (10 papers)

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Editorial

Jump to: Research, Review

6 pages, 187 KiB

Open AccessEditorial

Remote Sensing for Land Administration 2.0

by Mila Koeva, Rohan Bennett and Claudio Persello

Remote Sens. 2022, 14(17), 4359; https://doi.org/10.3390/rs14174359 - 2 Sep 2022

Cited by 4 | Viewed by 1986

Abstract

Contemporary land administration (LA) systems incorporate the concepts of cadastre and land registration. Conceptually, LA is part of a global land management paradigm incorporating LA functions such as land value, land tenure, land development, and land use. The implementation of land-related policies integrated with well-maintained spatial information reflects the aim set by the United Nations to deliver tenure security for all (Sustainable Development Goal target 1.4, amongst many others). Innovative methods for data acquisition, processing, and maintaining spatial information are needed in response to the global challenges of urbanization and complex urban infrastructure. Current technological developments in remote sensing and geo-spatial information science provide enormous opportunities in this respect. Over the past decade, the increasing usage of unmanned aerial vehicles (UAVs), satellite and airborne-based acquisitions, as well as active remote sensing sensors such as LiDAR, resulted in high spatial, spectral, radiometric, and temporal resolution data. Moreover, significant progress has also been achieved in automatic image orientation, surface reconstruction, scene analysis, change detection, classification, and automatic feature extraction with the help of artificial intelligence, spatial statistics, and machine learning. These technology developments, applied to LA, are now being actively demonstrated, piloted, and scaled. This Special Issue hosts papers focusing on the usage and integration of emerging remote sensing techniques and their potential contribution to the LA domain. Full article

(This article belongs to the Special Issue Remote Sensing for Land Administration 2.0)

Research

Jump to: Editorial, Review

17 pages, 19021 KiB

Open AccessArticle

Using LiDAR System as a Data Source for Agricultural Land Boundaries

by Natalia Borowiec and Urszula Marmol

Remote Sens. 2022, 14(4), 1048; https://doi.org/10.3390/rs14041048 - 21 Feb 2022

Cited by 11 | Viewed by 3236

Abstract

In this study, LiDAR sensor data were used to identify agricultural land boundaries. This is a remote sensing method using a pulsating laser directed toward the ground. This study focuses on accurately determining the edges of parcels using only the point cloud, which is an original approach because the point cloud is a scattered set, which may complicate finding those points that define the course of a straight line defining the parcel boundary. The innovation of the approach is the fact that no data from other sources are supported. At the same time, a unique contribution of the research is the attempt to automate the complex process of detecting the edges of parcels. The first step was to classify the data, using intensity, and define land use boundaries. Two approaches were decided, for two test fields. The first test field was a rectangular shaped parcel of land. In this approach, pixels describing each edge of the plot separately were automatically grouped into four parts. The edge description was determined using principal component analysis. The second test area was the inner subdivision plot. Here, the Hough Transform was used to emerge the edges. Obtained boundaries, both for the first and the second test area, were compared with the boundaries from the Polish land registry database. Performed analyses show that proposed algorithms can define the correct course of land use boundaries. Analyses were conducted for the purpose of control in the system of direct payments for agriculture (Integrated Administration Control System—IACS). The aim of the control is to establish the borders and areas of croplands and to verify the declared group of crops on a given cadastral parcel. The proposed algorithm—based solely on free LiDAR data—allowed the detection of inconsistencies in farmers’ declarations. These mainly concerned areas of field roads that were misclassified by farmers as subsidized land, when in fact they should be excluded from subsidies. This is visible in both test areas with areas belonging to field roads with an average width of 1.26 and 3.01 m for test area no. 1 and 1.31, 1.15, 1.88, and 2.36 m for test area no. 2 were wrongly classified as subsidized by farmers. Full article

(This article belongs to the Special Issue Remote Sensing for Land Administration 2.0)

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32 pages, 18677 KiB

Open AccessArticle

Application of a Hand-Held LiDAR Scanner for the Urban Cadastral Detail Survey in Digitized Cadastral Area of Taiwan Urban City

by Shih-Hong Chio and Kai-Wen Hou

Remote Sens. 2021, 13(24), 4981; https://doi.org/10.3390/rs13244981 - 8 Dec 2021

Cited by 6 | Viewed by 2953

Abstract

The cadastral detail data is used for overlap analysis with digitized graphic cadastral maps to solve the problem of inconsistencies between cadastral maps and the current land situation. This study investigated the feasibility of a handheld LiDAR scanner to collect 3D point clouds in an efficient way for a detail survey in urban environments with narrow and winding streets. Then, urban detail point clouds were collected by the handheld LiDAR scanner. After point cloud filtering and the ranging systematic error correction that was determined by a plane-based calibration method, the collected point clouds were transformed to the TWD97 cadastral coordinate system using control points. The land detail line data were artificially digitized and the results showed that about 97% error of the digitized detail positions was less than 15 cm compared to the check points surveyed by a total station. The results demonstrated the feasibility of using a handheld LiDAR scanner to perform an urban cadastral detail survey in digitized graphic areas. Therefore, the handheld LiDAR scanner could be used for the production of the detail lines for urban cadastral detail surveying for digitized cadastral areas in Taiwan. Full article

(This article belongs to the Special Issue Remote Sensing for Land Administration 2.0)

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26 pages, 15981 KiB

Open AccessArticle

Remote Sensing for Property Valuation: A Data Source Comparison in Support of Fair Land Taxation in Rwanda

by Mila Koeva, Oscar Gasuku, Monica Lengoiboni, Kwabena Asiama, Rohan Mark Bennett, Jossam Potel and Jaap Zevenbergen

Remote Sens. 2021, 13(18), 3563; https://doi.org/10.3390/rs13183563 - 8 Sep 2021

Cited by 7 | Viewed by 5149

Abstract

Remotely sensed data is increasingly applied across many domains, including fit-for-purpose land administration (FFPLA), where the focus is on fast, affordable, and accurate property information collection. Property valuation, as one of the main functions of land administration systems, is influenced by locational, physical, legal, and economic factors. Despite the importance of property valuation to economic development, there are often no standardized rules or strict data requirements for property valuation for taxation in developing contexts, such as Rwanda. This study aims at assessing different remote sensing data in support of developing a new approach for property valuation for taxation in Rwanda; one that aligns with the FFPLA philosophy. Three different remote sensing technologies, (i) aerial images acquired with a digital camera, (ii) WorldView2 satellite images, and (iii) unmanned aerial vehicle (UAV) images obtained with a DJI Phantom 2 Vision Plus quadcopter, are compared and analyzed in terms of their fitness to fulfil the requirements for valuation for taxation purposes. Quantitative and qualitative methods are applied for the comparative analysis. Prior to the field visit, the fundamental concepts of property valuation for taxation and remote sensing were reviewed. In the field, reference data using high precision GNSS (Leica) was collected and used for quantitative assessment. Primary data was further collected via semi-structured interviews and focus group discussions. The results show that UAVs have the highest potential for collecting data to support property valuation for taxation. The main reasons are the prime need for accurate-enough and up-to-date information. The comparison of the different remote sensing techniques and the provided new approach can support land valuers and professionals in the field in bottom-up activities following the FFPLA principles and maintaining the temporal quality of data needed for fair taxation. Full article

(This article belongs to the Special Issue Remote Sensing for Land Administration 2.0)

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28 pages, 13673 KiB

Open AccessArticle

Building Change Detection Method to Support Register of Identified Changes on Buildings

by Dušan Jovanović, Milan Gavrilović, Dubravka Sladić, Aleksandra Radulović and Miro Govedarica

Remote Sens. 2021, 13(16), 3150; https://doi.org/10.3390/rs13163150 - 9 Aug 2021

Cited by 5 | Viewed by 3271

Abstract

Based on a newly adopted “Rulebook on the records of identified changes on buildings in Serbia” (2020) that regulates the content, establishment, maintenance and use of records on identified changes on buildings, it is expected that the geodetic-cadastral information system will be extended with these records. The records contain data on determined changes of buildings in relation to the reference epoch of aerial or satellite imagery, namely data on buildings: (1) that are not registered in the real estate cadastre; (2) which are registered in the real estate cadastre, and have been changed in terms of the dimensions in relation to the data registered in the real estate cadastre; (3) which are registered in the real estate cadastre, but are removed on the ground. For this purpose, the LADM-based cadastral data model for Serbia is extended to include records on identified changes on buildings. In the year 2020, Republic Geodetic Authority commenced a new satellite acquisition for the purpose of restoration of official buildings registry, as part of a World Bank project for improving land administration in Serbia. Using this satellite imagery and existing cadastral data, we propose a method based on comparison of object-based and pixel-based image analysis approaches to automatically detect newly built, changed or demolished buildings and import these data into extended cadastral records. Our results, using only VHR images containing only RGB and NIR bands, showed object identification accuracy ranging from 84% to 88%, with kappa statistic from 89% to 96%. The accuracy of obtained results is satisfactory for the purpose of developing a register of changes on buildings to keep cadastral records up to date and to support activities related to legalization of illegal buildings, etc. Full article

(This article belongs to the Special Issue Remote Sensing for Land Administration 2.0)

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19 pages, 4532 KiB

Open AccessArticle

Deep Learning for Detection of Visible Land Boundaries from UAV Imagery

by Bujar Fetai, Matej Račič and Anka Lisec

Remote Sens. 2021, 13(11), 2077; https://doi.org/10.3390/rs13112077 - 25 May 2021

Cited by 16 | Viewed by 5007

Abstract

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. Full article

(This article belongs to the Special Issue Remote Sensing for Land Administration 2.0)

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23 pages, 2444 KiB

Open AccessArticle

Testing and Validating the Suitability of Geospatially Informed Proxies on Land Tenure in North Korea for Korean (Re-)Unification

by Cheonjae Lee and Walter Timo de Vries

Remote Sens. 2021, 13(7), 1301; https://doi.org/10.3390/rs13071301 - 29 Mar 2021

Cited by 2 | Viewed by 2968

Abstract

The role of remote sensing data in detecting, estimating, and monitoring socioeconomic status (SES) such as quality of life dimensions and sustainable development prospects has received increased attention. Geospatial data has emerged as powerful source of information for enabling both socio-technical assessment and socio-legal analysis in land administration domain. In the context of Korean (re-)unification, there is a notable paucity of evidence how to identify unknowns in North Korea. The main challenge is the lack of complete and adequate information when it comes to clarifying unknown land tenure relations and land governance arrangements. Deriving informative land tenure relations from geospatial data in line with socio-economic land attributes is currently the most innovative approach. In-close and in-depth investigations of validating the suitability of a set of geospatially informed proxies combining multiple values were taken into consideration, as were the forms of knowledge co-production. Thus, the primary aim is to provide empirical evidence of whether proposed proxies are scientifically valid, policy-relevant, and socially robust. We revealed differences in the distributions of agreements relating to land ownership and land transfer rights identification among scientists, bureaucrats, and stakeholders. Moreover, we were able to measure intrinsic, contextual, representational, and accessibility attributes of information quality regarding the associations between earth observation (EO) data and land tenure relations in North Korea from a number of different viewpoints. This paper offers valuable insights into new techniques for validating suitability of EO data proxies in the land administration domain off the reliance on conventional practices formed and customized to the specific artefacts and guidelines of the remote sensing community. Full article

(This article belongs to the Special Issue Remote Sensing for Land Administration 2.0)

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20 pages, 22448 KiB

Open AccessArticle

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

by Damian Wierzbicki, Olga Matuk and Elzbieta Bielecka

Remote Sens. 2021, 13(4), 611; https://doi.org/10.3390/rs13040611 - 8 Feb 2021

Cited by 25 | Viewed by 3466

Abstract

Automatic building extraction from remote sensing data is a hot but challenging research topic for cadastre verification, modernization and updating. Deep learning algorithms are perceived as more promising in overcoming the difficulties of extracting semantic features from complex scenes and large differences in buildings’ appearance. This paper explores the modified fully convolutional network U-Shape Network (U-Net) for high resolution aerial orthoimagery segmentation and dense LiDAR data to extract building outlines automatically. The three-step end-to-end computational procedure allows for automated building extraction with an 89.5% overall accuracy and an 80.7% completeness, which made it very promising for cadastre modernization in Poland. The applied algorithms work well both in densely and poorly built-up areas, typical for peripheral areas of cities, where uncontrolled development had recently been observed. Discussing the possibilities and limitations, the authors also provide some important information that could help local authorities decide on the use of remote sensing data in land administration. Full article

(This article belongs to the Special Issue Remote Sensing for Land Administration 2.0)

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23 pages, 42057 KiB

Open AccessArticle

High-Quality UAV-Based Orthophotos for Cadastral Mapping: Guidance for Optimal Flight Configurations

by Claudia Stöcker, Francesco Nex, Mila Koeva and Markus Gerke

Remote Sens. 2020, 12(21), 3625; https://doi.org/10.3390/rs12213625 - 4 Nov 2020

Cited by 29 | Viewed by 5048

Abstract

During the past years, unmanned aerial vehicles (UAVs) gained importance as a tool to quickly collect high-resolution imagery as base data for cadastral mapping. However, the fact that UAV-derived geospatial information supports decision-making processes involving people’s land rights ultimately raises questions about data quality and accuracy. In this vein, this paper investigates different flight configurations to give guidance for efficient and reliable UAV data acquisition. Imagery from six study areas across Europe and Africa provide the basis for an integrated quality assessment including three main aspects: (1) the impact of land cover on the number of tie-points as an indication on how well bundle block adjustment can be performed, (2) the impact of the number of ground control points (GCPs) on the final geometric accuracy, and (3) the impact of different flight plans on the extractability of cadastral features. The results suggest that scene context, flight configuration, and GCP setup significantly impact the final data quality and subsequent automatic delineation of visual cadastral boundaries. Moreover, even though the root mean square error of checkpoint residuals as a commonly accepted error measure is within a range of few centimeters in all datasets, this study reveals large discrepancies of the accuracy and the completeness of automatically detected cadastral features for orthophotos generated from different flight plans. With its unique combination of methods and integration of various study sites, the results and recommendations presented in this paper can help land professionals and bottom-up initiatives alike to optimize existing and future UAV data collection workflows. Full article

(This article belongs to the Special Issue Remote Sensing for Land Administration 2.0)

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Review

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36 pages, 6032 KiB

Open AccessFeature PaperReview

Review of Remote Sensing for Land Administration: Origins, Debates, and Selected Cases

by Rohan Mark Bennett, Mila Koeva and Kwabena Asiama

Remote Sens. 2021, 13(21), 4198; https://doi.org/10.3390/rs13214198 - 20 Oct 2021

Cited by 7 | Viewed by 4430

Abstract

Conventionally, land administration—incorporating cadastres and land registration—uses ground-based survey methods. This approach can be traced over millennia. The application of photogrammetry and remote sensing is understood to be far more contemporary, only commencing deeper into the 20th century. This paper seeks to counter this view, contending that these methods are far from recent additions to land administration: successful application dates back much earlier, often complementing ground-based methods. Using now more accessible historical works, made available through archive digitisation, this paper presents an enriched and more complete synthesis of the developments of photogrammetric methods and remote sensing applied to the domain of land administration. Developments from early phototopography and aerial surveys, through to analytical photogrammetric methods, the emergence of satellite remote sensing, digital cameras, and latterly lidar surveys, UAVs, and feature extraction are covered. The synthesis illustrates how debates over the benefits of the technique are hardly new. Neither are well-meaning, although oft-flawed, comparative analyses on criteria relating to time, cost, coverage, and quality. Apart from providing this more holistic view and a timely reminder of previous work, this paper brings contemporary practical value in further demonstrating to land administration practitioners that remote sensing for data capture, and subsequent map production, are an entirely legitimate, if not essential, part of the domain. Contemporary arguments that the tools and approaches do not bring adequate accuracy for land administration purposes are easily countered by the weight of evidence. Indeed, these arguments may be considered to undermine the pragmatism inherent to the surveying discipline, traditionally an essential characteristic of the profession. That said, it is left to land administration practitioners to determine the relevance of these methods for any specific country context. Full article

(This article belongs to the Special Issue Remote Sensing for Land Administration 2.0)

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