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In developing nations, such as Ekiti State, Nigeria, the utilization of remotely sensed data, particularly satellite and UAV imagery, remains significantly underexploited in land administration. This limits multi-resolution imagery’s potential in land governance and socio-economic development. This study examines factors influencing UAV adoption for land administration in Nigeria, mapping seven rural, peri-urban, and urban sites with orthomosaics (2.2 cm to 3.39 cm resolution). Boundaries were manually delineated, and parcel areas were calculated. Using the 0.05 m orthomosaic as a reference, the Horizontal Radial Root Mean Square Error (RMSEr) and Normalized Parcel Area Error (NPAE) were computed. Results showed a consistent increase in error with increasing resolution (0.1 m to 1 m), with RMSEr ranging from 0.053 m (formal peri-urban) to 2.572 m (informal rural settlement). Formal settlements with physical demarcations exhibited more consistent values. A comparison with GNSS data revealed that RMSEr values conformed to the American Society for Photogrammetry and Remote Sensing (ASPRS) Class II and III standards. The research demonstrates physical demarcations’ role in facilitating cadastral mapping, with formal settlements showing the highest suitability. This study recommends context-specific imagery resolution to enhance land governance. Key implications include promoting settlement typology awareness and addressing UAV regulatory challenges. NPAE values can serve as a metric for assessing imagery resolution fitness for cadastral mapping.

Rapid urban growth necessitates focused attention regarding its policy and governance to ensure affordable housing, transparent and efficient real-world systems, reduce social inequalities, and promote sustainable development. This study delves into the semantics and ontology for developing a Land Administration Domain Model (LADM) profile in the context of Pakistan’s Land Administration Systems (LASs), which currently face issues due to manual record-keeping, lack of transparency, frauds, and disintegration. Establishing a baseline through Record of Rights (RoR) and Property Information Report (PIR), alongside surveying and mapping procedures defined by laws and rules, forms the foundation for LADM profile development. This study explores the transition from manual LAS to 2D/3D representation, using LADM as a conceptual guideline. The LADM profile’s three key packages—PK_Party, PK_Administrative, and PK_SpatialUnit—a sub-package, and external classes are examined, with proposals for digitalisation and modernisation. Additionally, the study includes expert consultation, and highlights the significant support that the LADM implementation offers to achieve Sustainable Development Goals (SDGs) in Pakistan. In conclusion, the study underscores the need for a comprehensive and inclusive approach to address organisational overlaps and ambiguities within LAS, positioning PK LADM as a transformative force for sustainable urban LAS in Pakistan, aligning with broader SDGs. Recommendations include exploring realistic land valuation, integrated ownership and location verification systems, addressing historical survey data challenges, and promoting wider stakeholder adoption for sustainable 2D/3D urban LAS using LADM and its edition II as a way forward towards the creation of a smart city and digital twin.

Delineating and modelling building roof plane structures is an active research direction in urban-related studies, as understanding roof structure provides essential information for generating highly detailed 3D building models. Traditional deep-learning models have been the main focus of most recent research endeavors aiming to extract pixel-based building roof plane areas from remote-sensing imagery. However, significant challenges arise, such as delineating complex roof boundaries and invisible boundaries. Additionally, challenges during the post-processing phase, where pixel-based building roof plane maps are vectorized, often result in polygons with irregular shapes. In order to address this issue, this study explores a state-of-the-art method for planar graph reconstruction applied to building roof plane extraction. We propose a framework for reconstructing regularized building roof plane structures using aerial imagery and cadastral information. Our framework employs a holistic edge classification architecture based on an attention-based neural network to detect corners and edges between them from aerial imagery. Our experiments focused on three distinct study areas characterized by different roof structure topologies: the Stadsveld–‘t Zwering neighborhood and Oude Markt area, located in Enschede, The Netherlands, and the Lozenets district in Sofia, Bulgaria. The outcomes of our experiments revealed that a model trained with a combined dataset of two different study areas demonstrated a superior performance, capable of delineating edges obscured by shadows or canopy. Our experiment in the Oude Markt area resulted in building roof plane delineation with an F-score value of 0.43 when the model trained on the combined dataset was used. In comparison, the model trained only on the Stadsveld–‘t Zwering dataset achieved an F-score value of 0.37, and the model trained only on the Lozenets dataset achieved an F-score value of 0.32. The results from the developed approach are promising and can be used for 3D city modelling in different urban settings.

Digital twins (DTs) have been found useful in manufacturing, construction, and maintenance. Adapting DTs to serve cities, the question arises of what an urban digital twin should contain and how it should be orchestrated to serve a city’s dynamical ecosystem, along with how to enhance the efficiency of the city. We are aligning with the commonplace idea that the main advantage of using DTs is economical as, for example, DTs can improve the planning of activities thus saving money and time. But how can they be useful for a city? Instead of looking at the DTs as solutions in search of problems to be solved, we start from city needs. Our approach is two-fold. We start by briefly reviewing existing possibilities for meeting some specific needs, but keep the focus on identifying and attempting to close the gap between the needs arising from everyday city functions and the latest DT techniques useful for meeting those needs. DTs are technically different and serve different applications, yet they share a common identity and name, as well as several technical similarities. Adopting computer science terminology, we see a back-end city DT as the container of all information, while any single front-end, visualized or used either by humans or robots, offers a limited but meaningful representation of the DT for a specific application. Alas, there are multiple open questions regarding the realization and benefits of such back-end DT. Nevertheless, we discuss how the back-end DT (or any specific DT) could be updated autonomously from sensor data using artificial intelligence techniques, and how the front-ends could be used for large benefits to the entire city ecosystem.

Fit-for-purpose land administration (FFPLA) seeks to simplify cadastral mapping via lowering the costs and time associated with conventional surveying methods. This approach can be applied to both the initial establishment and on-going maintenance of the system. In Ethiopia, cadastral maintenance remains an on-going challenge, especially in rapidly urbanizing peri-urban areas, where farmers’ land rights and tenure security are often jeopardized. Automatic Feature Extraction (AFE) is an emerging FFPLA approach, proposed as an alternative for mapping and updating cadastral boundaries. This study explores the role of the AFE approach for updating cadastral boundaries in the vibrant peri-urban areas of Addis Ababa. Open-source software solutions were utilized to assess the (semi-) automatic extraction of cadastral boundaries from orthophotos (segmentation), designation of “boundary” and “non-boundary” outlines (classification), and delimitation of cadastral boundaries (interactive delineation). Both qualitative and quantitative assessments of the achieved results (validation) were undertaken. A high-resolution orthophoto of the study area and a reference cadastral boundary shape file were used, respectively, for extracting the parcel boundaries and validating the interactive delineation results. Qualitative (visual) assessment verified the completed extraction of newly constructed cadastral boundaries in the study area, although non-boundary outlines such as footpaths and artifacts were also retrieved. For the buffer overlay analysis, the interactively delineated boundary lines and the reference cadastre were buffered within the spatial accuracy limits for urban and rural cadastres. As a result, the quantitative assessment delivered 52% correctness and 32% completeness for a buffer width of 0.4 m and 0.6 m, respectively, for the interactively delineated and reference boundaries. The study proposed publicly available software solutions and outlined a workflow to (semi-) automatically extract cadastral boundaries from aerial/satellite images. It further demonstrated the potentially significant role AFE could play in delivering fast, affordable, and reliable cadastral mapping. Further investigation, based on user input and expertise evaluation, could help to improve the approach and apply it to a real-world setting.

The urban land administration system (LAS) of any country serves as a key pillar for good governance, resource planning, service delivery, infrastructure development, and revenue collection. To reform their LASs, countries need a thorough understanding of their existing context and global relevance. The goal of this paper is to examine the status and challenges of urban LASs in Pakistan using the United Nations Framework for Effective Land Administration (FELA). The exploratory case study method used in the paper employs a mixed approach, which includes FELA-based questionnaire surveys, group discussions, and desk reviews. A total of 525 urban LAS stakeholders, including owner-buyers, real estate agents, bankers, lawyers, and LAS organizations, participated in the activity. The results show that more than half of the stakeholders are not satisfied with existing urban LASs, their governance and accountability, laws, and policies. Corruption is prevalent mostly in government organizations. Fraud and joint ownership are the most common sources of dispute, with 67 percent of the respondents stating that the cases take more than two years to resolve in court. The financial aspect of urban LASs is suffering due to property undervaluation and low revenue collection. Manual data and record keeping in LASs further complicate the system, with 87 percent of all respondents interested in innovating the urban LAS using modern technologies. Furthermore, 92 percent of all respondents expressed the need to standardize the existing LASs. There is a lack of capacity and skills, and 89 percent of organizations’ respondents believe that human resources skilled in Geographical Information Systems (GIS) and Remote Sensing (RS) can improve the efficiency of urban LASs. There is a lack of partnership among LAS organizations and a gap in the accessibility of LAS-related quality information. The country’s vision of building smart cities can be realized through LAS standardization and 3D and GIS innovation.

Cultural heritage is under tremendous pressure in the rapidly growing and transforming cities of the global south. Historic cities and towns are often faced with the dilemma of having to preserve old monuments while responding to the pressure of adapting itself to a modern lifestyle, which often results in the loss of cultural heritage. Indian cities such as Delhi possess a rich legacy of tangible heritage, traditions, and arts, which are reflected in their present urban form. The creation of temporal 3D models of such cities not only provides a platform with which one can experience the past, but also helps to understand, examine, and improve its present deteriorating state. However, gaining access to historical data to support the development of city-scale 3D models is a challenge. While data gaps can be bridged by combining multiple data sources, this process also presents considerable technical challenges. This paper provides a framework to generate LoD-2 (level-of-detail) 3D models of the present (the 2020s) and the past (the 1970s) of a heritage mosque surrounded by a dense and complex urban settlement in Shahjahanabad (Old Delhi) by combining multiple VHR (very high resolution) satellite images. The images used are those of Pleiades and Worldview-1 and -3 (for the present) and HEXAGON KH-9 declassified spy images (for the past). The chronological steps are used to extract the DSMs and DTMs that provide a base for the 3D models. The models are rendered, and the past and present are visualized using graphics and videos. The results reveal an average increase of 80% in the heights of the built structures around the main monument (mosque), leading to a loss in the visibility of this central mosque.

The idea of a walkable city refers to the extent to which the built environment encourages people to walk by establishing comfortable pedestrian routes, which allows people to connect to numerous services with reasonable effort and time. Walkability is currently regarded as a “good to know about” rather than a “must-have” factor for sustainable development. A combination of walkability with a standard design strategy, such as generative design, may result in a more efficient way of planning a walkable city. Interestingly, the sole indicator taken into account for walkability in the generative design domain is “distance to amenities”, while in reality, other parameters, such as the comfort factor, could also influence walkability. Therefore, in this research, we developed a workflow based on the generative design, which considers the comfort dimension in combination with distance to amenities and street-level greeneries. We also included the human perspective, given that walkability is always personal. This research successfully generated three different scenarios of walkability-optimal urban plans, where the highest walkability is 82.43 (very walkable). Furthermore, the baseline scenario of two different locations also aligns with people’s perspectives when compared. In addition, we found that the inclusion of a temporal dimension, enhanced 3D-related indicators, and constraints should benefit future research.

Recent advances in Computer Science and the spread of internet connection have allowed specialists to virtualize complex environments on the web and offer further information with realistic exploration experiences. At the same time, the fruition of complex geospatial datasets (point clouds, Building Information Modelling (BIM) models, 2D and 3D models) on the web is still a challenge, because usually it involves the usage of different proprietary software solutions, and the input data need further simplification for computational effort reduction. Moreover, integrating geospatial datasets acquired in different ways with various sensors remains a challenge. An interesting question, in that respect, is how to integrate 3D information in a 3D GIS (Geographic Information System) environment and manage different scales of information in the same application. Integrating a multiscale level of information is currently the first step when it comes to digital twinning. It is needed to properly manage complex urban datasets in digital twins related to the management of the buildings (cadastral management, prevention of natural and anthropogenic hazards, structure monitoring, etc.). Therefore, the current research shows the development of a freely accessible 3D Web navigation model based on open-source technology that allows the visualization of heterogeneous complex geospatial datasets in the same virtual environment. This solution employs JavaScript libraries based on WebGL technology. The model is accessible through web browsers and does not need software installation from the user side. The case study is the new building of the University of Twente—Faculty of Geo-Information (ITC), located in Enschede (the Netherlands). The developed solution allows switching between heterogeneous datasets (point clouds, BIM, 2D and 3D models) at different scales and visualization (indoor first-person navigation, outdoor navigation, urban navigation). This solution could be employed by governmental stakeholders or the private sector to remotely visualize complex datasets on the web in a unique visualization, and take decisions only based on open-source solutions. Furthermore, this system can incorporate underground data or real-time sensor data from the IoT (Internet of Things) for digital twinning tasks.

Despite the significant and explicit focus on the United Nations Sustainable Development Goals (SDGs), much of the world’s land rights remain unrecorded and outside formal government systems. Blame is often placed on land administration processes that are considered slow, expensive, and expertise-dependent. Fit-For-Purpose Land Administration (FFPLA) has been suggested as an alternative, time and cost-effective approach. Likewise, the UN endorsed Framework for Effective Land Administration (FELA) demands attention to worldwide tenure insecurity by directly linking it to responsible land administration. Implementation of FFPLA and FELA is country-context dependent, and there are now many lessons of execution from various jurisdictions. Undertaken in 2022, this study synthesizes a review of experiences to provide a further update on the best global FFPLA implementation practices and inform approaches for future FFPLA projects. A systematic review is adopted as the research methodology, and contemporary articles from the internationally recognized land administration discourse are examined. The studies focus on FFPLA implementation practices and innovative approaches for delivering land tenure security. A checklist is developed, based on the FELA strategic pathways and the FFPLA fundamental framework principles and characteristic elements, to identify best implementation practices. Success stories across the globe show that the FFPLA characteristic elements and the FELA pathway goals are achieved through effective execution of the FFPLA framework key principles. As a result, the study identified successful FFPLA implementation practices in Asia and Africa, which can be synthesized and extended to realize tenure security in rapidly urbanizing areas. However, further study is necessary to determine the efficacy, practicability, innovativeness, and transferability of the best practices to other land administration scenarios.

Increasing urbanisation has inevitably led to the continuous construction of buildings. Urban expansion and densification processes reshape cities and, in particular, the third dimension (3D), thus calling for a technical shift from 2D to 3D for property valuation. However, most property valuation studies employ 2D geoinformation in hedonic price models, while the benefits of 3D modelling potentially brought for property valuation and the general context of digital twin (DT) creation are not sufficiently explored. Therefore, this review aims to identify appropriate urban 3D modelling method(s) for city DT, which can be used for 3D property valuation (3DPV) in the future (both short-term and long-term). We focused on 3D modelling studies investigating buildings and urban elements directly linked with residential properties. In total, 180 peer-reviewed journal papers were selected between 2016 and 2020 with a narrative review approach. Analytical criteria for 3D modelling methods were explicitly defined and covered four aspects: metadata, technical characteristics, users’ requirements, and ethical considerations. From this, we derived short-term and long-term prospects for 3DPV. The results provide references for integrating 3D modelling and DT in property valuation and call for interdisciplinary collaboration including researchers and stakeholders in the real estate sector, such as real estate companies, house buyers and local governments.

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.

The 3D cadastre, one of the most fundamental components of the 3D LAS (Land Administration System), aims to provide an integrated 3D view of legal (Right, Restrictions, and Responsibilities–RRR) and physical boundaries in multi-storey properties; therefore, to extend the flexible and modular basis of LADM, which best represents legal boundaries and RRRs, some integrated models using technical standards (e.g., CityGML, IFC, InfraGML) are developed to represent the full 3D cadastre. However, since most of the developed 3D integrated data models are designed at the conceptual level, there is a knowledge gap in logical data model relationships, which is the next processing step in the fully integrated 3D data model stage. The main argument of this study is an innovative ADE 4D Cadastral Data Model to represent 3D cadastral objects registration with time attributes using LADM and CityGML. The data management and organization are done in an open-source database for the Turkish cadastral system. This research will discuss two main topics. The first is how to implement a suitable way of realising LADM-based 3D cadastral object registration by focusing on developing the presentation of those cadastral objects to 4D (3D + t), with time attributes in alignment with the jurisdictional framework in Turkey. The second is how the data is managed in an open-source PostgreSQL database. In addition, the usage type of cadastral objects is shown in a CesiumJS, a visualisation platform. Moreover, this study will contribute to eliminating the knowledge gap between the conceptual and logical models.

China has experienced fast urbanization with a growing urban population, which has inevitably led to the adoption of a vertical housing style with high-rise buildings. However, how people subjectively perceive the vertical dimension (3D) in the property market is neither adequately documented nor well understood. The 3D perception helps us to understand a myriad of social and psychological effects of living in high-rise buildings. We organized and conducted semi-structured expert interviews, focus groups, and the circulation and compilation of questionnaires in Xi’an, China, to investigate how different stakeholders in the property market perceive 3D. The results show that: (1) real estate developers do not adjust property prices for specific 3D factors, and the local government does not consider 3D in housing policies; (2) the current status of 3D modeling in Xi’an is still in the embryonic state; (3) 3D factors are highly valued by buyers but not well-understood by real estate developers and local government. In addition, 3D factors score higher than horizontal (2D) factors (1.12 to 0.88). Gender and age groups do not influence housing preferences concerning 2D and 3D factors. These findings provide valuable insights for real estate developers concerning pricing policies and the local government concerning housing policies. In the future, 3D perceptions and factors should be prioritized in order to improve urban infrastructure and ensure the increased availability of, and fair public access to welfare related to 3D in urban areas.

Zoning regulation is considered as a tool used by government to control developments to ensure sustainability. In Ghana where about 80% of lands are held under customary land tenure systems, implementation of residential standards, which is a government function may conflict with customary norms of holding land. This paper uses case study to examine the implementation of residential policies and enforcement of residential standards in areas under customary land tenure in Ghana and if these policies and standards affect the enjoyment of land rights in the context of customary land tenure. Results showed that non-compliance to residential standards and non-conformity to the local plan has minimal interference on enjoyment of land rights. Residents are ignorant of the details of the residential standards and have never seen a zoning regulations document. There is also low level of monitoring and enforcement. Spatial analysis reveals four main types of non-conformity between orthophoto and local plans i) discrepancies in the orientation of the parcel boundaries, ii) discrepancies in the shapes of plot boundaries, iii) houses constructed on the plot boundary or straddle parcel boundaries, and iii) differences in plot sizes. Results suggest the need for planning authority to use efficient approaches such as GIS and UAV’s to communicate, monitor and enforce the residential standards. It is concluded that collaboration between customary land authorities and the Municipality during the allocation and development of plots may improve spatial conformity between orthophoto and the local plans.

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.

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.

The well-recognized and extensive task of mapping unrecorded land rights across sub-Saharan Africa demands innovative solutions. In response, the consortia of “its4land”, a European Commission Horizon 2020 project, developed, adapted, and tested innovative geospatial tools including (1) software underpinned by the smart Sketch maps concept, called SmartSkeMa; (2) a workflow for applying unmanned aerial vehicles (UAV); and (3) a boundary delineator tool based on the UAV images. Additionally, the consortium developed (4) a platform called Publish and Share (PaS), enabling integration of all the outputs of tool sharing and publishing of land information through geocloud web services. The individual tools were developed, tested, and demonstrated based on requirements from Rwanda, Kenya, Ethiopia, and Zanzibar. The platform was further tested by key informants and experts in a workshop in Rwanda after the AfricaGIS conference in 2019. With the project concluding in 2020, this paper seeks to undertake an assessment of the tools and the PaS platform against the elements of fit-for-purpose land administration. The results show that while the tools can function and deliver outputs independently and reliably, PaS enables interoperability by allowing them to be combined and integrated into land administration workflows. This feature is useful for tailoring approaches for specific country contexts. In this regard, developers of technical approaches tackling land administration issues are further encouraged to include interoperability and the use of recognized standards in designs.

Recent rapid population growth and increasing urbanisation have led to fast vertical developments in urban areas. Therefore, in the context of the dynamic property market, factors related to the third dimension (3D) need to be considered. Current hedonic price modelling (HPM) studies have little explicit consideration for the third dimension, which may have a significant influence on modelling property values in complex urban environments. Therefore, our research aims to narrow the cognitive gap of the missing third dimension by assessing both 2D and 3D HPM and identifying important 3D factors for spatial analysis and visualisation in the selected study area, Xi’an, China. The statistical methods we used for 2D HPM are ordinary least squares (OLS) and geographically weighted regression (GWR). In 2D HPM, they both have very low R2 (0.111 in OLS and 0.217 in GWR), showing a very limited generalisation potential. However, a significant improvement is observed when adding 3D factors, namely view quality, sky view factor (SVF), sunlight and property orientation. The obtained higher R2 (0.414) shows the importance of the third dimension or—3D factors for HPM. Our findings demonstrate the necessity to include such factors into HPM and to develop 3D models with a higher level of details (LoD) to serve more purposes such as fair property taxation.

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.

The COVID-19 health emergency is impacting all of our lives, but the living conditions and urban morphologies found in poor communities make inhabitants more vulnerable to the COVID-19 outbreak as compared to the formal city, where inhabitants have the resources to follow WHO guidelines. In general, municipal spatial datasets are not well equipped to support spatial responses to health emergencies, particularly in poor communities. In such critical situations, Earth observation (EO) data can play a vital role in timely decision making and can save many people’s lives. This work provides an overview of the potential of EO-based global and local datasets, as well as local data gathering procedures (e.g., drones), in support of COVID-19 responses by referring to two slum areas in Salvador, Brazil as a case study. We discuss the role of datasets as well as data gaps that hinder COVID-19 responses. In Salvador and other low- and middle-income countries’ (LMICs) cities, local data are available; however, they are not up to date. For example, depending on the source, the population of the study areas in 2020 varies by more than 20%. Thus, EO data integration can help in updating local datasets and in the acquisition of physical parameters of poor urban communities, which are often not systematically collected in local surveys.

Land administration constitutes the socio-technical systems that govern land tenure, use, value and development within a jurisdiction. The land parcel is the fundamental unit of analysis. Each parcel has identifiable boundaries, associated rights, and linked parties. Spatial information is fundamental. It represents the boundaries between land parcels and is embedded in cadastral sketches, plans, maps and databases. The boundaries are expressed in these records using mathematical or graphical descriptions. They are also expressed physically with monuments or natural features. Ideally, the recorded and physical expressions should align, however, in practice, this may not occur. This means some boundaries may be physically invisible, lacking accurate documentation, or potentially both. Emerging remote sensing tools and techniques offers great potential. Historically, the measurements used to produce recorded boundary representations were generated from ground-based surveying techniques. The approach was, and remains, entirely appropriate in many circumstances, although it can be timely, costly, and may only capture very limited contextual boundary information. Meanwhile, advances in remote sensing and photogrammetry offer improved measurement speeds, reduced costs, higher image resolutions, and enhanced sampling granularity. Applications of unmanned aerial vehicles (UAV), laser scanning, both airborne and terrestrial (LiDAR), radar interferometry, machine learning, and artificial intelligence techniques, all provide examples. Coupled with emergent societal challenges relating to poverty reduction, rapid urbanisation, vertical development, and complex infrastructure management, the contemporary motivation to use these new techniques is high. Fundamentally, they enable more rapid, cost-effective, and tailored approaches to 2D and 3D land data creation, analysis, and maintenance. This Special Issue hosts papers focusing on this intersection of emergent remote sensing tools and techniques, applied to domain of land administration.

There exists a demand for effective land administration systems that can support the protection of unrecorded land rights, thereby assisting to reduce poverty and support national development—in alignment with target 1.4 of UN Sustainable Development Goals (SDGs). It is estimated that only 30% of the world’s population has documented land rights recorded within a formal land administration system. In response, we developed, adapted, applied, and tested innovative remote sensing methodologies to support land rights mapping, including (1) a unique ontological analysis approach using smart sketch maps (SmartSkeMa); (2) unmanned aerial vehicle application (UAV); and (3) automatic boundary extraction (ABE) techniques, based on the acquired UAV images. To assess the applicability of the remote sensing methodologies several aspects were studied: (1) user needs, (2) the proposed methodologies responses to those needs, and (3) examine broader governance implications related to scaling the suggested approaches. The case location of Kajiado, Kenya is selected. A combination of quantitative and qualitative results resulted from fieldwork and workshops, taking into account both social and technical aspects. The results show that SmartSkeMa was potentially a versatile and community-responsive land data acquisition tool requiring little expertise to be used, UAVs were identified as having a high potential for creating up-to-date base maps able to support the current land administration system, and automatic boundary extraction is an effective method to demarcate physical and visible boundaries compared to traditional methodologies and manual delineation for land tenure mapping activities.

Cadastral boundaries are often demarcated by objects that are visible in remote sensing imagery. Indirect surveying relies on the delineation of visible parcel boundaries from such images. Despite advances in automated detection and localization of objects from images, indirect surveying is rarely automated and relies on manual on-screen delineation. We have previously introduced a boundary delineation workflow, comprising image segmentation, boundary classification and interactive delineation that we applied on Unmanned Aerial Vehicle (UAV) data to delineate roads. In this study, we improve each of these steps. For image segmentation, we remove the need to reduce the image resolution and we limit over-segmentation by reducing the number of segment lines by 80% through filtering. For boundary classification, we show how Convolutional Neural Networks (CNN) can be used for boundary line classification, thereby eliminating the previous need for Random Forest (RF) feature generation and thus achieving 71% accuracy. For interactive delineation, we develop additional and more intuitive delineation functionalities that cover more application cases. We test our approach on more varied and larger data sets by applying it to UAV and aerial imagery of 0.02−0.25 m resolution from Kenya, Rwanda and Ethiopia. We show that it is more effective in terms of clicks and time compared to manual delineation for parcels surrounded by visible boundaries. Strongest advantages are obtained for rural scenes delineated from aerial imagery, where the delineation effort per parcel requires 38% less time and 80% fewer clicks compared to manual delineation.

3D Cadastre models capture both the complex interrelations between physical objects and their corresponding legal rights, restrictions, and responsibilities. Most of the ongoing research on 3D Cadastre worldwide is focused on interrelations at the level of buildings and infrastructures. So far, the analysis of such interrelations in terms of indoor spaces, considering the time aspect, has not been explored yet. In The Netherlands, there are many examples of changes in the functionality of buildings over time. Tracking these changes is challenging, especially when the geometry of the spaces changes as well; for example, a change in functionality, from administrative to residential use of the space or a change in the geometry when merging two spaces in a building without modifying the functionality. To record the changes, a common practice is to use 2D plans for subdivisions and assign new rights, restrictions, and responsibilities to the changed spaces in a building. In the meantime, with the advances of 3D data collection techniques, the benefits of 3D models in various forms are increasingly being researched. This work explores the opportunities for using 3D point clouds to establish a platform for 3D Cadastre studies in indoor environments. We investigate the changes in time of the geometry of the building that can be automatically detected from point clouds, and how they can be linked with a Land Administration Model (LADM) and included in a 3D spatial database, to update the 3D indoor Cadastre. The results we have obtained are promising. The permanent changes (e.g., walls, rooms) are automatically distinguished from dynamic changes (e.g., human, furniture) and are linked to the space subdivisions.

There is a growing demand for cheap and fast cadastral mapping methods to face the challenge of 70% global unregistered land rights. As traditional on-site field surveying is time-consuming and labor intensive, imagery-based cadastral mapping has in recent years been advocated by fit-for-purpose (FFP) land administration. However, owing to the semantic gap between the high-level cadastral boundary concept and low-level visual cues in the imagery, improving the accuracy of automatic boundary delineation remains a major challenge. In this research, we use imageries acquired by Unmanned Aerial Vehicles (UAV) to explore the potential of deep Fully Convolutional Networks (FCNs) for cadastral boundary detection in urban and semi-urban areas. We test the performance of FCNs against other state-of-the-art techniques, including Multi-Resolution Segmentation (MRS) and Globalized Probability of Boundary (gPb) in two case study sites in Rwanda. Experimental results show that FCNs outperformed MRS and gPb in both study areas and achieved an average accuracy of 0.79 in precision, 0.37 in recall and 0.50 in F-score. In conclusion, FCNs are able to effectively extract cadastral boundaries, especially when a large proportion of cadastral boundaries are visible. This automated method could minimize manual digitization and reduce field work, thus facilitating the current cadastral mapping and updating practices.

The objective to fast-track the mapping and registration of large numbers of unrecorded land rights globally has led to the experimental application of Artificial Intelligence in the domain of land administration, and specifically the application of automated visual cognition techniques for cadastral mapping tasks. In this research, we applied and compared the ability of rule-based systems within Object-Based Image Analysis (OBIA), as opposed to human analysis, to extract visible cadastral boundaries from very high-resolution World View-2 images, in both rural and urban settings. From our experiments, machine-based techniques were able to automatically delineate a good proportion of rural parcels with explicit polygons where the correctness of the automatically extracted boundaries was 47.4% against 74.24% for humans and the completeness of 45% for the machine compared to 70.4% for humans. On the contrary, in the urban area, automatic results were counterintuitive: even though urban plots and buildings are clearly marked with visible features such as fences, roads and tacitly perceptible to eyes, automation resulted in geometrically and topologically poorly structured data. Thus, these could neither be geometrically compared with human digitisation, nor actual cadastral data from the field. The results of this study provide an updated snapshot with regards to the performance of contemporary machine-driven feature extraction techniques compared to conventional manual digitising. In our methodology, using an iterative approach of segmentation and classification, we demonstrated how to overcome the weaknesses of having undesirable segments due to intra-parcel and inter-parcel variability, when using segmentation approaches for cadastral feature delineation. We also demonstrated how we can easily implement a geometric comparison framework within the Esri’s ArcGIS software environment and firmly believe the developed methodology can be reproduced.

Unmanned Aerial Systems (UAS) are emerging as a tool for alternative land tenure data acquisition. Even though UAS appear to represent a promising technology, it remains unclear to what extent they match the needs of communities and governments in the land sector. This paper responds to this question by undertaking a socio-technical study in Rwanda, aiming to determine the match between stakeholders’ needs and the characteristics of the UAS data acquisition workflow and its final products as valuable spatial data for land administration and spatial planning. A needs assessment enabled the expression of a range of land information needs across multiple levels and stakeholder sectors. Next to the social study, three different UAS were flown to test not only the quality of data but the possibilities of the use of this technology within the current institutional environment. A priority list of needs for cadastral and non-cadastral information as well as insights into operational challenges and data quality measures of UAS-based data products are presented. It can be concluded that UAS can have a significant contribution to match most of the prioritized needs in Rwanda. However, the results also reveal that structural and capacity conditions currently undermine this potential.

Public participation is significant for the success of any urban planning project. However, most members of the general public are not planning professionals and may not understand the technical details of a 2D paper-based plan, which might hamper their participation. One way to expand the participation of citizens is to present plans in well-designed, user-friendly and interactive platforms that allow participation regardless of the technical skills of the participants. This paper investigates the impacts of the combined use of 3D visualization and e-participation on public participation in Kisumu, Kenya. A 3D city model, created with CityEngine2016, was exported into a web-based geoportal and used as a Planning Support System in two stakeholder workshops in order to evaluate its usability. In order to assess the workshops 300 questionnaires were given out to planning practitioners and interview were done with key informants. Five indicators were developed for evaluating the usability of the 3D model while the usability of e-participation was evaluated using communication, collaboration and learning as indicators. Results showed that effectiveness and efficiency varied within different professional groups while the questionnaires showed strong preference for e-participation methods, especially Short Message Servicess/Unstructured Supplementary Service Data and emails. The study concludes that the use of 3D visualization and e-participation has the potential to improve the quality and quantity of public participation and recommends further research on the subject.

Cadastres are argued as an essential tool to support land tenure security. Low cadastral coverage in developing countries creates a driver for innovative methods to expedite the mapping processes. As a human construct, the morphology of parcel boundaries is a diverse and complex topic: there are limited generalized rules for identifying, describing, and classifying them. This paper studies both the institutional and spatial aspects of cadastral boundaries, in order to provide more contemporary knowledge about the morphology of cadastral boundaries. This study inspects the relationship between topographic objects and general boundaries in the case context of Port Vila, Vanuatu. Statistical analysis reveals that under a dialectical error tolerance, large percentages of cadastral boundaries coincide with topographic objects. Specifically, in dense urban regions, road edges and building walls coincide with the majority of cadastral boundaries, with proportions of 49% and 35%, respectively. In suburban regions, the fence (25%), instead of buildings, plays an important role in marking a parcel border. The landscape is observed to have significant impact on parcel morphology. Therefore, constructing a map based on automatic or semi-automatic identification and classification of these features could significantly contribute to cadastral mapping in developing countries.

Many developing countries have witnessed the urgent need of accelerating cadastral surveying processes. Previous studies found that large portions of cadastral boundaries coincide with visible physical objects, namely roads, fences, and building walls. This research explores the application of airborne laser scanning (ALS) techniques on cadastral surveys. A semi-automated workflow is developed to extract cadastral boundaries from an ALS point clouds. Firstly, a two-phased workflow was developed that focused on extracting digital representations of physical objects. In the automated extraction phase, after classifying points into semantic components, the outline of planar objects such as building roofs and road surfaces were generated by an α-shape algorithm, whilst the centerlines delineatiation approach was fitted into the lineate object—a fence. Afterwards, the extracted vector lines were edited and refined during the post-refinement phase. Secondly, we quantitatively evaluated the workflow performance by comparing results against an exiting cadastral map as reference. It was found that the workflow achieved promising results: around 80% completeness and 60% correctness on average, although the spatial accuracy is still modest. It is argued that the semi-automated extraction workflow could effectively speed up cadastral surveying, with both human resources and equipment costs being reduced