Hasil untuk "Cadastral mapping"

Carles Ferrer-Ortiz, O. Marquet, L. Mojica et al.

Many academics, urban planners and policymakers subscribe to the benefits of implementing the concept of the 15-Minute City (FMC) in metropolises across the globe. Despite the interest raised by the concept, and other variants of chrono-urbanism, to date, only a few studies have evaluated cities from the FMC perspective. Most studies on the subject also lack a proper well-defined methodology that can properly assess FMC conditions. In this context, this study contributes to the development of an appropriate FMC-measuring method by using network analysis for services and activities in the City of Barcelona (Catalonia, northeastern Spain). By using network analyst and basing our analysis on cadastral parcels, this study is able to detail the overall accessibility conditions of the city and its urban social functions based on the FMC perspective. The resulting spatial synthetic index is enhanced with the creation of partial indexes measuring the impact of education, provisioning, entertainment, public and non-motorized transport, and care facilities. The results show that most residents of this dense and compact city live in areas with proximity to services, that can clearly be labeled as FMC, although there are some shortfalls in peripheral areas. Results validate the FMC methodology as a viable method to highlight spatial inequalities at the microscale level, a valuable tool for the development of effective planning policies.

Yang Ni, Fanli Jia

Artificial intelligence (AI)-enabled digital interventions, including Generative AI (GenAI) and Human-Centered AI (HCAI), are increasingly used to expand access to digital psychiatry and mental health care. This PRISMA-ScR scoping review maps the landscape of AI-driven mental health (mHealth) technologies across five critical phases: pre-treatment (screening/triage), treatment (therapeutic support), post-treatment (remote patient monitoring), clinical education, and population-level prevention. We synthesized 36 empirical studies implemented through early 2024, focusing on Large Language Models (LLMs), machine learning (ML) models, and autonomous conversational agents. Key use cases involve referral triage, empathic communication enhancement, and AI-assisted psychotherapy delivered via chatbots and voice agents. While benefits include reduced wait times and increased patient engagement, we address recurring challenges like algorithmic bias, data privacy, and human-AI collaboration barriers. By introducing a novel four-pillar framework, this review provides a comprehensive roadmap for AI-augmented mental health care, offering actionable insights for researchers, clinicians, and policymakers to develop safe, effective, and equitable digital health interventions.

Fan Yang

Conventional 3D cadastral models are susceptible to the influence of automatic texture mapping, resulting in a low degree of true matching in model construction. Therefore, this study proposes a method for constructing a 3D cadastral model based on drone oblique photography and GIS positioning. After using drone oblique photography and GIS positioning for 3D cadastral surveying, describe the topological relationship of 3D cadastral spatial entities, and construct an optimization model for 3D cadastral spatial data based on basic structural elements. The experimental results show that the model constructed in this paper has high attribute correlation and true matching, and is reliable.

Zoubere Dialla

This study examines the digitalisation of cadastral procedures in Burkina Faso, focusing on the municipalities of Ouagadougou and Bobo-Dioulasso where these initiatives are currently implemented. Using the concept of rationalisation tension within a qualitative methodological framework, we analyse the concrete actions undertaken and the tensions generated by this digitalisation process. Our findings reveal that cadastral digitalisation encompasses surveying documentation, parcel mapping, building records, archival materials, and the deployment of socio-technical management systems. The analysis identifies key rationalisation tensions, particularly the disconnect between digitalisation objectives and available resources, as well as coordination challenges amongst state technical services. Whilst digitalisation holds transformative potential for cadastral management, our research demonstrates that technological solutions alone cannot address underlying structural issues or resolve conflicts arising from stakeholder strategies.

Krausková Dominika, Mikita Tomáš, Hrůza Petr et al.

Accurate mapping of small water structures and streambeds is essential for hydrological modeling, erosion control, and landscape management. While traditional geodetic methods such as GNSS and total stations provide high precision, they are time-consuming and require specialized equipment. Recent advances in mobile technology, particularly smartphones equipped with LiDAR sensors, offer a potential alternative for rapid and cost-effective field data collection. This study assesses the accuracy of the iPhone 14 Pro’s built-in LiDAR sensor for mapping streambeds and retention structures in challenging terrain. The test site was the Dílský stream in the Oslavany cadastral area, characterized by steep slopes, rocky surfaces, and dense vegetation. The stream channel and water structures were first surveyed using GNSS and a total station and subsequently re-measured with the iPhone. Several scanning workflows were tested to evaluate field applicability. Results show that the iPhone LiDAR sensor can capture landscape features with useful accuracy when supported by reference points spaced every 20 m, achieving a vertical RMSE of 0.16 m. Retention structures were mapped with an average positional error of 7%, with deviations of up to 0.20 m in complex or vegetated areas. The findings highlight the potential of smartphone LiDAR for rapid, small-scale mapping, while acknowledging its limitations in rugged environments.

Fayez Tarsha Kurdi, Elżbieta Lewandowicz, Zahra Gharineiat et al.

Urban indicators established in spatial development plans should ensure the preservation of spatial order when introducing new construction investments. They should also harmonize with the existing urban structure and even modernize it toward sustainable development. When determining these indicators, the surrounding space is analyzed. Conventionally, building indicators in the existing space are determined based on available documents, which usually comprise 2D spatial data such as large-scale maps or cadastral maps. This study aims to investigate the method of calculating building indicators using 3D urban building models that will be created from airborne Light Detection and Ranging (LiDAR) measurements. In the discussion of the results, indicators calculated based on LiDAR data are compared with the ones calculated from 2D cadastral data. The calculated 3D indicators correlate with the classically calculated indicators. The accuracy of the computed building area, volume, and other indicators depends on the LiDAR point cloud density and accuracy. The indicators calculated from the 3D data align with the new trends in defining Building Morphology Indicators (BMIs).

B. Prodanov, Chavdar Gussev, Desislava Sopotlieva et al.

Coastal dunes (CD) are dynamic environments shaped by sediment accumulation, sea breeze and vegetation cover, which are also highly sensitive to human intervention. The urbanization of coastal areas, global warming and rising sea levels pose significant threats to CD systems, leading to erosion and habitat loss. In the past decade, the total CD area in Bulgaria has decreased by over 12 hectares, resulting in the complete loss of five dune systems. Over 5% of the CDs along the Bulgarian Black Sea Coast (BBSC) face similar challenges, primarily due to human-induced pressure and alterations in dune landforms. This study introduces a Standard Dune Mapping Procedure (SDMP) for the BBSC based on geospatial data of dune habitats, facilitating the cadastral maps and registers and effective conservation and management activities. The proposed methodology, developed through the collaboration of the Ministry of Environment and Water of the Republic of Bulgaria, the Institute of Oceanology and the Institute of Biodiversity and Ecosystem Research at the Bulgarian Academy of Sciences, is a multidisciplinary approach that integrates remote sensing, geomorphological, geological and phytocoenological surveys and habitat analysis in GIS environment. The procedure involves seven stages: initial inventory and data collection to processing, classification, and high-resolution mapping of coastal dune habitats. The SDMP aims to support sustainable management and conservation of CD ecosystems by emphasizing low-cost, non-intrusive remote sensing techniques. A pilot application on the “Kavatsi” CD system (Sozopol Municipality, Burgas District) validates the SDMP’s effectiveness in addressing the challenges of dune maintenance and anthropogenic impact. This comprehensive approach ensures accurate data collection and supports the development of sustainable management practices for CD ecosystems. The proposed procedure offers a significant step forward to the systematic mapping of CD habitats, responding to the urgent need for effective conservation strategies in the face of significant degradation due to human activities. The SDMP thus plays a crucial role in the sustainable management and conservation of the Bulgarian Black Sea CDs.

A. Zamzuri, A. A. Rahman, Muhammad Imzan Hassan et al.

Wenyu Zhang, Yao Tong, Yiqiu Liu et al.

Urban villages (UVs), informal settlements embedded within China's urban fabric, have undergone widespread demolition and redevelopment in recent decades. However, there remains a lack of systematic evaluation of whether the demolished land has been effectively reused, raising concerns about the efficacy and sustainability of current redevelopment practices. To address the gap, this study proposes a deep learning-based framework to monitor the spatiotemporal changes of UVs in China. Specifically, semantic segmentation of multi-temporal remote sensing imagery is first used to map evolving UV boundaries, and then post-demolition land use is classified into six categories based on the "remained-demolished-redeveloped" phase: incomplete demolition, vacant land, construction sites, buildings, green spaces, and others. Four representative cities from China's four economic regions were selected as the study areas, i.e., Guangzhou (East), Zhengzhou (Central), Xi'an (West), and Harbin (Northeast). The results indicate: 1) UV redevelopment processes were frequently prolonged; 2) redevelopment transitions primarily occurred in peripheral areas, whereas urban cores remained relatively stable; and 3) three spatiotemporal transformation pathways, i.e., synchronized redevelopment, delayed redevelopment, and gradual optimization, were revealed. This study highlights the fragmented, complex and nonlinear nature of UV redevelopment, underscoring the need for tiered and context-sensitive planning strategies. By linking spatial dynamics with the context of redevelopment policies, the findings offer valuable empirical insights that support more inclusive, efficient, and sustainable urban renewal, while also contributing to a broader global understanding of informal settlement transformations.

Seto Apriyadi, Irwan Meilano, Andri Hernandi et al.

Responding to cost inefficiency in the Indonesian cadastral mapping system, this study aimed to analyze the implementation of integrated mapping activities, namely complete systematic land registration, assessing land value zones, and regional land stewardship balance. This study employed an operational management system, particularly focusing on financial aspects, using data envelopment analysis (DEA), a non-parametric technique for evaluating the relative efficiency of decision-making units. These approaches are rarely explored in cadastral mapping. DEA was used to analyze the efficiency of seven aspects: aerial mapping, office supplies, meetings, consumption, transportation, capital expenses, and socialization. Content analysis was used to identify integration parameters derived from operational management-based integration. Cronbach’s alpha was used for the reliability test. The Way Sulan sub-district of South Lampung Regency in Lampung Province, Indonesia, was selected as the study area due to its complete mapping activities. The findings suggested that applying operational management for integrated cadastral mapping is effective. However, contrary to expectations, efficiency was lower in dense urban areas, where costs tend to be cheaper, while efficiency was higher in agricultural areas, where expenses were much greater. Based on this study, an operational management approach to integrated cadastral mapping is recommended to improve budget efficiency and general standards of land management, especially in areas with complex land use.

V. Targonska, O. Musienko, O. Afanasiev

The article explores the current trends, challenges, and prospects of automated mapping in land management, focusing on the increasing demand for high-precision, up-to-date cadastral information. Traditional cartographic methods, based on manual data processing and analog technologies, are labor-intensive and time-consuming, often leading to inconsistencies and inaccuracies. Automated mapping is an important direction in the development of modern geographic information systems and technologies in land management. The introduction of automated methods allows to significantly increase the productivity, accuracy and reliability of cartographic work, as well as to ensure the prompt updating of cartographic information. The study emphasizes the advantages of automated mapping through the integration of modern geoinformation technologies, including remote sensing (RS), global navigation satellite systems (GNSS), geographic information systems (GIS), and unmanned aerial vehicles (UAVs). These technologies significantly enhance the efficiency, accuracy, and reliability of spatial data collection, processing, and visualization. The article reviews recent scientific studies on the application of automated methods for processing remote sensing data, including aerial and satellite imagery, in the creation and updating of cartographic materials. Special attention is given to the use of UAVs for high-precision orthophoto mapping and 3D terrain modeling. The research also highlights the importance of ensuring data compatibility and consistency between various sources, such as cadastral registers, GPS measurements, and geoinformation resources. Furthermore, the study addresses the regulatory and legal aspects of automated mapping in land management, analyzing key legislative acts and government regulations in Ukraine that define the standards for cadastral mapping and land use planning. It discusses the necessity of implementing automated data validation and quality control procedures to improve the credibility of cartographic information. The article concludes by identifying key benefits of automated mapping, including increased accuracy, reduced costs, faster data processing, and improved decision-making in land management. It provides recommendations for the integration of advanced geoinformation technologies in land administration, ensuring the timely and reliable update of cadastral data, optimizing land resource management, and supporting sustainable land use planning.

Ilham Adi Panuntun, Ying-Nong Chen, Ilham Jamaluddin et al.

In the rise of climate change, land cover mapping has become such an urgent need in environmental monitoring. The accuracy of land cover classification has gotten increasingly based on the improvement of remote sensing data. Land cover classification using satellite imageries has been explored and become more prevalent in recent years, but the methodologies remain some drawbacks of subjective and time-consuming. Some deep learning techniques have been utilized to overcome these limitations. However, most studies implemented just one image type to evaluate algorithms for land cover mapping. Therefore, our study conducted deep learning semantic segmentation in multispectral, hyperspectral, and high spatial aerial image datasets for landcover mapping. This research implemented a semantic segmentation method such as Unet, Linknet, FPN, and PSPnet for categorizing vegetation, water, and others (i.e., soil and impervious surface). The LinkNet model obtained high accuracy in IoU (Intersection Over Union) at 0.92 in all datasets, which is comparable with other mentioned techniques. In evaluation with different image types, the multispectral images showed higher performance with the IoU, and F1-score are 0.993 and 0.997, respectively. Our outcome highlighted the efficiency and broad applicability of LinkNet and multispectral image on land cover classification. This research contributes to establishing an approach on landcover segmentation via open source for long-term future application.

Mahir Shahriar Dhrubo, Samira Akter, Anwarul Bashir Shuaib et al.

Efficient vectorization of hand-drawn cadastral maps, such as Mouza maps in Bangladesh, poses a significant challenge due to their complex structures. Current manual digitization methods are time-consuming and labor-intensive. Our study proposes a semi-automated approach to streamline the digitization process, saving both time and human resources. Our methodology focuses on separating the plot boundaries and plot identifiers and applying our digitization methodology to convert both of them into vectorized format. To accomplish full vectorization, Convolutional Neural Network (CNN) models are utilized for pre-processing and plot number detection along with our smoothing algorithms based on the diversity of vector maps. The CNN models are trained with our own labeled dataset, generated from the maps, and smoothing algorithms are introduced from the various observations of the map's vector formats. Further human intervention remains essential for precision. We have evaluated our methods on several maps and provided both quantitative and qualitative results with user study. The result demonstrates that our methodology outperforms the existing map digitization processes significantly.

M. Metaferia, R. Bennett, B. Alemie et al.

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.

Bedru Tareke, M. Koeva, C. Persello

Formal land registration systems are out of reach for most of the world’s population. Conventional mapping methods, such as high-precision ground surveys, are costly, making them inaccessible, especially in low- and middle-income countries. With the introduction of fit-for-purpose land administration, automatic feature extraction techniques have been actively investigated to accelerate the land rights mapping process. Therefore, in our research, we assessed the potential of deep learning to extract cadastral boundaries from very high-resolution images. Our study adopts a multitask learning strategy, which utilizes state of the art U-Net model for the segmentation task, whereas the frame field learning method provides structural information for the subsequent active contour model to produce regularized vector polygons. The experimental results show that the combined U-Net model and frame field information produced polygons with higher accuracy compared to a segmentation method on its own.

Mikko Rönneberg, P. Kettunen

ABSTRACT Due to urban expansion, agriculture, and the long history of the cadastre in Finland, the cadastral index map has millions of border markers that have low spatial accuracy, incomplete feature properties or both. The low quality of the border markers creates issues, such as forest cutting machines cutting from the wrong side of the border. As it is unfeasible for the national mapping agency to remeasure all these border markers, crowdsourcing is seen as a solution. However, the task of locating and measuring border markers requires motivated citizens. Therefore, in this study, a gamified map-based artefact enabling citizens to refine the quality of border markers in the cadastral index map was created. The artefact was designed, developed, demonstrated, and evaluated following the design science research approach. This study demonstrated with high sample size that gamified crowdsourcing is viable for motivating citizens to perform even challenging tasks. Of the applied gamification affordances, progression, points, and leaderboard were the most motivating. It was also found that involving stakeholders early in the creation process and focusing on usability of the artefact resulted in a pleasing user experience for the citizens. The artefact even spun a self-organised mapping party during its demonstration.

Neda Rahimpour Anaraki, Alireza Azadbakht, Maryam Tahmasbi et al.

Recently, there has been a high demand for accelerating and improving the detection of automatic cadastral mapping. As this problem is in its starting point, there are many methods of computer vision and deep learning that have not been considered yet. In this paper, we focus on deep learning and provide three geometric post-processing methods that improve the quality of the work. Our framework includes two parts, each of which consists of a few phases. Our solution to this problem uses instance segmentation. In the first part, we use Mask R-CNN with the backbone of pre-trained ResNet-50 on the ImageNet dataset. In the second phase, we apply three geometric post-processing methods to the output of the first part to get better overall output. Here, we also use computational geometry to introduce a new method for simplifying lines which we call it pocket-based simplification algorithm. For evaluating the quality of our solution, we use popular formulas in this field which are recall, precision and F-score. The highest recall we gain is 95 percent which also maintains high Precision of 72 percent. This resulted in an F-score of 82 percent. Implementing instance segmentation using Mask R-CNN with some geometric post-processes to its output gives us promising results for this field. Also, results show that pocket-based simplification algorithms work better for simplifying lines than Douglas-Puecker algorithm.

Yuping Yan, Xu Yao, Yu Qiu

In this paper, the three-dimensional real-life model and laser point cloud data are briefly introduced, and then the actual demand of cadastral survey under the background of big data is put forward. Finally, based on this, a new cadastral survey method is developed. Among them, it consists of three links, namely, data acquisition, data processing and the construction of cadastral model map. At the same time, the application effect of this method is verified from the aspects of result precision, work efficiency and comprehensiveness. Through verification, it can be found that, compared with the traditional surveying and mapping method, the surveying and mapping method based on 3D real-life model and laser point cloud data has higher accuracy, faster working efficiency, and can visually display the results, which is far superior to the traditional surveying method, and can be applied to practice.

I. Rozhi, B. Naradoviy

The article is devoted to the study of the implementation of geodetic innovations in the field of land management and cadastral activity. Modern technologies and their impact on the optimization and efficiency of work in the specified areas are considered. The main attention is paid to the methods of geographic information systems, their application for accurate mapping, data analysis and territorial development planning. The purpose of this article is to research and evaluate the use of the latest geodetic solutions in land and cadastral management, as well as to reveal the advantages and potential of their application to optimize the management of land resources. Used: analytical method, cartographic method, mathematical method, methods of digital automated processing of space images. Further research in the field of land management and cadastral activity can be directed to the development of new methods of processing and interpreting geodetic data using artificial intelligence and machine learning, adapting geoinformation systems to the needs of regional land management, in particular, to monitor climate changes, ensure food and water resources. The article can be useful for specialists in the field of land management, cadastre, as well as for everyone who is interested in innovative technologies in geodesy.

Rachid EL Mokadem, Yann Ben Maissa, Zineb El Akkaoui

Federated Machine Learning (Fed ML) is a new distributed machine learning technique applied to collaboratively train a global model using clients local data without transmitting it. Nodes only send parameter updates (e.g., weight updates in the case of neural networks), which are fused together by the server to build the global model. By not divulging node data, Fed ML guarantees its confidentiality, a crucial aspect of network security, which enables it to be used in the context of data-sensitive Internet of Things (IoT) and mobile applications, such as smart Geo-location and the smart grid. However, most IoT devices are particularly energy constrained, which raises the need to optimize the Fed ML process for efficient training tasks and optimized power consumption. In this paper, we conduct, to the best of our knowledge, the first Systematic Mapping Study (SMS) on Fed ML optimization techniques for energy-constrained IoT devices. From a total of more than 800 papers, we select 67 that satisfy our criteria and give a structured overview of the field using a set of carefully chosen research questions. Finally, we attempt to provide an analysis of the energy-constrained Fed ML state of the art and try to outline some potential recommendations for the research community.