Search Results (201)

Research on the semantic approach to different land use classes is considered an important aspect of overcoming challenges related to proper land management. This research has direct implications for sustainable land management. The aim of this study is to introduce a new land use class in the Polish cadastre based on land use registration systems that function in other European countries. To achieve this, the existing land use registration systems in selected European countries were analyzed. The criterion for including land in the new class will be its actual use. The proposed new land use class may be a highly promising solution for the clear identification of areas with a special functional nature. By proposing the introduction of this new class, authors highlights the areas that, under the current land use registration system, are not clearly identified within the broadly understood categories of built-up and urbanized land. The research findings may also serve as a practical guideline for local authorities responsible for land administration and property taxation. Moreover, accurate land use classification is essential for sustainable land management, as it enables better planning and resource allocation. Improved clarity in land categorization supports environmental protection and balanced development, contributing to long-term sustainability goals. Full article

This study presents an integrated surveying methodology for efficient and accurate cadastral documentation, combining UAV photogrammetry, SLAM-based terrestrial and aerial scanning, and conventional geodetic measurements. Designed to be scalable across various cadastral and planning contexts, the workflow was tested in Charlottenburg, Romania’s only circular heritage village. The approach addresses challenges in built environments where traditional total station or GNSS techniques face limitations due to obstructed visibility and complex architectural geometries. The SLAM system was initially deployed in mobile scanning mode using a backpack configuration for ground-level data acquisition, and was later mounted on a UAV to capture building sides and areas inaccessible from the main road. The results demonstrate that the integration of aerial and terrestrial data acquisition enables precise building footprint extraction, with a reported RMSE of 0.109 m between the extracted contours and ground-truth total station measurements. The final cadastral outputs are fully compatible with GIS and CAD systems, supporting efficient land registration, urban planning, and historical site documentation. The findings highlight the method’s applicability for modernizing cadastral workflows, particularly in dense or irregularly structured areas, offering a practical, accurate, and time-saving solution adaptable to both national and international land administration needs. Beyond the combination of known technologies, the innovation lies in the practical integration of terrestrial and aerial SLAM (dual SLAM) with RTK UAV workflows under real-world constraints, offering a field-validated solution for complex cadastral scenarios where traditional methods are limited. Full article

After the fall of communism, Romania embarked on a comprehensive land restitution process through Law No. 18/1991, aiming to re-establish private ownership rights, particularly for agricultural and forestry lands. Divergent historical legacies across regions have resulted in heterogeneous land administration systems, contributing to inconsistencies, overlapping claims, and prolonged legal disputes. To address these challenges, the Romanian government introduced the National Cadastre and Land Registration Program, which promotes systematic property registration across the country. Keeping in mind the fact that there is no integrated study that analyses national challenges from multiple dimensions such as history, law, institutions, technology, and socioeconomics and proposes systematic optimization strategies, this article provides a critical analysis of the legal and institutional framework governing land restitution and cadastral reform, highlighting the influence of historical administrative structures and the adoption of modern geospatial technologies such as Geographic Information Systems (GISs) and Unmanned Aerial Vehicles (UAVs). By adopting a qualitative and document-based research approach, focusing on the analysis of legislative frameworks, institutional procedures, and technical instruments used in systematic land registration in Romania, this study emphasizes the benefits of systematic registration, including increased legal certainty, investment stimulation, improved access to credit, and better planning and taxation. Despite progress, implementation remains uneven, hindered by documentation gaps, institutional capacity limitations, and administrative obstacles. Recent legislative adjustments and the integration of advanced geospatial tools aim to improve data quality and accelerate the registration process. Ultimately, the integration of legal, institutional, and geospatial components is essential for achieving transparent and accountable land governance, efficient resource management, and sustainable rural development in Romania. Full article

Coordinate registration (CR) is the key technology for improving the target positioning accuracy of sky-wave over-the-horizon radar (OTHR). The CR parameters are derived by matching the sea–land clutter classification (SLCC) results with prior geographic information. However, the SLCC results often contain mixed clutter, leading to discrepancies between land and island contours and prior geographic information, which makes it challenging to calculate accurate CR parameters for OTHR. To address these challenges, we transform the sea–land clutter data from Euclidean space into graph data in non-Euclidean space, and the CR parameters are obtained by calculating the similarity between graph pairs. And then, we propose a similarity calculation via a graph neural network (SC-GNN) method for calculating the similarity between graph pairs, which involves subgraph-level interactions and node-level comparisons. By partitioning the graph into subgraphs, SC-GNN effectively captures the local features within the SLCC results, enhancing the model’s flexibility and improving its performance. For validation, we construct three datasets: an original sea–land clutter dataset, a sea–land clutter cluster dataset, and a sea–land clutter registration dataset, with the samples drawn from various seasons, times, and detection areas. Compared with the existing graph matching methods, the proposed SC-GNN achieves a Spearman’s rank correlation coefficient of at least 0.800, a Kendall’s rank correlation coefficient of at least 0.639, a p@10 of at least 0.706, and a p@20 of at least 0.845. Full article

Accurate mapping of rubber plantation stand age is essential for forest inventory, land use monitoring, and carbon stock estimation. This study proposes a pixel-based approach that integrates the Bare Soil Index (BSI) with Normalized Difference Vegetation Index (NDVI) time series to detect land clearance events and predict stand age. The methodology involves feature engineering, selection, and evaluation of three tree-based and one non-parametric supervised machine learning models. Predictive features were extracted from interannual spectral index profiles, with an optimal subset selected using Recursive Feature Elimination (RFE). The best-performing model, optimized using a grid search matrix, was trained and applied to stacked images for pixel-level land clearance prediction over 37 years of NDVI and BSI time series. By aggregating predictions and performing post-classification analysis, a spatially explicit stand-age map was generated. The result was validated using secondary rubber farmer registration data, achieving an overall prediction accuracy of 84.5% and a root mean squared error (RMSE) of 1.86 years. The findings highlight the effectiveness of machine learning with NDVI and BSI time series for stand-age estimation, contributing to advancing remote sensing methodologies for forest inventory and support furfure high-precision carbon stock assessments. Full article

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. Full article

This article is based on a review of the governance of land tenure in 18 countries—16 in Africa and 2 in Asia—carried out from 2021 to 2023. It uses international guidelines on land policy and tenure governance as benchmarks to assess progress in each country through reviewing policy documents and literature, and obtaining inputs from key informants. This paper shows that, during the last decade, there has been significant progress in land tenure policies that have improved the recognition of customary and other communal land rights and improved women’s land rights. The formal registration of individual rights to customary and community land has now been achieved cost-effectively, with more rights going to women in a number of settings, and without leading to widespread commoditisation or land dispossession. There is a mixed picture, with countries trying different ways to grapple with common challenges such as securing customary tenure rights, unlocking development potential, improving women’s land rights, and managing the contesting interests in land. There are important examples of best practices in some countries that can be learnt from, such as the legislation of Free Prior and Informed Consent (FPIC) requirements and processes of large-scale land rights registration. Despite progress achieved in several countries, too many people are not enjoying the benefits of improved land tenure security; some countries still need to adopt new legislation, while others need to improve their implementation of existing legislation. More needs to be learnt from the range of different approaches to dealing with land tenure as national governments attempt to find solutions that accommodate contesting interests. The lessons and trends identified will be of value to country-level and international work on improving land tenure governance. Full article

Accurate and robust information on land cover changes in urban and coastal areas is essential for effective urban land management, ecosystem monitoring, and urban planning. This paper details the methodology and results of a pixel-level classification and change detection analysis, leveraging 1945 Royal Air Force (RAF) aerial imagery and 2011 Very High-Resolution (VHR) multispectral WorldView-2 satellite imagery from the broader area of Patras, Greece. Our attention is mainly focused on the changes in the coastline from the city of Patras to the northeast direction and the two major rivers, Charadros and Selemnos. The methodology involves preprocessing steps such as registration, denoising, and resolution adjustments to ensure computational feasibility for both coastal and riverbed change detection procedures while maintaining critical spatial features. For change detection at coastal areas over time, the Normalized Difference Water Index (NDWI) was applied to the new imagery to mask out the sea from the coastline and manually archive imagery from 1945. To determine the differences in the coastline between 1945 and 2011, we perform image differencing by subtracting the 1945 image from the 2011 image. This highlights the areas where changes have occurred over time. To conduct riverbed change detection, feature extraction using the Gray-Level Co-occurrence Matrix (GLCM) was applied to capture spatial characteristics. A Support Vector Machine (SVM) classification model was trained to distinguish river pixels from non-river pixels, enabling the identification of changes in riverbeds and achieving 92.6% and 92.5% accuracy for new and old imagery, respectively. Post-classification processing included classification maps to enhance the visualization of the detected changes. This approach highlights the potential of combining historical and modern imagery with supervised machine learning methods to effectively assess coastal erosion and riverbed alterations. Full article

Flood hazard studies for dam break cases are of utmost importance for understanding potential risks and minimizing the impact of such accidents. Siriu Dam, which has a clay core, is ranked as the third highest embankment dam in Romania. A fully dynamic 2D hydraulic numerical model was developed using HEC-RAS software to simulate the routing of the flood waves formed by breaching this dam. Four different failure scenarios were considered: two for overtopping and two for piping. The breach parameters were chosen based on the dam characteristics in accordance with appropriate empirical relationships. The flood hazard was quantified and analyzed in terms of depths, velocities, depth x velocity values, and flooded areas. The results provide useful information concerning flood risk mitigation, such as the dam break wave routing, peak discharges, arrival time, travel velocity, and inundation boundary. The influence of the scenario and site characteristics (topography, river morphology, and constructions) on the results was analyzed. Depths and velocities over 10 m and 15 m/s, respectively, were obtained close to the dam, while those in Buzău City (90 km away) were under 1 m and 2 m/s, respectively. The city was flooded 7–8.5 h after the breach (depending on the scenario), and over 15 to 50% of its total area was affected. Moreover, the flood hazard parameters were compared for the different scenarios, providing the practical details necessary to develop flood risk management plans and the associated response measures for the inhabited areas. This is the first numerical study to simulate the impact of a potential break accident that can occur for this dam. Full article

Accurate image registration is essential for various remote sensing applications, particularly in multi-temporal image analysis. This paper introduces DVF-NET, a novel deep learning-based framework for dual-temporal remote sensing image registration. DVF-NET integrates two displacement vector fields to address nonlinear distortions caused by significant variations between images, enabling more precise image alignment. A key innovation of this method is the incorporation of a Structural Attention Module (SAT), which enhances the model’s ability to focus on structural features, improving the feature extraction process. Additionally, we propose a novel loss function design that combines multiple similarity metrics, ensuring more comprehensive supervision during training. Experimental results on various remote sensing datasets indicate that the proposed DVF-NET outperforms the existing methods in both accuracy and robustness, particularly when handling images with substantial geometric distortions such as tilted buildings. The results validate the effectiveness of our approach and highlight its potential for various remote sensing tasks, including change detection, land cover classification, and environmental monitoring. DVF-NET provides a promising direction for the advancement of remote sensing image registration techniques, offering both high precision and robustness in complex real-world scenarios. Full article

Farmer’s incentive is a core issue in achieving sustainable agricultural development. In many developing countries, smallholder farming is predominant in agricultural production, potentially limiting improvements in agricultural sustainability. Promoting agricultural cooperatives is a widely adopted strategy to help resource-poor farmers obtain higher agricultural revenue. In China, these organizations have expanded rapidly since the early 21st century, reaching 2.22 million by September 2023 and providing services to nearly half of farming households. However, their effectiveness and impact on enhancing agricultural revenue remain subjects of ongoing debate. To provide more empirical evidence on this topic, this paper constructs an agricultural cooperatives database based on the national commercial registration enterprise dataset and matches it with the National Fixed Point Rural Survey (NFP). The findings reveal that the development of agricultural cooperatives in China significantly helps farmers enhance their production revenue, leading to an increase in household income. Furthermore, the paper identifies strong heterogeneity in the positive effects of cooperative development at both the village and household levels. In the mechanism analysis, it is shown that agricultural cooperatives in China facilitate increased investment in capital, intermediate inputs, and technology, optimizing the allocation of production factors in agricultural processes, thereby improving land productivity and ultimately increasing agricultural revenue. Full article

Facilitating land transfers and contract standardization is crucial for optimizing farmland use and achieving high-quality agricultural development following the round of confirmation, registration, and certification of contracted rural land management rights by the government. Social trust is crucial for cracking the growth dilemma of land transfer, and the impact of farmers’ trust on land transfer may also change after the round of land titling. This study aims to examine the impact of social trust on farm households’ land transfer decisions and the formalization of land transfer contracts within the context of the new round of land titling in China, using models such as IV-Probit, Heckprobit, and conditional mixed process (CMP), with survey data from 2600 Chinese farmers in 2020–2021. The results show that increased social trust significantly enhances both land transfer and contract formalization, and these results remain robust after addressing potential endogeneity. Further analysis reveals that while interpersonal trust promotes land transfer, institutional trust plays a more pivotal role in the formalization of contracts. The results underscore the importance of strengthening grassroots governance to foster institutional trust, enhance institutional reliability, and support the regulated growth of the land transfer market. Full article

When using point cloud technology to measure the dimension and geometric error of aircraft landing gear components, the point cloud data obtained after scanning may have certain differences because of the sophistication and diversity of the components that make up the landing gear. However, when using traditional point cloud registration algorithms, if the initial pose between point clouds is poor, it can lead to significant errors in the final registration results or even registration failure. Furthermore, the significant difference in registration results between point clouds can affect the final measurement results. Adopting Teaching-Learning-Based Optimization (TLBO) to solve some optimization problems has unique advantages such as high accuracy and good stability. This study integrates TLBO with point cloud registration. To increase the probability of using TLBO for point cloud registration to search for the global optimal solution, adaptive learning weights are first introduced during the learner phase of the basic TLBO. Secondly, an additional tutoring phase has been designed based on the symmetry and unimodality of the normal distribution to improve the accuracy of the solution results. In order to evaluate the performance of the proposed algorithm, it was first used to solve the CEC2017 test function. The comparison results with other metaheuristics showed that the improved TLBO has excellent comprehensive performance. Then, registration experiments were conducted using the open point cloud dataset and the landing gear point cloud dataset, respectively. The registration results showed that the point cloud registration method proposed in this paper has strong competitiveness. Full article

Brazil’s Forest Code (FC) is a landmark law that, despite dating back to the 1930s, has low compliance. Illegal deforestation continues, and millions of hectares that were set to be reforested remain degraded. Although sector agreements such as the Amazon Soy Moratorium (ASM) have been important in the fight against deforestation, the implementation of the FC represents the key long-term strategy to halt deforestation in the soy supply chain. Here, we used datasets of the boundaries of rural properties, deforestation permits, environmental licensing, and land cover in Mato Grosso to quantify illegal deforestation and analyzed compliance with the Forest Code (FC) on soy farms to explore how loopholes in the implementation of the FC allow deforestation to continue unabated. Our analyses show that between August 2009 and July 2019, soy farms in Mato Grosso State, the largest Brazilian soy producer, were responsible for 15% (or 468.1 thousand hectares) of all land cleared in registered properties. Half of this deforestation was illegal. The FC implementation within these properties has been slow: only 11% of registered soy farms have made it to the final stage of the registration process, thus being considered fully compliant. This novel analysis reinforces that accelerating the implementation of the FC could significantly reduce deforestation and advance the restoration of illegally cleared land particularly in the Cerrado, where 50% of the original cover has already been lost, as well as in the Amazon. By achieving full compliance in the soy sector, Brazil’s position in the international market would be strengthened as a supplier of sustainably produced, deforestation-free commodities. Full article

Land registration is an important program in asset legalization with the vast land resources owned by Indonesia. The reality is that there are 48 million certificated lands out of an estimated 126 million certificates throughout Indonesia, so the Ministry of Agrarian Affairs and Spatial Planning/National Land Agency (ATR/BPN) made a breakthrough through the Complete Systematic Land Registration (PTSL) program. The grouping mechanism of the PTSL program is divided into four clusters, namely K-1, K-2, K-3, and K-4. Land parcels included in K-4 have problems, namely that they have been registered but not mapped/mapped incorrectly, so the idea of modeling the K-4 typology is needed for accelerating the improvement in land data quality (KW). The research location is in Bandung Regency, which is included in the top five land offices with the highest number of K-4 in West Java Province. This research method uses a mixed method, namely quantitative with a Slovin approach for the identification and clustering of K-4 typology and descriptive qualitative for justification of typology modeling in multiple aspects. The results of K-4 typology modeling were 128 clusters based on seven types of data criteria and obtained 4 clusters that matched the data sample, namely T-1, T-43, T-63, and T-128. The four clusters were justified against the K-4 concept in the Ministry of ATR/BPN, the FFP-LA concept, and the PESTLE framework. Dissemination of K-4 typology modeling is a breakthrough that can be implemented by the Ministry of ATR/BPN and its staff in various regions and the role of multi-concepts in this research can be an input for improving the K-4 concept that has been in effect so far. Full article