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Mixed land use (MLU) plays a crucial role in fostering a sustainable urban development, vibrant communities, and efficient land utilization, providing a viable solution for smart growth, inclusive public transit, and urban sustainability. This study employs deep neural network (DNN) models: multilayer perceptron (MLP), and long short-term memory (LSTM), to analyze the effect of the transportation infrastructure-based accessibility on the prevalence of MLU patterns, based on the following data: infrastructure-based accessibility measures represented by the logsum (or transport supply), MLU patterns at the parcel level, and floor space prices by space type, for the years 2012 and 2015. Furthermore, the proposed methods are applied to the Jiang'an District of the city of Wuhan, China, at the parcel level as the case study. The study results reveal that MLU is predominantly accessible in areas close to the city center, characterized by a high density, and is relatively scarce on the city outskirts. Notably, parcels exhibiting mixed residential–commercial and residential land-use patterns underwent significant changes between 2012 and 2015, particularly in regions with robust accessibility via non-motorized modes and public transit, specifically in the central and southern parts of Jiang'an District. This transition is evident under scenario 3 (walk, bike, bus, subway) and scenario 6 (walk, bus, car) considered in this study. Furthermore, the study observed a substantial expansion in mixed commercial–residential and commercial districts, significantly near the high-transit accessibility area of subway line 1, as demonstrated in scenario 7 (bike, subway, taxi). The results from the MLP models show a mean relative error (MRE) of 4.7–14.08% for the MLU, and the LSTM models show an MRE of 3.74–10.38% for the MLU. More importantly, both the training and forecasting errors of the above models are lower, in most cases, than those reported in the literature. Moreover, these results indicate that the transportation supply or the infrastructure-based accessibility (represented by logsum) significantly influences MLU patterns. [ABSTRACT FROM AUTHOR]

Intensive land use assessment is a key research topic in urban land use, and most of the existing studies focus on macro-level assessment. There is a lack of research on the micro-level assessment of intensive urban land use, especially at the parcel level. The objective of this research is to propose a method for the parcel-based evaluation of urban commercial land intensification. The study uses a multidimensional evaluation framework and index system, comprehensive evaluation, and spatially exploratory analysis of urban commercial intensive land use based on "building intensity, use efficiency, compatibility, and diversity". The study finds that (1) the average value of intensive use of urban commercial land is 13.01, the standard deviation is 5.11, and the median value is 13, which generally indicate a medium level. (2) The degree of intensive use of commercial land has obvious characteristics of a high, medium, and low level. The study shows that when evaluating the degree of land use intensification at the parcel level, it is also necessary to consider the influence of the compatibility and diversity of external land use. The research results can provide a basis for spatial planning and the optimal design of urban land resources to improve urban vitality. [ABSTRACT FROM AUTHOR]

Purpose: the purpose of the article is to conduct an analysis of the state of legislative regulation of the agricultural lands market under martial law, to identify legal defects in the specified legislative regulation, and to substantiate ways of improving land legislation aimed at regulating the agricultural lands market in Ukraine in the post-war period. Methodology. The methodology includes a comprehensive analysis and generalization of the available scientific and theoretical material and the formulation of relevant conclusions and recommendations. During the research, the following methods of scientific knowledge were used: comparative-legal, systemic-structural, analysis and synthesis, and historical method. Results. In the research process, it was substantiated that in the post-war period it is necessary to develop special legal mechanisms of: a) acquisition by foreigners of ownership rights to agricultural lands with certain restrictions regarding the subject-object elements and based on the experience of the EU countries; b) fixing the damage caused to land resources by Russia's military aggression against Ukraine, its compensation, as well as the restoration of agricultural lands that were damaged as a result of the armed aggression of the Russian Federation; c) ensuring the legal regime of land plots, formed during the period of martial law; d) completion of the reform of the decentralization in the sphere of land relations, which was suspended under martial law. Originality. In the process of analyzing the legal regulation of the agricultural lands market under martial law conditions, the main areas of improvement of the land legislation, aimed at regulating the agricultural lands market in Ukraine in the post-war period, were substantiated, in particular with regard to the subject-object elements of the proper right on land as the main national wealth. Practical significance. The results of the study can be used in the scientific research field - for further development of the problems of legislative regulation of the agricultural lands market in Ukraine, as well as in law enforcement and law-making practice - in the preparation of draft legal acts aimed at improving the legislative regulation of the agricultural lands market.

In order to reduce the impact of noise on the accuracy of inversion products based on SAR images, many filtering algorithms have been developed for noise reduction of SAR images. This paper proposes a filtering method based on the spatial autocorrelation feature of the block fast Fourier transform (BFFT). The method statistically analyses the autocorrelation length of speckle noise on Sentinel-1B images for different features and then constructs a relationship between autocorrelation length and noise period. After that, the size of the optimal FFT filtering window radius was determined based on the relationship between the noise period and the components in the image frequency domain. Finally, we filtered the SAR image within the parcels. We compared BFFT with six commonly used filtering methods. The results show that: (1) The noise periods of the soybean, corn, paddy, and water objects on the SAR image have little difference, with noise periods of 3.36, 3.17, 3.13, and 3.14 pixels on the VV polarization and 3.49, 3.17, 2.94, and 2.42 pixels on the VH polarization; (2) after the BFFT filtering in the land parcel area, the mean value of the backscattering coefficient (BC) kept constant, whilst at the same time, the standard deviation (STD) was reduced to half of that before the filtering and (3) the BFFT and NLM filtering methods have a better effect on noise reduction inside the block. The BFFT filtering method retains the variation trend between different regions within the block and preserves the block boundary's clarity. This study provides a new idea for refined image processing. [ABSTRACT FROM AUTHOR]

Accurate and timely mapping of essential urban land use categories (EULUC) is vital to understanding urban land use distribution, pattern, and composition. Recent advances in leveraging big open data and machine learning algorithms have demonstrated the possibility of large-scale mapping of EULUC in a new cost-effective way. However, they are still limited by the transferability of samples, models, and classification results across space, particularly across different cities. Given the heterogeneities of environmental and socioeconomic conditions among cities, in-depth studies of data and model adaptation towards city-specific EULUC mappings are highly required to support policy making, and urban renewal planning and management practices. In addition, the trending need for timely and detailed small land unit data processing with finer data granularity becomes increasingly important. We proposed a City Meta Unit (CMU) data model and classification framework driven by multisource data and artificial intelligence (AI) algorithms to address these challenges. The CMU Framework was innovatively applied to systematically set up a grid-based data model and classify urban land use with an improved AI algorithm by applying Moore neighborhood correlations. Specifically, we selected Xiamen, Fujian, in China, a coastal city, as the typical testbed to implement this proposed framework and apply an AI transfer learning technique for grid and parcel land-use study. Experimental results with our proposed CMU framework showed that the grid-based land use classification performance achieves overall accuracies of 81.17% and 76.55% for level I (major classes) and level II (minor classes), which is much higher than the parcel-based land use classification (overall accuracies of 72.37% for level I, and 68.99% for level II). We further investigated the relationship between training sample size and classification performance and quantified the contribution of different data sources to urban land use classifications. The CMU framework makes data collections and processing intelligent and efficient, with finer granularity, saving time and cost by using existing open social data. Incorporating the CMU framework with the proposed grid-based model is an effective and new approach for urban land use classification, which can be flexibly extended and applied to various cities. [ABSTRACT FROM AUTHOR]

Intensive land use assessment is a key research topic in urban land use, and most of the existing studies focus on macro-level assessment. There is a lack of research on the micro-level assessment of intensive urban land use, especially at the parcel level. The objective of this research is to propose a method for the parcel-based evaluation of urban commercial land intensification. The study uses a multidimensional evaluation framework and index system, comprehensive evaluation, and spatially exploratory analysis of urban commercial intensive land use based on “building intensity, use efficiency, compatibility, and diversity”. The study finds that (1) the average value of intensive use of urban commercial land is 13.01, the standard deviation is 5.11, and the median value is 13, which generally indicate a medium level. (2) The degree of intensive use of commercial land has obvious characteristics of a high, medium, and low level. The study shows that when evaluating the degree of land use intensification at the parcel level, it is also necessary to consider the influence of the compatibility and diversity of external land use. The research results can provide a basis for spatial planning and the optimal design of urban land resources to improve urban vitality.

This work proposes an approach for establishing coffee management classes mainly supported by pedological information (soil survey) and land parcels, taking into account peculiarities of Brazilian coffee crops (land parcels already implemented with different crop ages, cultivars and density) and inspired by some management zone concepts. Two initial datasets were used based on soil survey and/or coffee crop management information. Eight sequences of tests were developed, involving: ranking of the most important variables for coffee yield modeling by random forest, reduction of data dimensionality through principal component analysis (PCA) or factorial analysis of mixed data (FAMD), generation of clusters with the hierarchical cluster on principal component (HCPC), applying hierarchical tree by using Ward's minimum variance method and improved by k-means classification. Cluster effectiveness was assessed by statistical difference in coffee yield. A total of 3 clusters were considered the most proper number of management classes, composed by the most accurate random forest model (crop age, crop density, silt fraction and soil organic matter content ranked as most important variables) and highest % of variables explanation by PCA. Although not well explored for such a purpose, HCPC applied in this study case was effective on generating homogeneous management classes, differing statistically from each other by means of coffee yield. [ABSTRACT FROM AUTHOR]

The interactive relationship between transportation and land use has become more difficult to understand and predict, due to the economic boom and corresponding fast-paced proliferation of private transportation and land-development activities. A lack of coordination between transportation and land-use planning has created an imbalanced provision of transportation infrastructure and land-use patterns; this is indicated by places where a high-density land-development pattern is supported by a low-capacity transport system or vice versa. With this, literature suggests that Mixed Land-Use (MLU) developments have the potential to provide relevant solutions for urban sustainability, smart growth, inclusive public transit use, and efficient land-use. Therefore, this study employed deep neural network models—Long Short-Term Memory (LSTM), and Multilayer Perceptron (MLP)—for forecasting the effect of transportation supply on the MLU pattern at the parcel level in the Jiang'an District, Wuhan, China. The findings revealed a strong relationship between the supply of public transportation and MLU. Moreover, the study results indicated that MLU is widely available in areas with high accessibility, high density, and proximity to the city center. The forecasting results from the MLP and LSTM models showed an average error of 5.55–7.36% and 3.62–4.28% for mixed use, respectively, while most of their 90th percentile errors were less than 13.73% and 10.46% for mixed use, respectively. The proposed models and the findings from this study should be useful for stakeholders and policy makers for more precise forecasting of MLU at the urban level. [ABSTRACT FROM AUTHOR]

Abandonment of agricultural land is a process described from different regions of many industrialized countries. Given the current focus on land use, land use change and food security, it appears highly relevant to develop improved tools to identify and monitor the dynamics of agricultural land abandonment. In particular, the temporal aspect of abandonment needs to be assessed and discussed. In this study, we used the detailed information available through the Norwegian subsidy claim database and analyzed the history of use of unique land parcels through a fourteen-year period. We developed and tested five different statistics identifying these land parcels, their temporal dynamics and the extent of occurrence. What became apparent was that a large number of land parcels existing in the database as agricultural land were taken out of production, but then entered into production again at a later stage. We believe that this approach to describe the temporal dynamics of land abandonment, including how it can be measured and mapped, may contribute to the understanding of the dynamics in land abandonment, and thus also contribute to an improved understanding of the food production system. [ABSTRACT FROM AUTHOR]

In order to reduce the impact of noise on the accuracy of inversion products based on SAR images, many filtering algorithms have been developed for noise reduction of SAR images. This paper proposes a filtering method based on the spatial autocorrelation feature of the block fast Fourier transform (BFFT). The method statistically analyses the autocorrelation length of speckle noise on Sentinel-1B images for different features and then constructs a relationship between autocorrelation length and noise period. After that, the size of the optimal FFT filtering window radius was determined based on the relationship between the noise period and the components in the image frequency domain. Finally, we filtered the SAR image within the parcels. We compared BFFT with six commonly used filtering methods. The results show that: (1) The noise periods of the soybean, corn, paddy, and water objects on the SAR image have little difference, with noise periods of 3.36, 3.17, 3.13, and 3.14 pixels on the VV polarization and 3.49, 3.17, 2.94, and 2.42 pixels on the VH polarization; (2) after the BFFT filtering in the land parcel area, the mean value of the backscattering coefficient (BC) kept constant, whilst at the same time, the standard deviation (STD) was reduced to half of that before the filtering and (3) the BFFT and NLM filtering methods have a better effect on noise reduction inside the block. The BFFT filtering method retains the variation trend between different regions within the block and preserves the block boundary’s clarity. This study provides a new idea for refined image processing.

Accurate and timely mapping of essential urban land use categories (EULUC) is vital to understanding urban land use distribution, pattern, and composition. Recent advances in leveraging big open data and machine learning algorithms have demonstrated the possibility of large-scale mapping of EULUC in a new cost-effective way. However, they are still limited by the transferability of samples, models, and classification results across space, particularly across different cities. Given the heterogeneities of environmental and socioeconomic conditions among cities, in-depth studies of data and model adaptation towards city-specific EULUC mappings are highly required to support policy making, and urban renewal planning and management practices. In addition, the trending need for timely and detailed small land unit data processing with finer data granularity becomes increasingly important. We proposed a City Meta Unit (CMU) data model and classification framework driven by multisource data and artificial intelligence (AI) algorithms to address these challenges. The CMU Framework was innovatively applied to systematically set up a grid-based data model and classify urban land use with an improved AI algorithm by applying Moore neighborhood correlations. Specifically, we selected Xiamen, Fujian, in China, a coastal city, as the typical testbed to implement this proposed framework and apply an AI transfer learning technique for grid and parcel land-use study. Experimental results with our proposed CMU framework showed that the grid-based land use classification performance achieves overall accuracies of 81.17% and 76.55% for level I (major classes) and level II (minor classes), which is much higher than the parcel-based land use classification (overall accuracies of 72.37% for level I, and 68.99% for level II). We further investigated the relationship between training sample size and classification performance and quantified the contribution of different data sources to urban land use classifications. The CMU framework makes data collections and processing intelligent and efficient, with finer granularity, saving time and cost by using existing open social data. Incorporating the CMU framework with the proposed grid-based model is an effective and new approach for urban land use classification, which can be flexibly extended and applied to various cities.

In 2019, the Austrian surveyors will celebrate the 50 years anniversary of the enactment of the Austrian Surveying Act. The date of the enactment of this law was also the hour of birth for the Austrian Legal Boundary Cadastre (germ. der Grenzkataster) and with it a milestone to ensure the legal security of parcel boundaries. The jubilee is taken as the occasion to introduce the development, implementation and running of the Legal Boundary Cadastre to a wider scientific community. After a short history of the Austrian Cadastre, the process of its transition from the Fiscal Cadastre to the Legal Boundary Cadastre is documented in the article. The current situation with strengths and weaknesses of the system is outlined and scheduled developments are introduced.

One of the most important characteristic features of a land parcel as an object of property titles is the specifics of possession. Nowadays such possession is usually done through making title to land public by entries in a public registry. Mentioning property title directly in the law can be also considered as a record of title. At the same time, it is obvious that it is not enough to make the title public, it is also necessary to somehow identify the land parcel itself. The aim of the article is to establish the legal nature of the land parcel description as a part of title registration, and to identify the proper remedies in various cases of possession by record violations. The author argues that the protection of the owner deprived of his or her possession should be done by means of actio vindicatio. There is no theoretical basis for introduction of additional or specific remedies, in particular, the remedies of "annulment of state registration of land parcels", "rendering title documents invalid", "termination of rights in land" etc. The claim to establish a boundary has specific legal nature and can be identified as actio finium regundorum which was already known at the time of Ancient Rome as a specific in rem remedy. The specific features of land parcels as immovable objects can create situations when land parcel can be in simultaneous conflicting possession of two and more persons (including situations when titles are "recorded" by a reference in the law). Such violations should be regarded as lasting and can be remedies through actio vindicatio. [ABSTRACT FROM AUTHOR]

The interactive relationship between transportation and land use has become more difficult to understand and predict, due to the economic boom and corresponding fast-paced proliferation of private transportation and land-development activities. A lack of coordination between transportation and land-use planning has created an imbalanced provision of transportation infrastructure and land-use patterns; this is indicated by places where a high-density land-development pattern is supported by a low-capacity transport system or vice versa. With this, literature suggests that Mixed Land-Use (MLU) developments have the potential to provide relevant solutions for urban sustainability, smart growth, inclusive public transit use, and efficient land-use. Therefore, this study employed deep neural network models—Long Short-Term Memory (LSTM), and Multilayer Perceptron (MLP)—for forecasting the effect of transportation supply on the MLU pattern at the parcel level in the Jiang’an District, Wuhan, China. The findings revealed a strong relationship between the supply of public transportation and MLU. Moreover, the study results indicated that MLU is widely available in areas with high accessibility, high density, and proximity to the city center. The forecasting results from the MLP and LSTM models showed an average error of 5.55–7.36% and 3.62–4.28% for mixed use, respectively, while most of their 90th percentile errors were less than 13.73% and 10.46% for mixed use, respectively. The proposed models and the findings from this study should be useful for stakeholders and policy makers for more precise forecasting of MLU at the urban level.

Purpose of research. Determine the investment income of the entrepreneur for the land. To substantiate the market value of the developed land plot with the operating business for tax purposes.Materials and methods. The basis of the paper is the norms of thelaw in appraisal activity; the results of cadastral evaluation of real estate objects in Russian settlements; the method of comparative analysis of factors determining the market and cadastral value of real estate objects.Results. Methodological bases of calculation of market and cadastralvalue of built-up land plots of settlements as capitalized rent of non- reproducible factors of market value of a single real estate object are formulated. It is shown that in the theory and practice of measurement of market and cadastral value of the built-up parcels of land of settlements of Russia article 3 of the law 135-Federal Law of 29.07.1998 is violated, in consequence of what, the market and cadastral value of capital construction in 2–5 times is overestimated, and the market and cadastral value of parcels of land in 2–10 times is underestimated. The formula of calculation of investment profit of the entrepreneur for the land plot is offered.Conclusion. Regardless of the method (technology) of property valuation, cadastral value of built-up land in a settlement should be determined as the difference between the market value of a single property and the market value of its improvements, using the built-up land as analogs. That is, excluding from the value of a single property the value of improvements of the land, we receive the market value of the built-up land, as “not built-up” clause 20, Federal Valuation Standard №7.

Implementing green infrastructure (GI) to reach certain stormwater reduction goal may be a challenging task for some land parcels (LP) in urban areas due to their unfavorable landuse conditions. In this paper, we proposed a capacity/credit trading (CT) method that allows city LPs with favorable landuse conditions to build more GIs than required and trade their extra capacity as monetary credit to LPs with building difficulties; this will allow the whole city area to achieve general stormwater mitigation goal in a more cost effective way. We investigated the effects of CT on cost reduction and (re)distribution of GIs among LPs over different trading scales, and an optimization model was constructed on the basis of different zoning of CT. The model was applied to determine GI distributions among individual LPs in order to minimize the overall cost. With a case study, we demonstrated that, without CT, requiring individual LPs to meet the mitigation goal on their own can be costly, and the cost grows with implementation pressure from storm runoff reduction; engaging CT for GI implementation reduced the cost significantly even at a small trading scale. Our analysis showed that, cost increment for GI implementation can be cut in half by performing CT at a spatial scale of 500–600 m that includes 5–6 LPs; when the CT trading zone is expanded to 1200 m that include 17 LPs, the cost increment can be cut by 3/4. The benefit of CT is obtained by re-distributing GIs among different LPs; but the spatial scale of CT needs to be limited to preserve the virtue of onsite treatment of stormwater with GIs. The proposed approach can be used to take advantage of the city landuse diversities to lower the overall cost of GI implementation for stormwater management. [ABSTRACT FROM AUTHOR]

Abandonment of agricultural land is a process described from different regions of many industrialized countries. Given the current focus on land use, land use change and food security, it appears highly relevant to develop improved tools to identify and monitor the dynamics of agricultural land abandonment. In particular, the temporal aspect of abandonment needs to be assessed and discussed. In this study, we used the detailed information available through the Norwegian subsidy claim database and analyzed the history of use of unique land parcels through a fourteen-year period. We developed and tested five different statistics identifying these land parcels, their temporal dynamics and the extent of occurrence. What became apparent was that a large number of land parcels existing in the database as agricultural land were taken out of production, but then entered into production again at a later stage. We believe that this approach to describe the temporal dynamics of land abandonment, including how it can be measured and mapped, may contribute to the understanding of the dynamics in land abandonment, and thus also contribute to an improved understanding of the food production system.

The first maps and studies in the field of land use were initiated in the United States as early as before World War I. Between the two world wars such studies were carried out with the main focus on regional planning. Planning process at all levels is usually based on rich statistical materials, which allows to compare the state of the space in different time periods. In the presented paper, the authors investigated transformations of the spatial structure of lands for a part of Podgórze, the district of Krakow for the period of the past 169 years. Source materials utilised for the research works and analyses included cadastral maps from the selected analysed years, i.e. 1847, 1909, 2003 and 2016. Based on the mentioned materials, the characteristics of land parcels as spatial features were analysed and changes were determined for the periods of 1847–1909, 1909–2003 and 2003–2016 showing the present situation. Computer processing of maps was performed for this purpose, and distributions and correlations between spatial variables characterizing parcel structure were analysed for the study area.

Cadastral information is a key component for many land use functions and a core to the effective land markets, land use and sustainable development. These applications require up-to-date, reliable and complete data. This paper focuses on one aspect of land cadastre – identification of the objects of the cadastre. The characteristics and development of the existing cadastral system are discussed. We follow an empirical approach and propose an overview how and in what form the objects of state land cadastre are identified and registered in Ukraine. The paper analyses the problems associated with the plurality of the land cadastre objects. It is proposed to remove the registration numbers of the state land cadastre objects and new cadastral numbers of the land parcel from the legislation and return the legalization of the old cadastral number of the land parcel. The paper also underlines the necessity to consider the land parcel as a single object of the state land cadastre. Finally, it is proposed that the cadastral number of restrictions in the use of the land parcel will be an immense advantage in land registration and in visualisation the restrictions on the public cadastral map of Ukraine.

Land fragmentation is a major obstacle to agricultural development in Nigeria. The objective of this study is to examine land fragmentation and its determinants by smallholder farmers. In Nigeria through the use of Ikenne Agricultural zone of Ogun State. A multi-stage sampling procedure was used to select 120 smallholder farmers in the study area. Descriptive statistics, Simpson index and Stepwise Multiple Linear Regression were used. The results of the Simpson’s Index showed that the average land fragmentation index was 0.38 which implies that smallholder farm lands are highly fragmented. Household average annual income (p

Explicit spatial information about crop types on smallholder farms is important for the development of local precision agriculture. However, due to highly fragmented and heterogeneous cropland landscapes, fine-scale mapping of smallholder crops, based on low- and medium-resolution satellite images and relying on a single machine learning (ML) classifier, generally fails to achieve satisfactory performance. This paper develops an ensemble ML-based framework to improve the accuracy of parcel-level smallholder crop mapping from very high spatial resolution (VHSR) images. A typical smallholder agricultural area in central China covered by WorldView-2 images is selected to demonstrate our approach. This approach involves the task of distinguishing eight crop-level agricultural land use types. To this end, six widely used individual ML classifiers are evaluated. We further improved their performance by independently implementing bagging and stacking ensemble learning (EL) techniques. The results show that the bagging models improved the performance of unstable classifiers, but these improvements are limited. In contrast, the stacking models perform better, and the Stacking #2 model (overall accuracy = 83.91%, kappa = 0.812), which integrates the three best-performing individual classifiers, performs the best of all of the built models and improves the classwise accuracy of almost all of the land use types. Since classification performance can be significantly improved without adding costly data collection, stacking-ensemble mapping approaches are valuable for the spatial management of complex agricultural areas. We also demonstrate that using geometric and textural features extracted from VHSR images can improve the accuracy of parcel-level smallholder crop mapping. The proposed framework shows the great potential of combining EL technology with VHSR imagery for accurate mapping of smallholder crops, which could facilitate the development of parcel-level crop identification systems in countries dominated by smallholder agriculture.

Quantifying the urbanization level is an essential yet challenging task in urban studies because of the high complexity of this phenomenon. The urbanization degree has been estimated using a variety of social, economic, and spatial measures. Among the spatial characteristics, the Shannon entropy of the landscape pattern has recently been intensively explored as one of the most effective urbanization indexes. Here, we introduce a new measure of the spatial entropy of land that characterizes its parcel mosaic, the structure resulting from the division of land into cadastral parcels. We calculate the entropies of the parcel areas’ distribution function in different portions of the urban systems. We have established that the Shannon and Renyi entropies R0 and R1/2 are most effective at differentiating the degree of a spatial organization of the land. Our studies are based on 30 urban systems located in the USA, Australia, and Poland, and three desert areas from Australia. In all the cities, the entropies behave the same as functions of the distance from the center. They attain the lowest values in the city core and reach substantially higher values in suburban areas. Thus, the parcel mosaic entropies provide a spatial characterization of land to measure its urbanization level effectively.

Cadastral information is a key component for many land use functions and a core to the effective land markets, land use and sustainable development. These applications require up-to-date, reliable and complete data. This paper focuses on one aspect of land cadastre - identification of the objects of the cadastre. The characteristics and development of the existing cadastral system are discussed. We follow an empirical approach and propose an overview how and in what form the objects of state land cadastre are identified and registered in Ukraine. The paper analyses the problems associated with the plurality of the land cadastre objects. It is proposed to remove the registration numbers of the state land cadastre objects and new cadastral numbers of the land parcel from the legislation and return the legalization of the old cadastral number of the land parcel. The paper also underlines the necessity to consider the land parcel as a single object of the state land cadastre. Finally, it is proposed that the cadastral number of restrictions in the use of the land parcel will be an immense advantage in land registration and in visualisation the restrictions on the public cadastral map of Ukraine. [ABSTRACT FROM AUTHOR]

Land consolidation is an important stage for agriculture and rural development. During the land reform, parcels of irrational size and shape were formed, which became difficult to cultivate efficiently. In essence, land consolidation projects must primarily be developed in areas with large, viable farms, and land parcels forming land holdings are not compact, distant from each other and from centers. Land consolidation projects have been under development for many years, but it is appropriate to pay attention to the expediency of implemented projects. Land consolidation goals and procedures vary from country to country, as the development of this procedure in each country is determined by historical trends, culture, traditions and land consolidation legislation. Many of those who have analyzed this topic emphasize the need for consolidation, but it is very important that landowners understand the importance of this process and make sure of its opportunities and benefits. Land consolidation must be designed so that the benefits of the project are higher than the results of the conversion works before the project. The purpose of this article is to compare land consolidation projects which were prepared in different areas of Lithuania. Two objects of the research in which land consolidation projects had been carried out and implemented in 2013 were selected, where the number of sites in units decreased: 41.8% in one project and 28% in the other. Changes in parcel configuration were also noticeable, when in one of the projects analyzed even 92 percent of land parcels have become regular - close to rectangular - shapes. The average size of land parcels in this project increased from 5.32 ha to 9.14 ha. [ABSTRACT FROM AUTHOR]

Abstract The ability to accurately and precisely perform soil nutrient mapping over large areas is essential in the decision-making processes for precision agriculture. However, existing grid-based or non-grid-based digital soil mapping (DSM) can lead to the problem of mixed units of input information, which causes the mapping results to be unsuitable for direct use in guiding the implementation of precision agriculture. Instead, the goal of this study was to achieve DSM based on land parcels, which are the basic units of agricultural management and have practical geographical significance for precision mapping in agricultural areas. This study established a convolutional neural network-based automatic extraction model to extract land parcels from high resolution remote sensing images. Thirty environmental covariates were chosen and calibrated at land parcels to establish the relationships between soils and landscapes. Four prediction algorithms, namely, ordinary kriging, cokriging, random forest and artificial neural network, were combined with the land-parcel-based DSM framework to develop and evaluate their effectiveness in predicting four topsoil nutrient properties in an alluvial-diluvia plain agricultural region located in Ningxia province, China. The results of comparisons show that, overall, the land-parcel-based RF model achieved the best prediction accuracy; its relative improvement (RMSE %) values over the competing models were 1.27, 4.23, 3.19 and 9.01 for soil organic matter, soil total nitrogen, available phosphorus and available potassium, respectively. In addition, land-parcel-based mapping can improve algorithmic efficiency by approximately 4 times by effectively reducing the mapping units for complex agricultural areas compared with the grid-based mapping results when using the same algorithm, and it also achieves a better performance at the detail level. Overall, the land-parcel-based DSM approach achieved good results in plain agricultural areas, but the model still needs improvement for land-parcel-based DSM in mountainous and hilly agricultural areas, and a challenge remains in selecting the most appropriate environmental covariates. Graphical abstract Unlabelled Image Highlights • Soil nutrient properties are predicted using a land parcel-based DSM method. • Land parcel are automatically extracted from GF-2 fusion image (0.8 m). • Four models and two mapping units are compared in their performance. • The land parcel-based digital soil maps include spatial prediction and uncertainty. • The spatial variations and influence factors at land-parcel scale were analyzed. [ABSTRACT FROM AUTHOR]

In 2019, the Austrian surveyors will celebrate the 50 years anniversary of the enactment of the Austrian Surveying Act. The date of the enactment of this law was also the hour of birth for the Austrian Legal Boundary Cadastre (germ. der Grenzkataster) and with it a milestone to ensure the legal security of parcel boundaries. The jubilee is taken as the occasion to introduce the development, implementation and running of the Legal Boundary Cadastre to a wider scientific community. After a short history of the Austrian Cadastre, the process of its transition from the Fiscal Cadastre to the Legal Boundary Cadastre is documented in the article. The current situation with strengths and weaknesses of the system is outlined and scheduled developments are introduced. [ABSTRACT FROM AUTHOR]

Land fragmentation is a major obstacle to agricultural development in Nigeria. Therefore, the objective of this study is to examine land fragmentation and its determinants as seen by smallholder farmers in Ikenne Agricultural zone of Ogun State, Nigeria. A multistage sampling procedure was used to select 120 smallholder farmers in the study area. Descriptive statistics, Simmons index and Tobit Regression were used. The results of the Simmons index showed that the average land fragmentation index was 0.38, implying that smallholder farmland is highly fragmented. The average annual household income (p < 0.01), labor force of household (p < 0.05), education level (p < 0.01) and land ownership (p < 0.1) were the significant factors that negatively influenced land fragmentation in the study area. However, the size of land rented in by household (p < 0.001) significantly increases the degree of land fragmentation. Therefore, land consolidation and application of specific land protection policies to prevent agricultural land from being developed for non-agricultural purposes are recommended. [ABSTRACT FROM AUTHOR]

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The boundary extraction of an object from remote sensing imagery has been an important issue in the field of research. The automation of farmland boundary extraction is particularly in demand for rapid updates of the digital farm maps in Korea. This study aimed to develop a boundary extraction algorithm by systematically reconstructing a series of computational and mathematical methods, including the Suzuki85 algorithm, Canny edge detection, and Hough transform. Since most irregular farmlands in Korea have been consolidated into large rectangular arrangements for agricultural productivity, the boundary between two adjacent land parcels was assumed to be a straight line. The developed algorithm was applied over six different study sites to evaluate its performance at the boundary level and sectional area level. The correctness, completeness, and quality of the extracted boundaries were approximately 80.7%, 79.7%, and 67.0%, at the boundary level, and 89.7%, 90.0%, and 81.6%, at the area-based level, respectively. These performances are comparable with the results of previous studies on similar subjects; thus, this algorithm can be used for land parcel boundary extraction. The developed algorithm tended to subdivide land parcels for distinctive features, such as greenhouse structures or isolated irregular land parcels within the land blocks. The developed algorithm is currently applicable only to regularly arranged land parcels, and further study coupled with a decision tree or artificial intelligence may allow for boundary extraction from irregularly shaped land parcels.

One of the land reform implementation results in Ukraine is the distribution of the state-owned agricultural land to the rural population in the form of physical land parcels. As a consequence, however, the land was subdivided into many small units. This land fragmentation has led to fundamental changes in the formation of the new agricultural enterprises and brought some negative consequences in their functioning. The problem of the land fragmentation in Ukraine is quite new and uninvestigated. The aim of the article is to analyze the existing measures (instruments) in European countries for reducing the effects of agricultural land fragmentation and to determine the possibility of «transplantability» of Western experience to Ukraine. The principal measures to decrease the agricultural land fragmentation in European countries are: voluntary parcel exchange, land banking and land consolidation. The article presents the characteristics and comparative analysis of these measures. One of the four types of land fragmentation is a main problem of Ukraine, namely the discrepancy between the landownership and the land use. The Western European countries have been used the three instruments for reducing only two types of land fragmentation: the land use fragmentation and the internal fragmentation. Consequently, the using of Western European measures to decrease agricultural land fragmentation is impossible without their adaptation to the Ukrainian realities. Therefore, the actual problem in Ukraine today is to find the own measures to overcome the problem of agricultural land fragmentation based on the Western European experience.

The aim of the study is to evaluate the spatial variation in the size and shape of land plots in Mazowieckie voivodship. For the purposes of the study both the shape and the compactness of the plots were measured, and the typology of communes was drawn based on this information. Subsequently, based on the two indicators related to the shape of plots, four types of communes were distinguished, depending on whether their values were higher or lower than the average (Dzieciucho­wicz and Dmochowska-Dudek, 2014). In addition, the paper includes calculations for the average share of unused land in the plot surface area, the average horizontal intensity of parcel development, and an indication of the type of land coverage dominant in the plots, by commune type. Pearson’s correlation coefficient was used in the analyses. It has been found that the studied area is dominated by plots characterised by small diversity in shape and high degree of compactness – mainly in the north-western part of the voivodship, as well as those where plots are highly diverse in shape and low in compactness – in the south-eastern part of the voivodship.