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The current issues that provide a scientific basis for improving land management are: determining the relationship between the concepts of land protection and environmental protection, which also includes land as a component of the natural environment; substantiating the differentiation of norms of land and environmental law in a narrow sense with regard to land protection; establishing the limits of differentiation of legal norms governing land protection, depending on their legal regime. Purpose: to substantiate the differentiation of norms of land and environmental law in a narrow sense with regard to land protection; to identify the specifics of the methods of legal impact used in the legal regulation of land protection; to establish the relationship between the legal regime of land and obligations for its protection. Methods: empirical methods of comparison, description, interpretation; theoretical methods of formal and dialectical logic; specific scientific methods: legal-dogmatic and interpretation of legal norms. Results: the article reveals that the protection of lands from negative impact on the environment should constitute a subject of environmental law in a narrow sense, since in this case the negative impact is carried out on lands as a component of a complex environmental object, and the relevant legal requirements are conditioned by the type of activity resulting in negative impact, as well as by the nature of this negative impact. Rational use is ensured by compliance with the legal regime of a land parcel. The main elements of the legal regime of a land parcel are a meaningful reference point for determining measures aimed at restoring the disturbed state of lands.

The addition of transit routes transforms urban development by disrupting the existing equilibrium that land parcels have achieved over time and promotes revitalization. It is based on the relationships between land parcel variables and transit route characteristics, including feeder routes and road infrastructure. Traditional parametric methods for explaining this relationship have problems with multicollinearity and generalizability while non-parametric methods are not used with the multiple variables of both transit route and land parcel changes over time. This study applies the C5.0 decision tree algorithm, a non-parametric model that creates a decision tree with leaf nodes that predict the relationship. Using the BRT Lahore case study, the time series data of parcel variables in the 2 km circle of five transit stations before BRT 2010 and after BRT 2018, as well as transit route characteristics including feeder routes and road infrastructure, were collected and analyzed. The model identified eight important predictors and explained the relationship in the form of a flowchart. Property condition emerged as the strongest predictor, followed by property value, parking, population density, land use, building height, access routes, and distance from transit stations, in that order. The results show that well-developed transport infrastructure, parking spaces, and feeder routes enable sustainable urban transformation.

Agricultural land parcels (ALPs) are essential for effective agricultural management, influencing activities ranging from crop yield estimation to policy development. However, traditional methods of ALP delineation are often labor-intensive and require frequent updates due to the dynamic nature of agricultural practices. Additionally, the significant variations across different regions and the seasonality of agriculture pose challenges to the automatic generation of accurate and timely ALP labels for extensive areas. This study introduces the cadastral-to-agricultural (Cad2Ag) framework, a novel approach that utilizes cadastral data as training labels to train deep learning models for the delineation of ALPs. Cadastral parcels, which are relatively widely available and stable elements in land management, serve as proxies for ALP delineation. Employing an adapted U-Net model, the framework automates the segmentation process using remote sensing images and geographic information system (GIS) data. This research evaluates the effectiveness of the proposed Cad2Ag framework in two U.S. regions—Indiana and California—characterized by diverse agricultural conditions. Through rigorous evaluation across multiple scenarios, the study explores diverse scenarios to enhance the accuracy and efficiency of ALP delineation. Notably, the framework demonstrates effective ALP delineation across different geographic contexts through transfer learning when supplemented with a small set of clean labels, achieving an F1-score of 0.80 and an Intersection over Union (IoU) of 0.67 using only 200 clean label samples. The Cad2Ag framework’s ability to leverage automatically generated, extensive, free training labels presents a promising solution for efficient ALP delineation, thereby facilitating effective management of agricultural land.

Agricultural land parcels (ALPs) are essential for effective agricultural management, influencing activities ranging from crop yield estimation to policy development. However, traditional methods of ALP delineation are often labor-intensive and require frequent updates due to the dynamic nature of agricultural practices. Additionally, the significant variations across different regions and the seasonality of agriculture pose challenges to the automatic generation of accurate and timely ALP labels for extensive areas. This study introduces the cadastral-to-agricultural (Cad2Ag) framework, a novel approach that utilizes cadastral data as training labels to train deep learning models for the delineation of ALPs. Cadastral parcels, which are relatively widely available and stable elements in land management, serve as proxies for ALP delineation. Employing an adapted U-Net model, the framework automates the segmentation process using remote sensing images and geographic information system (GIS) data. This research evaluates the effectiveness of the proposed Cad2Ag framework in two U.S. regions—Indiana and California—characterized by diverse agricultural conditions. Through rigorous evaluation across multiple scenarios, the study explores diverse scenarios to enhance the accuracy and efficiency of ALP delineation. Notably, the framework demonstrates effective ALP delineation across different geographic contexts through transfer learning when supplemented with a small set of clean labels, achieving an F 1 -score of 0.80 and an Intersection over Union (IoU) of 0.67 using only 200 clean label samples. The Cad2Ag framework's ability to leverage automatically generated, extensive, free training labels presents a promising solution for efficient ALP delineation, thereby facilitating effective management of agricultural land. [ABSTRACT FROM AUTHOR]

Depending on the development of remote sensing technologies, data at various spatial, temporal and spectral resolutions are obtained and play a key role in Earth investigation. Land cover/use maps are also created using these data. These data play an important role as a base for determining land cover and land use classes, detecting changes, monitoring forest, agriculture and wetlands, as well as updating the LPIS data used by countries. As well as being reference data in the LPIS update, current land cover and land use classification results with satellite images also provide statistical information about which class has changed over the years and how much the class or classes need to be updated, and will also provide the planning of the update to be made on a national scale. In this study, land cover classification for 2020 covering Çivril-Baklan Plain, which is between the borders of Denizli Province (Turkey), Baklan, Çal and Çivril Districts, was performed. The LPIS classes of the study area drawn in 2015 and the land cover classes as a result of the 2020 classification were compared as a result of the classification process, and particularly the areas with change were referenced in the LPIS update. In the classification process, the open source Eo-Learn library, which uses machine learning and deep learning algorithms in remote sensing studies and Sentinel-2 images that can be accessed directly in this library were used. The open source Eo-Learn library has been preferred because it provides great convenience to users in the classification process with ready-made machine learning models and easy access to satellite images, image processing steps (cloud masking, calculate index, feature extraction, temporal interpolation) are carried out in a certain workflow. Instead of land cover classification from a single image, 17 different time (multi-temporal) images between 01.02.2020 and 31.11.2020 were used. In the classification process, 12 different features were used for the image taken on each date, including NDVI, NDWI, NDBI indices and Tasseled Cap transformations, as well as (Blue) B02, (Green) B03, (Red) B04, (NIR) B08, (SWIR-1) B11, (SWIR-2) B12 bands. In the classification process, the Light Gradient Boosting Machines (LightGBM) algorithm in the Eo-Learn library and the physical blocks produced within the scope of the Land Parcel Identification System (LPIS) project were used as ground truth data; arable land, bare land, forest, artificial surface, shrubland, tree crops and water classes were created. The classification results were evaluated using the K fold (k = 5) cross validation method, with F1 score, recall, and precision values calculated for each class, and the overall accuracy %92.2. When the classification result is compared with the LPIS data, it is seen that the arable land, shrub land, and water classes have changed. This study determined that these classes, in particular, need to be updated.

The addition of transit routes transforms urban development by disrupting the existing equilibrium that land parcels have achieved over time and promotes revitalization. It is based on the relationships between land parcel variables and transit route characteristics, including feeder routes and road infrastructure. Traditional parametric methods for explaining this relationship have problems with multicollinearity and generalizability while non-parametric methods are not used with the multiple variables of both transit route and land parcel changes over time. This study applies the C5.0 decision tree algorithm, a non-parametric model that creates a decision tree with leaf nodes that predict the relationship. Using the BRT Lahore case study, the time series data of parcel variables in the 2 km circle of five transit stations before BRT 2010 and after BRT 2018, as well as transit route characteristics including feeder routes and road infrastructure, were collected and analyzed. The model identified eight important predictors and explained the relationship in the form of a flowchart. Property condition emerged as the strongest predictor, followed by property value, parking, population density, land use, building height, access routes, and distance from transit stations, in that order. The results show that well-developed transport infrastructure, parking spaces, and feeder routes enable sustainable urban transformation. [ABSTRACT FROM AUTHOR]

The purpose of this study was to statistically test the effect of a parcel index - consisting of a combination of a soil index, a fertility index and a location index - intended to be used as a price-determining indicator for the sale of agricultural land at farmland markets. In the hedonic price model, the coefficients of the variables representing parcel index, population, gross return and parcel irrigation investment status were positive and statistically significant at a significance level of 0.01. There was a negative relationship between parcel size and sale price, which implied that the selling price per decare tends to decrease as the parcel size increases. In the study area, the prices of farmland with large parcel sizes and irrigation efficiency investments were higher. The population density in the region and gross income from farmlands were the major factors that generated demand for the land. The hedonic price model establishes an important link between the parcel index and the sale price of farmland. Based on this link, parcel index-based pricing can contribute significantly to the creation of a farmland market in Turkey.

Homeowners’ Associations (HOAs) dictate landscape structure and management through legally enforceable land covenants at the neighborhood scale in the USA. Determining the location and spatial extent of HOAs is critical for examining its influence. However, such analysis is confounded by the lack of spatial data at the appropriate unit for such analysis. The purpose of this paper is to develop and realize an open source implementation to automate land parcel classification, which is an initial step towards the goal of determining the impact of HOAs on urban land management. Using Maricopa County, Arizona as a testbed, we found that parcel merging processes reduce the number of subdivisions from 26,042 to 17,269, such that boundaries better align with neighborhood units to which rule sets like land covenants apply. Moreover, after an initial training period, this process was completed in just over 7 hours. This research is an important first step in enabling a number of analysis including determining the location and spatial extent of HOAs regionally and, eventually, nationally and determining proposed links between HOAs and land management outcomes.

Currently the official land parcel map of Uruguay does not allow a suitable interoperability with other available geographic information. We are considering using a national high accuracy and high resolution orthomosaic in two ways: either to support the creation of a brand new land parcel map or to allow amending the existing one. In particular, the present work is part of a set of experiments which considers different techniques to amend the existing land parcel map. In order to perform a fair comparison it is necessary to have test cases produced with a reference procedure properly and objectively evaluated. The metric of success will be used as attainable goal for any other alternative. Using the orthomosaic in order to identify the apparent limits of parcels in the land parcel map we were able to modify its location, thus making the map interoperable with the image. In order to measure the accuracy of the fit, we used the relative difference between the area declared at the parcel map (taken as the reference value) respect to the one computed from the GIS. We found that the discrepancy between the areas computed from the initial and the final dataset w.r.t. the declared area decreases, but not up to expected level. Once filtered for outliers the remaining discrepancies follow a normal distribution, where its bias and standard deviation varies from (-0.65%, 11.24%) before editing to (‑1.72%, 8.59%) later. Despite the drastic change in planimetric accuracy the improvement of the area accuracy is marginal. Considering the admissibility criteria of the Uruguayan National Cadastral Office, for the legacy dataset only 39.4% parcel maps could be accepted, a value which improves up to 49.7% after the manual edition. It is still far from the goal of 100%, and the reasons for such pessimistic finding will be discussed.

Accurate and up-to-date information on the spatial and geographical characteristics of agricultural areas is an indispensable value for the various activities related to agriculture and research. Most agricultural studies and policies are carried out at the field level, for which precise boundaries are required. Today, high-resolution remote sensing images provide useful spatial information for plot delineation; however, manual processing is time-consuming and prone to human error. The objective of this paper is to explore the potential of deep learning (DL) approach, in particular a convolutional neural network (CNN) model, for the automatic outlining of agricultural plot boundaries from orthophotos over large areas with a heterogeneous landscape. Since DL approaches require a large amount of labeled data to learn, we have exploited the open data from the Land Parcel Identification System (LPIS) from the Chartered Community of Navarre, Spain. The boundaries of the agricultural plots obtained from our methodology were compared with those obtained using a state-of-the-art methodology known as gPb-UCM (global probability of boundary followed by ultrametric contour map) through an error measurement called the boundary displacement error index (BDE). In BDE terms, the results obtained by our method outperform those obtained from the gPb-UCM method. In this regard, CNN models trained with LPIS data are a useful and powerful tool that would reduce intensive manual labor in outlining agricultural plots.

Today the land parcel map of Uruguay is available as an updated, integrated legacy digital cadastre which planimetric accuracy is not enough to assure geometric interoperability. The creation of a brand new land parcel map is being considered with a procedure yet to be defined but in any case subject to reasonable goals in terms of geometric accuracy. In this paper we have tested a direct procedure in which each parcel is joined to its immediate neighbors until a block is formed. In Uruguay such block will grow until a road, river or similar object is found. The block is manually inserted within the road network, which has absolute coordinates. The rural survey maps (approx. 250 000) are already scanned but they need to be vectorized and edited through scaling, rotation and manual matching to its neighbors. GNSS tracks for roads and highways are available for the whole country, so we could fit the block within them in order to properly register it. The resulting geometric accuracy was evaluated in terms of the relative error in area, which has a normal distribution with a mean 0.10% and a standard deviation 3.51%, leaving 82.0% of the parcels with relative area error less than 5%. The planimetric error, crucial to assure interoperability, was 53.79 m at the 95% confidence level.

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.

Peri-urban agriculture (PUA) has been widely regarded as a sub-field of multifunctional agriculture for improving the sustainability of urban environments. However, urban sprawl has both negative and positive effects on peri-urban farming, and the research on this issue in Japan is insufficient. This study aims to demonstrate the spatial distribution of farmland parcels in Tokyo and Osaka metropolitan areas and explore the synergistic effect of distance from cities and urban sprawl on the size of farmland parcels and farm-diversified activities such as direct marketing, farming experience, and environmentally friendly practices. Ordinary Least Squares (OLS) and Poisson regression analyses were used with a nationwide agricultural land parcel Geographic Information System (GIS) database (Tokyo metropolitan area = 1,939,162 and Osaka metropolitan area = 1,507,072 parcels), in Japan, to specify the farmland locations and calculate the extent of urban sprawl. The results revealed that more than 50% of farmlands in the targeted areas were located within 4 km from the boundaries of densely inhabited districts (DIDs). Furthermore, with a decreasing distance from a DID, the urban sprawl had more positive effects on farmland parcel sizes and farm-diversified activities. These findings imply that PUA has a wider presence in Japan, and the peri-urban farmers may be capable of utilizing the multifunctional nature of intensively sprawled urban environments.

Soil moisture is one of the important hydrological elements. Obtaining soil moisture accurately and effectively is of great significance for water resource management in irrigation area. During the process of soil moisture content retrieval with multiremote sensing data, multi- remote sensing data always brings multi-spatial scale problems which results in inconformity of soil moisture content retrieved by remote sensing in different spatial scale. In addition, agricultural water use management has suitable spatial scale of soil moisture information so as to satisfy the demands of dynamic management of water use and water demand in certain unit. We have proposed to use land parcel unit as the minimum unit to do soil moisture content research in agricultural water using area, according to soil characteristics, vegetation coverage characteristics in underlying layer, and hydrological characteristic into the basis of study unit division. We have proposed division method of land parcel units. Based on multi thermal infrared and near infrared remote sensing data, we calculate the ndvi and tvdi index and make a statistical model between the tvdi index and soil moisture of ground monitoring station. Then we move forward to study soil moisture remote sensing retrieval method on land parcel unit scale. And the method has been applied in Hetao irrigation area. Results show that compared with pixel scale the soil moisture content in land parcel unit scale has displayed stronger correlation with true value. Hence, remote sensing retrieval method of soil moisture content in land parcel unit scale has shown good applicability in Hetao irrigation area. We converted the research unit into the scale of land parcel unit. Using the land parcel units with unified crops and soil attributes as the research units more complies with the characteristics of agricultural water areas, avoids the problems such as decomposition of mixed pixels and excessive dependence on high-resolution data caused by the research units of pixels, and doesn’t involve compromises in the spatial scale and simulating precision like the grid simulation. When the application needs are met, the production efficiency of products can also be improved at a certain degree.

Satellite crop detection technologies are focused on the detection of different types of crops in fields. The information of crop-type area is more useful for food security than the earlier phenology stage is. Currently, data obtained from remote sensing (RS) are used to solve tasks related to the identification of the type of agricultural crops; additionally, modern technologies using AI methods are desired in the postprocessing stage. In this paper, we develop a methodology for the supervised classification of time series of Sentinel-2 and Sentinel-1 data, compare the accuracies based on different input datasets and find how the accuracy of classification develops during the season. In the EU, a unified Land Parcel Identification System (LPIS) is available to provide essential field borders. To increase usability, we also provide a classification of the entire field. This field classification also improves overall accuracy.

The LPIS (Land Parcel Identification System) supports area-based payment schemes in EU countries under the Common Agricultural Policy and is a useful tool enabling the proper functioning of the structural agriculture support mechanism. This study analyses the agricultural parcel management system in Poland in the context of solutions applied in the EU within the Common Agricultural Policy. The research concerns solutions applied in the field of the LPIS system, arising from European legal and technical specifications, particularly those referring to the adopted type of a reference parcel (agricultural parcel, cadastral parcel, farmers’ block, physical block, topographic block). Based on the data originating from LPIS questionnaire interviews, a study was conducted related to the adopted models of reference parcels in the EU countries and regions, including in Poland in the years 2006–2016. The research sample comprised LPIS systems functioning in the EU in the analytical period. In the paper, it is demonstrated how the implemented models of reference parcels had been changing over time and the most frequent reasons of the changes were indicated. Poland is one of few EU Member States in which the Land and Property Register has been the basis for the LPIS since 2006. The conclusions of the research provide a basis for planning of a change in LPIS in Poland.

The article addresses special conditions sanctioning demolition of buildings and structures erected without a permit on the state-owned land parcel restricted for construction according to the Georgian legislation and the practice of suspension of enforcement proceedings of similar resolutions by the court. It reviews the regulation envisaged by part 4 of Article 25 of Product Safety and Free Movement Code, according to which buildings constructed on state and/or self-governing unit-owned land parcels are subject to demolition and an appeal of the resolution on demolition does not suspend its enforcement. Despite the above, within the procedural legislation, the court suspends the enforcement process in the part of demolition. The article assesses the prospective of placing such buildings and structures within the legal framework in the future (compliance with the general plan, functional zoning, rules of development and use of the territory), i.e. their eligibility for obtaining a permit in deciding on the suspension/non-suspension of the demolition enforcement process. In Georgian practice, this factor is not accen­tuated by the construction control body when making a resolution on demolition, nor does the court evaluate it, which is the most significant circumstance in the process of making and enforcing such type of resolutions. Examples from the Polish and German practice where a permit allowing illegal buildings and structures to be admitted (as complying with the substantive con­struction law) is one of the most significant preconditions for making a resolution regarding demolition are presented and discussed. The circumstances to be considered in the course of suspension by the court of demolition of buildings and structures illegally constructed on state and/or self-governing unit-owned land parcels are also described, and legislative changes/ amended regulations for the sake of avoidance of procrastination of such type of demolitions have been recommended.

【Objective】 Plants in most urban areas need irrigation, and understanding their demand for irrigation is important for urban water management. This paper analyzes the spatiotemporal variation in irrigation demand of green spaces in small and medium-sized metropolitan cities in arid areas of Northwest China, as well as the influencing factors. 【Method】 A net irrigation water accounting model was developed for plot scale by integrating meteorological data, root zone soil moisture, surface quantitative remote sensing data, and the principle of root zone soil water balance. Quantile random forest and Bayesian linear regression models were used to identify key factors that influence irrigation water demand. 【Result】 ① In 2022, the daily average net irrigation water demand of green space plots estimated from the model ranged from 0.45 to 0.85 mm, slightly lower than the measured values. Temporarily, irrigation demand peaked during the vegetation growth period. Spatially, plots with denser, healthier vegetation had high demand for irrigation, showing significant spatial autocorrelation. ② Key factors that affect irrigation demand included root zone soil moisture, surface temperature, vegetation growth, sunshine duration and air temperature. Meteorological data and surface remote sensing images explained 61% and 78% of the variation in net irrigation water demand, respectively, highlighting the multifaceted nature of irrigation demand. 【Conclusion】 Understanding the demand of urban green spaces for irrigation at plot scale has an important implication for urban water resource allocation and improving urban water management.

The purpose of this study was to statistically test the effect of a parcel index – consisting of a combination of a soil index, a fertility index and a location index – intended to be used as a price-determining indicator for the sale of agricultural land at farmland markets. In the hedonic price model, the coefficients of the variables representing parcel index, population, gross return and parcel irrigation investment status were positive and statistically significant at a significance level of 0.01. There was a negative relationship between parcel size and sale price, which implied that the selling price per decare tends to decrease as the parcel size increases. In the study area, the prices of farmland with large parcel sizes and irrigation efficiency investments were higher. The population density in the region and gross income from farmlands were the major factors that generated demand for the land. The hedonic price model establishes an important link between the parcel index and the sale price of farmland. Based on this link, parcel index-based pricing can contribute significantly to the creation of a farmland market in Turkey. [ABSTRACT FROM AUTHOR]

Termite nests have long been suggested to be good indicators of groundwater but only a few studies are available to demonstrate the relationship between the two. This study therefore aims at investigating the most favourable spots for locating groundwater structures on a small parcel of land with conspicuous termite activity. To achieve this, geophysical soundings using the renowned vertical electrical sounding (VES) technique was carried out on the gridded study area. A total of nine VESs with one at the foot of a termitarium were conducted. The VES results were interpreted and assessed via two different techniques: (1) physical evaluation as performed by drillers in the field and (2) integration of primary and secondary geoelectrical parameters in a geographic information system (GIS). The result of the physical evaluation indicated a clear case of subjectivity in the interpretation but was consistent with the choice of VES points 1 and 6 (termitarium location) as being the most prospective points to be considered for drilling. Similarly, the integration of the geoelectrical parameters led to the mapping of the most prospective groundwater portion of the study area with the termitarium chiefly in the center of the most suitable region. This shows that termitaria are valuable landscape features that can be employed as biomarkers in the search of groundwater.

In many developing countries, urban growth is characterised by the emergence of informal housing at the periphery. Nevertheless, there is little evidence based on data from informal land markets and, in general, studies focusing on such markets often neglect environmental factors. Therefore, to contribute to these research gaps, this article aims to enhance our understanding of land markets in informal land parcels and their relationship to environmental amenities, by providing empirical evidence from Mexico City. The article estimates a hedonic pricing model using robust ordinary least squares with a SHAC (Spatial Heteroskedasticity and Autocorrelation Consistent) inference, including structural, environmental, neighbourhood and accessibility features. Results provide empirical insights regarding the way this land market behaves in the peri-urban area. Our findings reveal that informal land parcel purchasers are willing to pay for basic services such as access to piped water, proximity to schools and accessibility features such as being close to city centre, motorways and underground stations. Although a positive relationship between land price and distance to the nearest forest or Protected Natural Area is highlighted, it is low, meaning that individuals are largely ambivalent about environmental amenities. Therefore, the problem of irregular settlements could be approached from two different angles. Firstly, informal land buyers will not desist from invading and modifying natural areas without a comprehensive urban and environmental policy, oriented towards changing the perception of green areas as potential urbanisation opportunities. Secondly, public policy needs to solve the housing supply crisis, considering the characteristics presented here. [ABSTRACT FROM AUTHOR]

Homeowners' Associations (HOAs) dictate landscape structure and management through legally enforceable land covenants at the neighborhood scale in the USA. Determining the location and spatial extent of HOAs is critical for examining its influence. However, such analysis is confounded by the lack of spatial data at the appropriate unit for such analysis. The purpose of this paper is to develop and realize an open source implementation to automate land parcel classification, which is an initial step towards the goal of determining the impact of HOAs on urban land management. Using Maricopa County, Arizona as a testbed, we found that parcel merging processes reduce the number of subdivisions from 26,042 to 17,269, such that boundaries better align with neighborhood units to which rule sets like land covenants apply. Moreover, after an initial training period, this process was completed in just over 7 hours. This research is an important first step in enabling a number of analysis including determining the location and spatial extent of HOAs regionally and, eventually, nationally and determining proposed links between HOAs and land management outcomes. [ABSTRACT FROM AUTHOR]

Dockless bike sharing (DBS) is convenient for bike users because it can solve their last-mile problems. However, dockless bikes that are disorderly parked in public spaces can disturb normal traffic. In planning electric fences for DBS systems, the maximum coverage location problem (MCLP) identifies locations suitable for electric fences. However, the MCLP model ignores the regional boundaries of bike use and causes substantial errors in coverage assessments. In this article, we present a novel framework for planning electric fences, accounting for both trip distribution and area shapes. We first introduce a dynamic land parcel subdivision algorithm, which can reasonably divide the city into fine-scale regions concerning the distributions of spatial parking demands. Then, we formulate the electric fence-planning problem as a regional coverage maximization problem. We develop an accelerated maximum coverage model with complementary coverage (MCMCC) to locate electric fence areas, which can improve solution accuracy and reliability. The DBS data of Shenzhen, China, are used to validate the framework. The study findings indicate that MCMCC produces more robust and accurate results than the MCLP model because the planning solutions are not affected by changes in the shapes and sizes of the regions. The framework can also automatically and accurately determine electric fence capacities and locations. [ABSTRACT FROM AUTHOR]

The current agroecological transition of agriculture pushes to a diversification of cropping systems, which requires quantified data describing crop successions. “Crop successions indicators 2015-2021” dataset provides a set of twenty synthesis indicators to characterize crop diversity, crop seasonality, particular components of crop successions and duration of crop rotations. Indicators are computed for a seven-year period from 2015 to 2021. Data source are raw crop sequences open access dataset. Indicators are available for municipalities, departments, regions and the whole mainland France, for arable land. A group of experts in agronomy has been associated to this work, in order to define relevant themes, relevant indicators and relevant indicator definitions. This dataset could be useful to characterize agricultural practices on a given territory, for researchers, local actors as decentralized state services, water agencies, territorial collectivities, chambers of agriculture, or agricultural cooperatives. Proposed indicators could be useful for policy makers to monitor the evolution of cultivation practices, in order to design, implement or evaluate measures targeting cultivation practices.

Čeprav je identifikacijski sistem za zemljišča v Sloveniji star šele nekaj let, je doživel že kar nekaj pomembnih sprememb. Sprva so bili uporabljeni atributni podatki zemljiškega katastra, nato kot grafična podlaga podatki zemljiškokatastrskega prikaza, vendar takšna rešitev ni ustrezala visokim zahtevam po kakovosti prostorskih podatkov. Treba je bilo začeti znova in na podlagi državnih ortofoto načrtov so bila ponovno zajeta vsa kmetijska zemljišča posameznih kmetijskih gospodarstev. Do danes je sistem, ki ga v Sloveniji predstavljajo grafične enote rabe kmetijskih zemljišč, ali krajše GERK, prerasel v sistem, ki se navezuje na ali informativno uporablja obsežno zbirko prostorskih podatkov in vključuje registre trajnih nasadov. Ti prostorski podatki se uporabljajo za zagotavljanje zahtevane kakovosti v postopkih vzdrževanja in rednih postopkih administrativnih kontrol v okviru kmetijske politike. Del podatkov je vsako leto preverjen na kraju samem. Proaktivno zagotavljanje kakovosti in stalna kontrola sta najpomembnejši nalogi. Cilj je učinkovitejši sistem in torej zmanjšano tveganje pri dodeljevanju javnih sredstev upravičencem pomoči v kmetijstvu. Še dodaten korak naprej je naredila Španija, kjer je sodelovanje med zemljiškim katastrom in SIGPAC v duhu direktive INSPIRE primer dobre prakse, ki bi lahko postala model tudi za druge države članice ; Although the land parcel identification system in Slovenia was established only few years ago, it has already had been changed many times. At first, an alphanumeric land cadastre data was used, then (as a graphical basis) a digital cadastral index map was introduced; however, such solutions did not meet the high quality requirements of spatial data. It had to be started again and all agricultural parcels of individual agricultural holdings were defined on the basis of digitised orthophotos. At present, the system that is represented with graphical units of agricultural parcels (GERK) has grown into a system related to or using a broad collection of spatial data, including registers of permanent crops. This spatial data is used for the assurance of the required quality in procedures of maintenance and in regular procedures of administrative controls in the framework of agricultural policy. Every year, a part of the data is checked on the field. Proactive quality assurance and continuous control are the highest priority tasks. The objective is a more efficient system and a consequent reduction of risk in the distribution of public funds to the entitled farmers. Another step forward was made in Spain, where cooperation between land cadastre and SIGPAC in the frame of the INSPIRE directive is an example of good practise, which could become a model for the other Member States.

IntroductionThe Common Agricultural Policy (CAP) is a vital policy framework implemented by the European Union to regulate and support agricultural production within member states. The Land Parcel Identification System (LPIS) is a key component that provides reliable land identification for administrative control procedures. On-the-spot checks (OTSC) are carried out to verify compliance with CAP requirements, typically relying on visual interpretation or field visits. However, the CAP is embracing advanced technologies to enhance its efficiency.MethodsThis study focuses on using Sentinel-2 time series data and a two-level approach involving recurrent neural networks (RNN) and convolutional neural networks (CNN) to accurately identify permanent pastures.ResultsIn the first step, using RNN, the model achieved an accuracy of 68%, a precision of 36%, a recall of 97% and a F1-score of 52%, which indicates the model’s ability to identify all the true positive parcels (correctly identified permanent pasture parcels) and minimize the false negative parcels (non-identified permanent pasture parcels). This occurs due to the difficulty in distinguishing between permanent pastures and other similar land covers (such as temporary pastures and shrublands). In the second step, it was possible to distinguish the permanent pasture parcels from the others. The obtained results improved significantly from the first to the second step. Using CNN, an accuracy of 93%, a precision of 89%, and a recall of 98% were achieved for the “Permanent pasture” class. The F1-score was 94%, indicating a balanced measure of the model’s performance.DiscussionThe integration of advanced technologies in the CAP’s control mechanisms, as demonstrated, has the potential to automate the verification of farmers’ declarations and subsequent subsidy payments.

维护数据更新前后的空间拓扑一致性与属性逻辑一致性是数据更新过程中必须考虑的问题.由于地类图斑的属性面积与图形面积在数据库存储的精度不同，四舍五入后造成数据更新前后属性面积统计值存在差异.本文在分析地理实体变化类型后提出了数据更新过程中地类图斑的6种变化类型，针对分割与综合变化2种具有代表性的变化类型，设计与实现了不同的维护面积一致性的方法，并在实际应用系统中得到有效验证.