Government Decision Research Articles

The adoption of dual Vocational Education and Training in Spain: Analysis of descentralised decision-making in educational policy transfer

This paper examines the adoption of dual VET in Spain as a process of educational policy transfer. Using Phillips and Ochs’ (2003, 2004) policy transfer spectrum and decision-making classification and Perry and Tor’s (2008) model of transfer forms, the study relies on semi-structured interviews with key decision-makers in the Spanish central government and the Autonomous Communities of the Basque Country and Andalusia. Findings indicate that EU diffusion activities facilitated adoption, involving first-order learning. Initially, adoption at the central level was uninformed, driven by quick-fix decisions, but later shifted to informed transfer through second- and third-order learning, enhancing realism. In Andalusia and the Basque Country, decision-making consistently adapted to the real context, progressing from second- to third-order learning. Ultimately, this analysis concludes that dual VET adoption at the central, Andalusian, and Basque levels falls between ‘negotiated under constraint’ and ‘borrowed purposefully’, though not at the same point.

The Existence of Kerapatan Adat Nagari (KAN) in Society Decision Making: Case Study in Durian Tinggi

In general, the aim of this research is to map the existence of the Kerapatan Adat Nagari (KAN) in Nagari Durian Tinggi. The specific objectives are analyzing the structure and function of the KAN and analyzing the degree of power of KAN in government decision making in nagari. This research uses qualitative methods, with a field study approach to collect data. Data was collected through in-depth interviews and observations. The data that has been collected is classified and then made into a code that answers the research questions. The answer to a research question is the results obtained and then reported into a research discussion. The manuscript resulting from this research also wants to be further developed as a form of academic reaction to the condition of KAN in several nagari based on the latest regional regulations, such as the dilemma of positioning the role of KAN which is feared to overlap with the role of the nagari government and also for education for the community in increasing policy on the position of the elements contained in the nagari, both government and non-government or elements of society.

The coupling characteristics and influencing factors of the "homestead-cultivated land system" in the Three Gorges Reservoir Region of China: A farmer perspective

Homesteads and cultivated land are crucial elements of the rural territorial system. Their coupling characteristics and changing patterns are essential for understanding the evolution of rural human-land systems and for developing coordinated rural development strategies. This study takes Huilong Village and Pingba Village in the TGRR, employing methods such as GIS spatial analysis, comprehensive index evaluation, coupling coordination degree model, and geographic detector model. The analysis focuses on the coupling characteristics and influencing factors of the “HCS” in these villages and proposes optimization strategies to provide scientific guidance for village planning, selection of high-quality rural development paths, and strategy design. The main findings are as follows: (1) The spatial distribution of homesteads and cultivated land in typical villages exhibits clear traffic directionality. The spatial coupling of the “HCS” demonstrates significant convergent evolution and adjacent distribution characteristics. (2) The utilization coupling of the “HCS” in Huilong Village and Pingba Village is predominantly high, with 98.72% and 94.62% of farmers, respectively, being highly coupled. However, the degree of coupling coordination is mostly barely coordinated. (3) The primary factors influencing the coordinated development of the “HCS” in the study area include the utilization conditions of homestead and cultivated land, labor status, and economic status. These factors can be categorized into four types: labor factor restrictions, nonagricultural development disturbance, agricultural development, and lagging farming conditions. Differentiated development strategies are proposed for each type. This research enhances the understanding of the coupling between homestead land and cultivated land, offering valuable insights for regional village planning, high-quality development path selection, and government decision-making.

Framework for multivariate carbon price forecasting: A novel hybrid model

The complex characteristics of volatility and non-linearity of carbon price pose a serious challenge to accurately predict carbon price. Therefore, this study proposes a new hybrid model for multivariate carbon price forecasting, including feature selection, deep learning, intelligent optimization algorithms, model combination and evaluation indicators. First, this study collects and organizes the historical carbon price series of Hubei and Shanghai as well as the influencing factors in five dimensions including structured and unstructured data, totaling twenty variables. Second, data dimensionality reduction is performed and input variables are obtained using the least absolute shrinkage and selection operator, followed by the introduction of nine advanced deep learning models to predict carbon price and compare the prediction effects. Then, through the combination of models, three models with the best performance are combined with Pelican optimization algorithm to construct a hybrid forecasting model. Finally, the experimental results show that the developed forecasting model outperforms other comparation models in terms of prediction accuracy, stability and statistical hypothesis testing, and exhibits excellent prediction performance. Furthermore, this study also applies the developed model to European carbon market price prediction and uses the Hubei carbon market as an example for quantitative trading simulation, and the empirical results further verify its robust prediction performance and investment application value. In conclusion, the proposed hybrid prediction model can not only provide high-precision carbon market price prediction for the government and corporate decision makers, but also help investors optimize their trading strategies and improve their returns.

가상가치평가법(CVM)을 활용한 청와대(靑瓦臺) 이용가치 추정

Purpose: This study employs a 2-stage dichotomous choice contingent valuation method (CVM), to evaluate the use value of Cheongwadae (the Blue House)--the former presidential office and residence of South Korea--which opened for tourist visits in May 2022. Methods: To minimize the hypothetical bias, hypothetical and actual scenarios (cash/card payment) were jointly used. Survey data of 400 Cheongwadae visitors were collected, and the data were analyzed using willingness to pay (WTP) estimation and logistic regression analysis. Results: Logistic regression analysis revealed that bid amount, landmark attributes (form, symbolism), experience factors (esthetic, escape), gender, age, marital status, visitor type, and political orientation significantly influenced respondents’ willingness to pay (WTP) for entry fees. The estimated per-person use value, based on the truncated mean WTP, was KRW 6,002 in the hypothetical scenario and KRW 2,347 in the actual scenario. This difference suggests that using an actual scenario and a truncated mean is more appropriate for minimizing hypothetical bias and estimating a more accurate value. Considering the total number of visitors in the past 12 months (3,381,664, May 2023), the total use value of Cheongwadae was calculated as KRW 7937.37 million. Conclusion: This study’s findings highlight Cheongwadae’s significant economic and cultural value as a landmark. The results can assist public awareness campaigns regarding Chengwadae’s socio-economic importance and guide government decisions for its preservation and development as a tourism attraction. Furthermore, the methodological insights on minimizing hypothetical bias in CVM studies can be applied to the future valuation of cultural heritage sites.

Spatiotemporal changes and driving mechanism of ecosystem carbon sink in karst peak cluster depression basin in Southwest Guangxi based on the interaction of “water-rock-soil-air-biology”

Research on ecosystem carbon sinks is vital for developing policies to reduce emissions and enhance carbon sequestration. Analyzing the temporal and spatial trends of carbon sinks and their driving mechanisms is crucial for guiding policy and measures. Traditional methods for estimating carbon sinks in karst regions focus primarily on vegetation-soil carbon sinks, mainly comprising net primary productivity (NPP) and soil heterotrophic respiration (Rh). However, these methods overlook the unique karst carbon sink (KCS), leading to significant uncertainties in carbon sink evaluations. This study focuses on the karst peak cluster depression basin in southwest Guangxi and proposes a Karst Ecosystem Carbon Sink Assessment System (KECAS) based on the “water-rock-soil-air-biology” coupling framework, considering lithological characteristics. Theil-Sen trend analysis and restricted cubic spline (RCS) regression were used to explore the temporal and spatial variations and driving mechanisms of carbon sinks in the area from 2000 to 2022. The results show the following: (1) The total carbon sink flux (TCSF) in the study area has shown an upward trend over time, with a 23-year average ranging from 538 to 929 g CO2 m−2 a−1 and a growth rate of 3.1039a−1. This means TCSF increases by 6.78 g CO2 m−2 a−1 each year. (2) Spatially, areas with increasing carbon sinks cover 34.17 % of the study area, mainly in Chongzuo, Nanning, and Wenshan Prefecture. Areas with decreasing carbon sinks cover 11.79 % of the total area, mainly in Chongzuo, with smaller areas in Nanning and Baise. The mean TCSF distribution is higher in the northwest and lower in the southeast. (3) The critical thresholds for influencing factors were identified using RCS regression: precipitation (1430 mm), temperature (17.9 °C), evapotranspiration (855 mm), NDVI (0.63), DEM (637 m), slope (16.3°), nighttime light index (6.21), and population density (421 people/km2). (4) Areas with high carbon sink supply potential, based on natural and human thresholds, account for 25.09 % and 92.97 % of the southwestern Guangxi study area, respectively. When considering both natural and human conditions, high potential areas account for approximately 22.31 % of the total study area. These findings provide a scientific basis for carbon sink protection and management and offer valuable references for government decision-making.

PELAKSANAAN PEMILIHAN PRESIDEN YANG SESUAI DENGAN PRINSIP KEDAULATAN RAKYAT DALAM SISTEM DEMOKRASI DI INDONESIA

This research was submitted to answer two fundamental questions regarding the implementation of people's sovereignty in the direct election of the President and Vice President in Indonesia, namely, what is the ideal ideal of the implementation of the presidential election in accordance with the principle of people's sovereignty in the democratic system in Indonesia. This research is normative. This research is a library research conducted by examining library materials in the form of books, journals, research reports and/or secondary data that discuss the implementation of general elections in Indonesia in relation to people's sovereignty. Application of laws and regulations (legal facts) that regulate the implementation of elections in Indonesia. From this research, the author concludes that in a democracy, people's rights should be respected and upheld. There is no justification for government decisions that can harm people's rights, let alone policies that aim to oppress the people for the sake of the ruler. In this case, by adhering to the basic philosophy of democracy guided by the wisdom of deliberation and representation, the holding of general elections as a means of implementing people's sovereignty in Indonesia must not deviate from the ideals and objectives of the formation of a Government of the State of Indonesia to protect all Indonesian people and all of Indonesia's territory, advance public welfare, improve the life of the nation, and participate in implementing world order based on independence, eternal peace and social justice as mandated by the constitution.

The Extent to which Conflict of Interest in the Public Sector Affects Private Sector Functioning

This analysis explores the profound implications of conflict of interest in the public sector on private companies, highlighting the intricate relationship between governmental decisions and market outcomes. Public-sector conflicts of interest can distort regulatory frameworks, disrupt market competition, and compromise ethical standards, leading to significant challenges for private enterprises, including financial instability and reduced investor confidence. The study underscores the importance of transparent governance and ethical integrity in maintaining a fair and competitive economic environment. It concludes that addressing conflicts of interest in public administration is not only a matter of ethical compliance but a critical necessity for ensuring equitable business practices and sustaining long- term economic growth. By fostering a regulatory environment centered on transparency, accountability, and ethical governance, we can mitigate the detrimental effects of conflicts of interest, thereby promoting a healthier and more trustworthy landscape for private sector operations.

The role of political institutions in the religious sphere before and during the Russo-Ukrainian war: national security vs. freedom of religion

The paper aims to demonstrate characteristics of interaction between governmental efforts to maintain national security and the situation with freedom of religion in Ukraine. To answer the questions of how political decisions have influenced the religious sphere in Ukraine over the past five years including wartime and whether there is a balance between national interests and freedom of religion we apply event impact assessment comparing trends of transitions of parishes and people attitudes towards different Orthodox churches before and after political actions and governmental decisions during 2018–2023. The paper confirms that although religion was repeatedly brought into the political sphere religious pluralism persists in Ukraine on the territories that are under government control. We demonstrate that establishing the Orthodox church that was independent of Russia bolstered freedom of religion and full-scale Russia invasion caused a steep increase in many religious transfers from the Ukrainian Orthodox Church of the Moscow Patriarchate (UPC MP) to the Orthodox Church of Ukraine (OCU). Despite an increased role of the state and issues of national security due to Russian aggression, the religious sphere was quite autonomous.

The Development of Industrial Agglomeration in Industrial Designation Areas and its Impact on Land Use Change (Case Study: Pringsurat Subdistrict and Kranggan Subdistrict, Temanggung Regency)

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>Industrial agglomeration refers to the clustering of industries in a specific region, where they are in close proximity and have interconnections. This phenomenon not only stimulates the growth of industrial activities, but also influences the development of other economic sectors. As industrialization expands, it gradually extends into rural areas that usually have abundant undeveloped land primarily used for agriculture. Consequently, this trend prompts a transition from agricultural to industrial land use. Pringsurat subdistrict and Kranggan subdistrict are located in Temanggung Regency, Central Java Province. Both of these subdistricts are designated as Industrial Designation Areas (KPI) according to the Temanggung Regency Spatial Plan 2011-2031. The plan has implications for the rapid development of large and medium industries. The purpose of this study is to examine the growth of industrial agglomeration in Pringsurat subdistrict and Kranggan subdistrict and their impact on agricultural land conversion. This research was conducted from 2023 to 2024 and utilizing data from 2021 and 2022. The approach of this research is descriptive quantitative and utilizes spatial analysis with Geographic Information Systems (GIS) and descriptive analysis techniques. Based on the results of the analysis, there is an agglomeration of large and medium industries in Pringsurat subdistrict and Kranggan subdistrict. The growth of agglomeration encourages the establishment of new industries and industry linkages. Furthermore, agglomeration growth has led to changes in land use, an increase in the variety of land uses, and an increase in land prices. Results of the analysis also show that there are regulatory changes in the development of industrial agglomeration that will affect to local government decision in the future. </span></p></div></div></div>

A new transferable deep learning approach for crop mapping

ABSTRACT Accurate crop mapping provides important information for government decision-making and agricultural management. Recent advancements in deep learning have significantly enhanced the capabilities of remote sensing-based crop mapping. In this study, we developed a new deep learning approach, DSH, which comprises three modules, DeepLabV3+ (D), channel self-attention (S), and histogram matching (H). The DeepLabV3+ module learns the spatial distribution of crops in the spatial and spectral dimensions. The channel self-attention module was used to enhance the weights of the important features. The histogram-matching module addresses the domain gap between the training and testing images and enhances the transferability of the DSH. In addition, the input data of the DSH are monthly synthesized Sentinel-2 data, thus relaxing the data collection requirements. The performance of the DSH was evaluated and benchmarked against HRNetV2, DeepLabV3+, and random forest (RF) at eight sites in the U.S. China, and France with different farming structures, climate conditions, and landscape complexities. Temporal transfer revealed that the classification performance of DSH improved as the growth season progressed, peaked in the peak growth season (August), and then declined at each study site. The DSH model trained during the peak growth season demonstrated superior temporal transferability. Spatial transfer experiments demonstrated that transferring DSH to sites with similar planting structures achieved higher precision than full spatial transfers. Spatiotemporal transfer experiments conducted at eight sites over three years (2020–2022) demonstrated an average overall accuracy (OA) of 88.1% for DSH, highlighting the importance of similar planting structures for effective spatiotemporal transfer. DSH outperformed HRNetV2, DeepLabV3+, and RF in OA, producer’s accuracy, and user’s accuracy at all eight study sites. Notably, DSH exhibited comparable performance during both the peak and full growth seasons, with an average OA of 93.9%. The classification accuracy of test images after histogram matching remained stable from 2020 to 2022, whereas the accuracy of raw test images fluctuated annually. This indicates that histogram matching is effective in improving the transferability of DSH. Embedding the channel self-attention module into the position of the high-level features had a more significant impact on improving DSH’s performance than other configurations. Visualizing features from the channel self-attention and high-level layers revealed that integrating the channel self-attention module with DeepLabV3+‘s high-level features significantly improved crop mapping by enhancing relevant semantic information. Overall, DSH exhibits robust spatiotemporal generalizability and requires minimal input data, making it suitable for large-scale crop mapping.

Policy design for renewable energy development based on government support: A system dynamics model

Energy's centrality to life, work, and progress underscores its indispensable role in modern society. However, burgeoning energy consumption and finite resources have prompted concerns regarding resource sustainability. Consequently, renewable energy sources have gained prominence as alternatives, integral to societal advancement. Therefore, with the increase in the price of fossil fuels and its limitations and the attention to renewable energies, in this research, we decided to study the implementation processes of the development of this type of energy about possible future scenarios and possible implementation policies at the time of each scenario. Let's pay Considering the dynamic complexities of this issue over time and the involvement of government sectors in policy discussions and private sectors in the implementation and participation of these two groups simultaneously, using dynamic system modeling to design a dynamic model that economic aspects, technical, political and environmental aspects should be evaluated simultaneously in a causal-disability manner. To implement this model, two scenarios of government support and non-support for this industry were taken into consideration, and in each scenario two policies of workforce training and technology import for the use of private sector managers and in the best scenario two policies of tax exemption and reduction of technology import tariff for Government decision-making was reviewed. The results showed that when this industry enjoys government support, the speed of development of renewable energy projects can be greatly increased by importing equipment. Without governmental backing, expedited progress is achievable through skilled labor training and employment. Tax exemptions are the most effective policy tool for stimulating renewable energy project processes, production, and sales. The findings underscore the pivotal role of strategic governmental intervention in facilitating renewable energy development, which is essential for meeting energy demands while mitigating environmental impact.

Construction of a Food Safety Evaluation System Based on the Factor Analysis of Mixed Data Method.

Food safety evaluation, which aims to reflect food safety status, is an important part of food safety management. Traditional food evaluation methods often consider limited data, and the evaluation process is subjective, time-consuming, and difficult to popularize. We developed a new food safety evaluation system that incorporates simple qualification degrees, food consumption, project hazard degrees, sales channels, food production regions, and other information obtained from food safety sampling and inspection to reflect the food safety situation accurately, objectively, and comprehensively. This evaluation model combined the statistical method and the machine learning method. The optimal distance method was used to calculate the basic qualification degree, and then expert elicitation via a questionnaire and the factor analysis of mixed data method (FADM) was applied to modify the basic qualification degree so as to obtain the food safety index, which indicates food safety status. Then, the effectiveness of this new method was verified by calculating and analyzing of the food safety index in region X. The results show that this model can clearly distinguish food safety levels in different cities and food categories and identify food safety trends in different years. Thus, this food safety evaluation system based on the FADM quantifies the real food safety level, screens out cities and food categories with high food safety risks, and, finally, helps to optimize the allocation of regulatory resources and provide technical and theoretical support for government decision-making.

Rain and evapotranspiration using the Penman-Monteih model for the periods 1963-1982. 1983-2001 and 2002-2019 for the amparo de São Francisco – Sergipe, Brazil

Evaluate the performance of the empirical method of estimating evapotranspiration, with the standard Penman-Monteih method, suggested by FAO-56, for Amparo de São Francisco – Sergipe in the periods 1963-1982; 1983-2001 and 2002-2019 generating their respective graphs and information for government decision makers. The Penman-Monteih method makes it possible to estimate (ETo(PM)) in mm/month and, after computing it, it is multiplied by the total number of days in each month of the year, thus obtaining the Penman-Monteith model estimated in mm/month. The standard Penman-Monteih method can help with irrigation planning and management, especially for small irrigators who do not have access to meteorological data. A better adaptation of the Penman-Monteith and Thornthwaite model was observed in the estimation of ETo, where its results showed adaptation for the location studied. It is advisable to evaluate other models so that they can provide more appropriate results. It is stated that the Penman-Monteith and Thornthwaite models are the best alternatives for the use of evapotranspiration in Amparo de São Francisco - Sergipe.

Research on the Socio-Spatial Resilience Evaluation and Evolution of the Central Area of Beijing in Transitional China

In recent decades, the increasing threats of global climate change, natural disasters, and epidemics have brought extensive attention to resilience theory. However, most studies focus on the physical aspects of cities, overlooking the significance of the social perspective. This study addresses this gap by using social areas as the core spatial unit for analysis. By constructing a scientific indicator system and employing the set pair analysis method, this study comprehensively evaluates the multidimensional resilience levels of Beijing’s central area during its transitional period (1990–2020). The findings reveal that socio-spatial divisions, which emphasize the network structure of social relations and the dynamic changes in social spaces, are more suitable than traditional administrative divisions as fundamental units for resilience assessment. During Beijing’s transitional period, the comprehensive resilience of the central area and all social areas steadily increased, although social capital resilience faced the challenges of loss and slow recovery. Social areas with a unique internal connection, such as those based on ethnicity, exhibited more positive social capital resilience development. Additionally, social areas with lower population diversity and greater homogeneity tended to have higher comprehensive resilience and a more positive trend in social capital resilience development, whereas highly heterogeneous social areas faced significant constraints in resilience development. These findings may provide valuable insights for cities and communities to enhance their capacity to address future uncertainties and assist policymakers in making informed governance decisions.

Encoder-Decoder Transformers for Textual Summaries on Social Media Content

Social media has a leading role to our lives due to radical upgrade of internet and smart technology. It is the primary way of informing, advertising, exchanging opinions and expressing feelings. Posts and comments under each post shape public opinion on different but important issues making social media’s role in public life crucial. It has been observed that people's opinions expressed through social networks are more direct and representative than those expressed in face-to-face communication. Data shared on social media is a cornerstone of research because patterns of social behavior can be extracted that can be used for government, social, and business decisions. When an event breaks out, social networks are flooded with posts and comments, which are almost impossible for someone to read all of them. A system that would generate summarization of social media contents is necessary. Recent years have shown that abstract summarization combined with transfer learning and transformers has achieved excellent results in the field of text summarization, producing more human-like summaries. In this paper, a presentation of text summarization methods is first presented, as well as a review of text summarization systems. Finally, a system based on the pre-trained T5 model is described to generate summaries from user comments on social media.

Evaluating Regional Potentials of Agricultural E-Commerce Development Using a Novel MEREC Heronian-CoCoSo Approach

In terms of both economy and sustainability, rural areas can greatly benefit from adopting E-commerce. The Chinese government is currently devoting significant efforts to developing agricultural E-commerce. However, one of the most significant problems is the lack of effective tools for evaluating regional potentials in this regard, possibly leading to inappropriate policymaking, investment allocation, and regional planning. To address this issue, this study proposes a novel and effective method for evaluating regional potentials for agricultural E-commerce development, integrating the method based on the removal effects of criteria (MEREC), Heronian mean operator, and combined compromise solution (CoCoSo) method. The method’s effectiveness is then tested and confirmed in the Chinese city of Yibin through an evaluation of its ten regions. The results suggest that such a method is robust, objective, and able to consider indicator interactions effectively. By applying this method, regional agricultural E-commerce development potentials can be thoroughly evaluated and ranked. This study contributes to the literature by providing new analytical techniques for agricultural studies, as well as by supporting political and investment decision-making for governments and E-commerce practitioners in the agriculture sector.

The Problems and Countermeasures of Chongqing's University Think Tanks Participating in Government Decision-Making

The Problems and Countermeasures of Chongqing's University Think Tanks Participating in Government Decision-Making

Sustainable Maintenance of Conductors in Transmission/Distribution Networks Using Complex Magnetic Field Analysis

This study presents issues related to electromagnetic pollution and the level of magnetic field radiation occurring around conductors used for electricity transmission and distribution. The fact that modeling and simulation are the most efficient methods of optimization, considering the cost–benefit ratio, was the premise of this work. This paper proposes the performance of a complex analysis, carried out in a comparative manner, which includes physical tests and simulations in the existing field around transmission and distribution cables used in transformer substations. In the first stage, the level of the magnetic field existing near the conductor carried by an electric current was tested (measured), and a virtual model was then designed to simulate the field in conditions similar to those of the test. The results obtained from the simulation were analyzed in comparison with those obtained by testing. The maximum permissible limits of exposure to an electromagnetic field, which are regulated by Government Decision HG 520/2016 of 20 July 2016 and Directive 2013/35/EU of the European Parliament and of the Council of 26 June 2013, were used as the reference to formulate conclusions for both situations considered. These comparisons were intended to determine the level of exposure to electromagnetic fields existing in places where electricity transmission/distribution conductors are located. Energy sustainability exists due to the versatile properties of the conductors, with the energy transmission and distribution network being functional regardless of the source of energy production.

Automatic mapping of winter wheat planting structure and phenological phases using time-series sentinel data

The precise extraction of winter wheat planting structure holds significant importance for food security risk assessment, agricultural resource management, and governmental decision-making. This study proposed a method for extracting the winter wheat planting structure by taking into account the growth phenology of winter wheat. Utilizing the fitting effect index, the optimal Savitzky–Golay (S–G) filtering parameter combination was determined automatically to achieve automated filtering and reconstruction of NDVI time series data. The phenological phases of winter wheat growth was identified automatically using a threshold method, and subsequently, a model for extracting the winter wheat planting structure was constructed based on three key phenological stages, including seeding, heading, and harvesting, with the combination of hierarchical classification principles. A priori sample library was constructed using historical data on winter wheat distribution to verify the accuracy of the extracted results. The validation of fitting effect on different surfaces demonstrated that the optimal filtering parameters for S–G filtering could be obtained automatically by using the fitting effect index. The extracted winter wheat phenological phases showed good consistency with ground-based observational results and MOD12Q2 phenological products. Validation against statistical yearbook data and the proposed priori knowledge base exhibited high statistical accuracy and spatial precision, with an extracting accuracy of 94.92%, a spatial positioning accuracy of 93.26%, and a kappa coefficient of 0.9228. The results indicated that the proposed method for winter wheat planting structure extracting can identify winter wheat areas rapidly and significantly. Furthermore, this method does not require training samples or manual experience, and exhibits strong transferability.