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This contribution focuses on the utilization of very-high-resolution (VHR) images to identify construction areas and their temporal changes aiming to estimate the investment in construction as a basis for economic forecasts. Triggered by the need to improve macroeconomic forecasts and reduce their time intervals, the idea arose to use frequently available information derived from satellite imagery. For the improvement of macroeconomic forecasts, the period to detect changes between two points in time needs to be rather short because early identification of such investments is beneficial. Therefore, in this study, it is of interest to identify and quantify new construction areas, which will turn into build-up areas later. A multiresolution segmentation followed by a kNN classification is applied to WorldView images from an area around the southern part of Berlin, Germany. Specific material compositions of construction areas result in typical classification patterns different from other land cover classes. A GIS-based analysis follows to extract specific temporal “patterns of life” in construction areas. With the early identification of such patterns of life, it is possible to predict construction areas that will turn into real estate later. This information serves as an input for macroeconomic forecasts to support quicker forecasts in future.

To effectively address the pressing issues of urban development and environmental conservation, sustainable construction areas must be identified. In this paper, a proposal for a novel method for segmenting and analyzing satellite images to locate suitable construction sites is based on infrastructure monitoring and ground subsidence monitoring operations. Our method utilizes maximally stable extremal regions with thresholding to accurately classify bodies of water, land areas, bridges, construction zones, forests, and unused land areas. This allows for a comprehensive evaluation of the landscapes. In order to refine our analysis, convolutional neural networks are used to estimate the fertilization levels of the identified areas. To determine whether a structure is suitable by analyzing the characteristics of the surrounding area and calculating the fertility probability levels. By providing a more precise and comprehensive understanding of the surrounding area, this method significantly outperforms existing methods. Extensive experiments were conducted on numerous satellite image datasets to evaluate the performance of our proposed model. With precision and accuracy rates of 99.4% and 98.9%, respectively, and recall rates of 99.2%, the results demonstrate exceptional precision, accuracy, and recall rates. In addition, the model has a remarkable area under the curve score of 98.4%, demonstrating its high discriminatory ability. Importantly, compared to recently proposed models, our method exhibits less delay, enabling rapid decision-making for green building projects. In addition, using Q-learning is a method of reinforcement learning, to incorporate continuous infrastructure monitoring into our model. This integration enables continuous evaluation and improvement of sustainability levels, ensuring the long-term viability of urban areas. It will contribute to reducing environmental impact and enhancing the overall sustainability of urban development by optimizing construction practices iteratively in response to real-time datasets and samples.

To make sure that regional reconstruction goes smoothly, it is important to know how rural construction areas in a river basin change over time and space and what factors affect those changes. This study focuses on the rural construction areas in the Nanxi River Basin. Through geographic information systems’ spatial analysis methods, the construction area morphology, center of gravity migration, and agglomeration degree are analyzed to reveal its spatiotemporal evolution from 1990 to 2020. The geographical detector is used to explore the interaction of multidimensional driving factors such as natural geography, socio-economic development, and cultural heritage protection. The research results show that (1) the rural construction area in the Nanxi River Basin shows an evolution trend of “agglomeration expansion and northward shift of the center of gravity.” (2) Cultural, economic, and natural factors all play a part in how rural construction areas change over time. Cultural factors, like the distance between farms and the layout of educational resources, have the most significant impact, followed by economic and natural factors. (3) The study also suggests a “cultural gene-natural base-economic potential” model that can help us understand how to protect cultural heritage and boost the economy at the same time. This result has direct guiding significance for the implementation of China’s rural revitalization strategy. It can give natural resource planning departments a scientific way to figure out the best way to use land and give cultural heritage management agencies a way to come up with safe development plans. It also provides a reference for the sustainable development path of resource-rich villages around the world.

[Purpose/Significance] Accelerating the construction of digital rural areas is a strategic direction for comprehensively promoting rural revitalization, which plays an important role in promoting the modern basic living conditions in rural areas, improving the quality and efficiency of farmers' production and living, and achieving the development of urban-rural integration. In recent years, the construction of digital rural areas has been actively promoted in various regions, and a number of typical models are being explored and practised which has brought more digital dividends to rural residents. However, the regular characteristics of various practices have not been summarized in related research, resulting in poor promotion of best experience models and a lack of directional indicators for digital rural construction in different regions. This paper has summarized the regular characteristics and application mechanisms of diversified digital rural construction practices in China. The relevant conclusions would provide theoretical reference for promoting the construction of digital rural areas in different regions through classification. [Method/Process] Based on the perspective of structural elements and dynamic effects, this paper investigated four typical county-level digital rural construction models as research objects, namely Deqing County, Pukou District, Cao County, and Dianjiang County, in the national or provincial-level digital rural pilot county. Then, it used a multi-case analysis method to explore the regulatory characteristics of different types of digital rural construction models. [Results/Conclusions] The construction of digital rural areas in China mainly involves the integration of urban and rural areas, the integration of industry and city, the integration of point, line, and area, and the promotion of construction throughout the county. Influenced by factors such as the main functions of the construction, the requirements of the construction content, and the resource endowment, location, and transportation of the construction area, the internal and external thrust mechanisms of different construction models also exhibit different characteristics. In promoting the construction of digital rural areas in the future, different regions should fully consider the strength of the main functions of the construction, choose different construction mechanisms and content, so that digital rural areas can benefit more farmers, and help promote the comprehensive revitalization of rural areas. However, there are some shortcomings in this paper, such as the lack of in-depth analysis on the role and functional positioning of farmers, different levels of government, and market subject as participants. More wisdom is needed to embed the operation and maintenance mechanism for the later stage of digital rural area construction in future research.

Fu, Z. and Wang, J., 2020. Study on improvement and risk assessment of marine logistics finance development model based on the background of free trade zone construction. In: Liu, X. and Zhao, L. (eds.), Today's Modern Coastal Society: Technical and Sociological Aspects of Coastal Research. Journal of Coastal Research, Special Issue No. 111, pp. 222–225. Coconut Creek (Florida), ISSN 0749-0208. The free trade area has a more important impact on the marine logistics industry, promoting the country's re-export trade, offshore trade, offshore finance, and shipping services. Through the determination of maritime logistics enterprises, this paper provides a method for the determination of the comprehensive risk value of logistics financial business. Through the establishment of the risk evaluation model by the wavelet support vector regression algorithm, from the development of the comprehensive value-at-risk business, the paper summarizes the concepts, practice forms, development process, and important relations of the relevant trade and shipping logistics industry and the foreign free trade zone.

Since 1979, the Pearl River Delta （PRD） of China has experienced rapid socio- economic development along with a fast expansion of construction area. Affected by both natural and human factors, a complex interdependency is found among the regional changes in construction area, GDP and population. A quantitative analysis of the four phases of the regional land use data extracted from remote sensing images and socioeconomic statistics spanning 1979 to 2009 demonstrates that the proportion of construction area in the PRD increased from 0.5% in 1979 to 10.8% in 2009, accompanied with a rapid loss of agricultural land. An increase of one million residents was associated with an increase of GDP of ap- proximately 32 billion yuan before 2000 and approximately 162 billion yuan after 2000. Be- cause the expansion of construction area has approached the limits of land resource in some cities of the PRD, a power function is found more suitable than a linear one in describing the relationship between GDP and construction area. Consequently, the Logistic model is shown to provide more accurate predictions of population growth than the Malthus model, particu- larly in some cities where a very large proportion of land resource has been urbanized, such as Shenzhen and Dongguan.

Unmanned pavement construction is of great significance in China, and the primary issue to be solved is how to identify the boundaries of the Pavement Construction Area (PCA). In this paper, we present a simple yet effective method, named the Bidirectional Sliding Window (BSW) method, for PCA boundary recognition. We first collected the latitude and longitude coordinates of the four vertices of straight quadrilaterals using the Global Positioning System—Real Time Kinematic (GPS-RTK) measurement principle for precise single-point positioning, analyzed single-point positioning accuracy, and determined the measurement error distribution models. Next, we took points at equal intervals along one straight line segment and two curved line segments with curvature radii of 70 m to 300 m, for simulation experiments. BSW was adopted to recognize the Possible Irrelevant Points (PIP) and Relevant Points (RP), which were used to identify PCA boundaries. Experiments show that when the proposed BSW algorithm is used and the single-point positioning accuracy is at the centimeter level, PCA boundary recognition for straight polygons reaches single-point positioning accuracy, and that for curved polygons reaches decimeter-level accuracy.

The power industry is the primary energy through the power generation equipment into electricity, and then through the transmission, transformation and distribution system to supply users as energy as the industrial sector, for the industrial and national economy and other sectors to provide basic power, is the first sector of national economic development[1]. As an important link to ensure the normal operation of the power industry, the importance of the power transmission system is self-evident[2]. The transmission tower, as the cornerstone to ensure the normal operation of the transmission system, has a large construction workload and faces problems such as complex construction environment and construction safety risks. Therefore, how to ensure the safe construction of transmission line in complex terrain is of great significance to power system construction. At present, in the construction of transmission tower, the suspension pole group tower and the whole lifting tower are the two main tower construction methods widely used. However, the pylon group tower has a great safety risk[3-4]. The State Grid has been trying to optimise the suspension of the tower. Secondly, due to the inconvenience of transportation, it is difficult for heavy lifting equipment to reach the construction site, which makes it difficult to promote the whole tower scheme using heavy lifting equipment in the mountainous terrain. In addition, the current construction of the tower group lacks scientific calculation and analysis of the force and deformation of the tower and key construction components, and there are hidden dangers such as overload and excessive deformation. Therefore, a new type of tower formation is needed to avoid the risk of personnel working at high altitudes and problems such as the inability of large heavy equipment to reach the construction site in mountainous areas[5]. In this paper, simulation technology is studied against the background of integral tower technology based on airship.

With the continuous development of China’s economy and the acceleration of urbanization, the phenomenon of high-quality cultivated land being converted to construction land is becoming increasingly prominent. In mountainous provinces such as Yunnan, the contradiction between cultivated land protection and blind expansion of construction land is becoming increasingly obvious. Based on the characteristic region of the mountainous province of Yunnan, this paper integrates remote sensing image interpretation of land use/land cover data in three phases (i.e., 2000, 2010, and 2020) with GIS technology and econometric methods. Through the interpretation of remote sensing images from 3 phases of Yunnan Province, a detailed calculation was conducted on the per capita cultivated land area (CULA) and per capita construction land area (COLA) and their changes in 129 counties in the province over the past 20 years (2000~2020). The spatiotemporal evolution laws and spatial pattern characteristics of CULA and COLA were analyzed, and then, the influencing factors in the quantitative characteristics of cultivated land and construction land in the province were studied further by using spatial econometric models. This study finds that the total and per capita CULA in Yunnan Province have significantly decreased over the past 20 years, which poses a threat to the national food security to a certain extent. At the same time, the total amounts of COLA and the per capita COLA have significantly increased, leading to the phenomenon of blind expansion and rough utilization of construction land. Compared with international research results, Yunnan can learn many lessons about controlling the reduction in CULA and the rapid expansion of COLA, among which the most important thing is to choose suitable urban and industrial development paths and adopt effective intensive land utilization methods. The research results of this study can provide a basic reference for mountainous provinces to formulate reasonable measures for cultivated land protection, prevent the disorderly expansion of construction land, and promote the coordinated development of urban and rural areas.