Spatial Geographic Information Research Articles

Addressing incomplete tile phenomena in image tiling: Introducing the grid six-intersection model

Abstract With the continuous development of Earth Observation technology, resolution of imagery and gridded data has significantly increased, leading to a rapid increase in data volume. To efficiently acquire and analyze these vast amounts of imagery and gridded data, image tiling technology has been developed to effectively access data of interested areas. Tiling technology divides large-scale image data into smaller tiles, providing fast, accurate, and efficient access support for imagery and gridded data. The spatial grid model, as the foundational framework of the new generation of geographic spatial information, plays a critical role in the retrieval, integration, services, and applications of imagery and gridded data resources. When tiling image data based on the spatial grid model, it always generates both complete and incomplete tiles. Particularly, when conducting image tile retrieval using the same rule-based grid in the retrieval area, incomplete tile phenomena along the boundary regions of the retrieved images often occur, resulting in gaps within the retrieval area. To tackle this issue, in this study, we present a new topological model called the Grid Six-Intersection Model (G-6IM), specifically designed for regular rectangular grids, to accurately represent boundary issues in image tiling. Through a practical case study, we demonstrate the effectiveness and practical application potential of the proposed G-6IM model, providing new insights and guidance for the improvement and optimization of imagery and gridded data tiling technology.

Research on power generation prediction of hydropower in river basin based on multi-head attention graph convolutional neural network

For the power generation prediction of traditional hydropower stations, most of them only use time series prediction and neglect to study the spatial topological relationship of hydropower stations in the river basin, so that it is difficult to fully explore the characteristic relationship of space power stations. In this paper, a research method for power generation prediction of hydropower in river basin hydropower stations based on multi-head attention map convolutional neural network is proposed. This method establishes a first-level node neighborhood feature map based on the spatial geographic distribution information of hydropower stations in the basin, and uses the method of graph convolution to carry out node feature mining and feature learning, so as to transform the power generation capacity evaluation problem of the hydropower station in the basin into the node prediction problem in the graph, which is different from the global normalization rule. the multi-head attention mechanism introduced further improves the information aggregation quality of the graph node, and uses the historical temperature, power generation, electricity price, unit status and other data of each hydropower station in the basin for training. The reasoning results show that the proposed method achieves higher accuracy than other schemes, and the power prediction method is conducive to the formulation of power plans of hydropower stations in the basin, and can also play a positive role in guiding the site selection of hydropower plants.

A method for hierarchical weighted fitting of regular grid DSM with discrete points

Abstract. A Digital Surface Model (DSM) is a crucial spatial geographic information data used to describe the shape of the earth’s surface in Geographic Information Systems (GIS). DSM is the core data used in terrain analysis in GIS. A regular grid DSM is generally generated by interpolating a large number of discrete point clouds. This paper proposes a method of using a hierarchical weighted strategy to fit a regular grid DSM with discrete points. This method uses a pyramid hierarchical strategy to refine the target regular grid from one grid with finer parameters of 3*3, until the nth level (the interval of the grid is equal to the expected interval), and then gradually places the discrete point cloud into the corresponding grid by weighted averaging, and uses the result of this level as the initial value of the next level. This algorithm can avoid the problem of low efficiency in retrieving a large number of discrete point clouds, and the indirect interpolation method not considering the contribution of distant neighboring point clouds. The operation of point cloud data is a stream operation, which does not require consideration of the topological information of point clouds, and has simple operation and no additional memory consumption. It is especially suitable for the production of regular grid DSM with massive point clouds. To verify the effectiveness of this method, the article selected six typical terrain data such as high mountains, mountains, hills, plains, urban areas, and lakes for experiments. The results show that compared with the construct-TIN method for producing DSM, this method has very good processing accuracy and processing efficiency.

Advancing mortality rate prediction in European population clusters: integrating deep learning and multiscale analysis

Accurately predicting population mortality rates is crucial for effective retirement insurance and economic policy formulation. Recent advancements in deep learning time series forecasting (DLTSF) have led to improved mortality rate predictions compared to traditional models like Lee-Carter (LC). This study focuses on mortality rate prediction in large clusters across Europe. By utilizing PCA dimensionality reduction and statistical clustering techniques, we integrate age features from high-dimensional mortality data of multiple countries, analyzing their similarities and differences. To capture the heterogeneous characteristics, an adaptive adjustment matrix is generated, incorporating sequential variation and spatial geographical information. Additionally, a combination of graph neural networks and a transformer network with an adaptive adjustment matrix is employed to capture the spatiotemporal features between different clusters. Extensive numerical experiments using data from the Human Mortality Database validate the superiority of the proposed GT-A model over traditional LC models and other classic neural networks in terms of prediction accuracy. Consequently, the GT-A model serves as a powerful forecasting tool for global population studies and the international life insurance field.

Spatial-temporal deep learning model based on Similarity Principle for dock shared bicycles ridership prediction

Demand prediction plays a critical role in traffic research. The key challenge of traffic demand prediction lies in modeling the complex spatial dependencies and temporal dynamics. However, there is no mature and widely accepted concept to support the solution of the above challenge. Essentially, a prediction model combined with similar objects in temporal and spatial dimensions could obtain better performance. This paper proposes a concept called the Similarity-based Principle (SP), which is applied to improve the prediction performance of deep learning models in complex traffic scenarios. For the temporal components, the long-term temporal dynamics in contemporaneous historical data for ridership are extracted by the Stacked Autoencoder (SAE) method. For the spatial components, the activity-based spatial geographic information (ABG-information) is used to capture the spatial correlation of the traffic network, which is reflected in the daily activities of humans. Specifically, the SP is applied to a Spatio-temporal Graph Convolutional Neural Network (STGCNN) model. In the case study, the Similarity-based Principle Spatio-temporal Graph Convolutional Neural Network (SP-STGCNN) model predicts demand for bicycle sharing in San Francisco. The results show that the SP effectively improves the model's performance. The prediction accuracy is enhanced by up to 10.34% compared with STGCNN. For spatial relationships, the model using the geographic information attribute performs better than that using the road information attribute and the distance attribute. It is proved that the construction of the Spatio-temporal model-based similarity principle can improve the performance.

Lightweight Multilevel Feature-Fusion Network for Built-Up Area Mapping from Gaofen-2 Satellite Images

The timely, accurate acquisition of geographic spatial information such as the location, scope, and distribution of built-up areas is of great importance for urban planning, management, and decision-making. Due to the diversity of target features and the complexity of spatial layouts, the large-scale mapping of urban built-up areas using high-resolution (HR) satellite imagery still faces considerable challenges. To address this issue, this study adopted a block-based processing strategy and constructed a lightweight multilevel feature-fusion (FF) convolutional neural network for the feature representation and discrimination of built-up areas in HR images. The proposed network consists of three feature extraction modules composed of lightweight convolutions to extract features at different levels, which are further fused sequentially through two attention-based FF modules. Furthermore, to improve the problem of incorrect discrimination and severe jagged boundaries caused by block-based processing, a majority voting method based on a grid offset is adopted to achieve a refined extraction of built-up areas. The effectiveness of this method is evaluated using Gaofen-2 satellite image data covering Shenzhen, China. Compared with several state-of-the-art algorithms for detecting built-up areas, the proposed method achieves a higher detection accuracy and preserves better shape integrity and boundary smoothness in the extracted results.

Resilient critical infrastructures: An innovative methodological perspective for critical infrastructure (CI) integrated assessment models by inducing digital technologies during multi-hazard incidents

Over the last decade, the notion of community resilience, which encompasses planning for, opposing, absorbing, and quickly recovering from disruptive occurrences, has gained momentum across the world. Critical Infrastructures (CI) are seen as critical to attaining success in today's densely populated countries. Such infrastructures must be robust in the face of multi-hazard catastrophes by implementing appropriate disaster management and recovery plans. Given these facts, it is critical to establish a new methodological perspective with an integrated system for effective disaster management of CI, as well as an intelligent application that will aid in the construction of more resilient and sustainable cities and communities. This perspective proposes a holistic gaming scenario application for assessing the vulnerability and accessibility of critical infrastructures during multi-hazard events, with a primary focus on conducting an integrated assessment for critical infrastructures and their assets. Mainly, the perspective includes a holistic gaming scenario application that will aid in accurately quantifying geographical spatial information and integrating big data into predictive and prescriptive management tools using virtual reality.•Conducting Integrated Assessment Models for evaluating vulnerability of Critical Infrastructures.•Inducing Digital Technologies during Multi-Hazard Incidents for improving Natural hazard assessment models.•Developing an open-world gaming scenario that is considered with high visual motion pictures and scenes.

Serviceability evaluation of highway tunnels based on data mining and machine learning: A case study of continental United States

The appraisal of a tunnel service condition usually takes into account the lining deformations and defects of the lining structure, with the assessment technique heavily rely on the expertise of the evaluation. In this study, based on the US National Tunnel Inventory (NTI) database, a TOPSIS-CRITIC evaluation system was established to comprehensively evaluate the service performance of tunnels during the operation period from three categories of structural, traffic & civil, and non-structural components, resulting in the Tunnel Service Performance Index (TSPI). Then, a K-means clustering method was used to unevenly divide the TSPI of tunnels into service levels I ∼ IV. After consulting with experts, 23 important features were selected from 88 basic characteristics of tunnels, and further refined this list to 19 key factors using Spearman correlation analysis. These features were grouped into five categories: traffic, geological, space & time, planning & construction, and operational. Due to the imbalance of categories, SMOTE oversampling method was employed for data augmentation. Further, a TPE Bayesian optimized LightGBM classifier was proposed to predict the service level of tunnels and compared with nine common machine learning models (LR, KNN, SVM, DT, RF, GBDT, XGB, ET and MLP). After comprehensive experiments and numerical testing, the TPE-LGBM classifier achieved the highest overall performance among the ten models. Lastly, the SHapley Additive exPlanations (SHAP) interpretability model in conjunction with GIS spatial geographic information was employed to conduct an in-depth analysis of how full life cycle features impact tunnel service performance during operation.

Research on Real-time and High-precision Positioning Method of Ground Target through UAV Stereo Vision and Spatial Information Fusion

Background: Positioning accuracy is the most important index of the reconnaissance positioning system. Positioning accuracy involves many factors, such as the position, attitude and motion state of the flight platform, the pointing accuracy of the stable platform, and various coordinate transformations. A reasonable fusion strategy can guarantee stable positioning accuracy, so it is very important to study the precise fusion of machine vision, 3D geographic data and UAV airborne positioning information to realize the optimal combination of positioning data and complete the accurate and rapid positioning of the ground target. Methods: In this paper, a location model based on stereo vision and spatial information fusion method is proposed. It fully integrates visual information, satellite positioning information and spatial geographic information, greatly improves positioning accuracy, and through a real-time processing algorithm, significantly improves real-time positioning. Results: Through the related experiments, positioning accuracy and real-time ability of positioning could reach about three meters. Conclusion: The proposed real-time and high-precision positioning method of the ground target through UAV stereo vision and spatial information fusion is proposed showing significant improvement compared to other traditional methods.

A spatial analysis of an effective path for low-carbon development of energy-intensive industries

Reducing the carbon intensity of the energy-intensive heavy industry is the key to achieving low-carbon development. The existing literature often ignore the important role of spatial effects, when investigating the heavy industry. The innovation of this paper is to incorporate important spatial geographic information into the analysis framework, and use the geographically weighted regression model to investigate the carbon intensity of the heavy industry in China. The findings display that: (1) environmental decentralization generates a greater impact in the eastern region, on account of the more investment in energy conservation and environmental protection. (2) The impact of urbanization on the carbon intensity in the central region is the most obvious, because this region invests more scientific and technological talents. (3) Green technology innovation contributes more to reducing carbon intensity in the central region, due to the faster growth of renewable energy patented technologies. (4) The industrial structure has little impact in the central and western regions, because their tertiary industry is underdeveloped. (5) The carbon intensity of the western region receives the least impact from foreign direct investment, since the area attracts the least foreign investment. Therefore, the local governments ought to make targeted policies according to local specific conditions, in order to realize the low-carbon development of the heavy industry.

A Sample Enhancement Method Based on Simple Linear Iterative Clustering Superpixel Segmentation Applied to Multibeam Seabed Classification

Full-coverage and high-efficiency seabed sediment detection and identification are critical elements of digital marine construction that support the three-dimensional and thematic development of maritime spatial geographic information systems. With the development of multibeam echo sounder (MBES), the use of MBES backscatter intensity and bathymetry data to extract backscatter angular response (AR) features has increased. Using backscatter intensity features and seabed terrain features for classification is an effective way to achieve large-scale seabed sediment classification. However, it was still limited by small sample size problems and the poor stability of the classification model due to the complexity of performing seabed sediment sampling. In response to the above issues, this paper proposes a sample enhancement method based on simple linear iterative clustering (SLIC) superpixel segmentation to address the problems. First, a superpixel-based sample homogeneity expansion method is combined with multibeam backscatter intensity images to achieve adaptive sample range selection. Then, a Random Forest (RF)-based model is constructed using MBES backscatter intensity and seabed terrain features. To assess the model’s validity, the experiment uses data from an extensive MBES survey and field sampling information from the Celtic Sea, UK. It achieves accurate predictions for the area’s eight sediment types. The experimental results show that the proposed method expands 60 groups of original sample points to 9293 groups of valid sample points, and achieves an overall classification accuracy and kappa coefficient of 84.05% and 0.81 for the seabed sediment, respectively. In addition, the classification accuracy is significantly better compared to the traditional methods.

EXPERIENCE OF OBTAINING INITIAL DATA FROM GOOGLE EARTH TO BUILD A DIGITAL TERRAIN MODEL

The Google Earth system is widely available, which allows to collect geographic spatial information both on a commercial basis and for own needs. Geodetic measurements are accompanied by measurement errors, they are divided into rough, systematic and random. Systematic errors always distort the measurement result in any direction. Systematic errors are tried to be eliminated by introducing amendments. The analysis of publications shows that the question of obtaining data from the Google Earth system has interested many researchers. Some came to the conclusion that it makes no sense to use geospatial height data, but the use of 2-D data requires research. The relevance is substantiated. The purpose of this article is to confirm the hypothesis that the Google Earth system provides precisely the systematic errors in finding distances so that by introducing linear corrections it is possible to increase the accuracy of linear measurements in this system. The order of the experiment: 1) to take several places (territories) located in different parts of the country; 2) in the Google Earth, to find objects with clear contours near the experimenter's location; 2) to measure distances using the Google Earth; 3) to take screenshots of the measured areas; 4) to measure the distance with a tape measure; 5) to calculate the difference; 6) to repeat the experiment in another part of the territory; 8) to calculate the arithmetic mean (using MS Excel); 9) to calculate the standard deviation (using MS Excel). Three territories located in the northern part of eastern Ukraine were considered. 10 experiments were carried out in each territory. The hypothesis (that the Google Earth system gives systematic errors in finding distances) has been proven. Now, through the introduction of linear corrections, it is possible to increase the accuracy of linear measurements in this system. Keywords: Google Earth, systematic measurement errors, digital model of the situation, distance measurement.

Spatial distribution characteristics and accessibility analysis of beautiful leisure villages in China.

This study was designed to evaluate the spatial distribution characteristics of 1432 beautiful leisure villages in China using econometric geography and spatial geographic information system analysis methods, such as nearest distance index, K index, and nuclear density. We also used the grid cost weighted distance algorithm to determine the spatial accessibility of beautiful leisure villages and the overall accessibility of county units. In addition, our evaluations determined the spatial differences in county accessibility using exploratory spatial data analysis (ESDA). Our results showed that the spatial distribution of the beautiful leisure villages in China could be best described using the cohesion type classification and that there were large differences in their distribution between provinces and economic regions. The average accessibility time of beautiful leisure villages was 197.24 min with only 57.19% of these commutes being less than 2 h, and only 17.88% being less than 30 min. The area with the longest accessibility time was located on the Qinghai Tibet Plateau, at up to 1510.03 min. The spatial distribution of accessibility showed obvious traffic directivity producing a positive Moran I value for most counties. There was also a significant positive correlation between the accessibility of beautiful leisure villages and their adjacent areas, and clear patterns of hot spots–sub-hot spots–sub-cold spots—cold spots from east to west. The overall service scope of beautiful leisure villages was characterized by west > east > middle, with topography, population, economy, and location acting as the major factors in the spatial distribution of these beautiful leisure villages in China.

Agricultural Internet of Things Application in the Construction of Regional Smart Cities

Smart city originated from the field of media, which refers to the use of various information technologies or innovative concepts to connect and integrate urban systems and services. It improves the efficiency of resource utilization, optimizes urban management and services, and improves the quality of life of citizens. A smart city is a new generation of information integration technologies using the internet of things, cloud computing, big data, and spatial geographic information integration. It is a new theory and new model to promote urban planning, construction, management, and service intelligence. The IoT technology originated from the third technological revolution. It refers to the connection of objects to the network through the information sensor equipment according to the agreed protocol. It communicates and circulates information through the information dissemination medium during the connection process to realize intelligent identification, tracking, supervision, and other functions. The internet of things is the internet where everything is connected. It is an extension and expansion of the internet based on the network. It is a huge network formed by combining various information sensing devices with the internet. In fact, people, machines, and things are interconnected at any time and at any place. Agricultural internet of things refers to the internet of things that participates in automatic control through various instruments and equipment as parameters of automatic control. Its essence is to use RFID, wireless data communication, and other technologies to construct an “internet of things” covering everything in the world on the basis of the computer internet. It realizes the automatic identification of items and the interconnection and sharing of information through the computer internet. This study aims to study the application of the agricultural internet of things in the construction of regional smart cities. It expects to promote the development of modern agriculture by improving the level of agricultural management with the help of smart city and internet of things technology. Based on the B/S system model, this study uses the related technologies of the internet of things to build a smart agriculture platform. By comparing the data in the database and entering the smart agriculture platform to monitor the data, it can achieve efficient and convenient management. It proposes the framework structure and system characteristics of the facility agricultural internet of things technology system. It also investigates and analyzes related products according to the system framework structure, and summarizes the current status of product development and the functions that can be achieved. According to the survey, 56 people believed that there was no after-sale service in the whole survey process. It accounts for 32.5% of the total surveyed persons, indicating the lack of local modern agricultural management. Thirty-one people are willing to buy agricultural products online, accounting for 18% of the total surveyed, indicating that the development of local online shopping is relatively backward.

Adaptive Dual-View WaveNet for urban spatial–temporal event prediction

Spatial–temporal event prediction is a particular task for multivariate time series forecasting. Therefore, the complex entangled dynamics of space and time need to be considered. This task is an essential but crucial loop in future smart cities construction, which can be widely applied in urban traffic management, disaster monitoring and mobility analysis. In recent years, video-like spatial–temporal modelling has been the most common approach in many deep learning models. However, the video-like modelling approach cannot consider some latent region-wise correlations other than geographic spatial distance information. To overcome the limitation, we propose a novel neural network framework, Adaptive Dual-View WaveNet (ADVW-Net), for the urban spatial–temporal event prediction. By integrating the spatial representations from Convolutional Neural Network (CNN) and that from adaptive Graph convolutional neural network (GCN), our proposed model can capture not only the geographic correlations but also some latent region-wise dependencies from the input data. In addition, the effective architecture, WaveNet, can be transferred to region-wise spatial–temporal prediction scenarios for long-range temporal dependencies learning. Experimental results on three urban datasets demonstrate the superior performance of our proposed model.

Prediction of rockhead using a hybrid N-XGBoost machine learning framework

The spatial information of rockhead is crucial for the design and construction of tunneling or underground excavation. Although the conventional site investigation methods (i.e. borehole drilling) could provide local engineering geological information, the accurate prediction of the rockhead position with limited borehole data is still challenging due to its spatial variation and great uncertainties involved. With the development of computer science, machine learning (ML) has been proved to be a promising way to avoid subjective judgments by human beings and to establish complex relationships with mega data automatically. However, few studies have been reported on the adoption of ML models for the prediction of the rockhead position. In this paper, we proposed a robust probabilistic ML model for predicting the rockhead distribution using the spatial geographic information. The framework of the natural gradient boosting (NGBoost) algorithm combined with the extreme gradient boosting (XGBoost) is used as the basic learner. The XGBoost model was also compared with some other ML models such as the gradient boosting regression tree (GBRT), the light gradient boosting machine (LightGBM), the multivariate linear regression (MLR), the artificial neural network (ANN), and the support vector machine (SVM). The results demonstrate that the XGBoost algorithm, the core algorithm of the probabilistic N-XGBoost model, outperformed the other conventional ML models with a coefficient of determination ( R 2 ) of 0.89 and a root mean squared error (RMSE) of 5.8 m for the prediction of rockhead position based on limited borehole data. The probabilistic N-XGBoost model not only achieved a higher prediction accuracy, but also provided a predictive estimation of the uncertainty. Thus, the proposed N-XGBoost probabilistic model has the potential to be used as a reliable and effective ML algorithm for the prediction of rockhead position in rock and geotechnical engineering.

Development of an anthropogenic atmospheric mercury emissions inventory in Thailand in 2018

Mercury (Hg) is a highly toxic pollutant characterized by its long-range transport in the atmosphere. In the present study, the atmospheric Hg emission inventory in Thailand was developed using a bottom-up approach. To reflect the emitting behaviors of various Hg sources in Thailand in 2018, activity data specific to individual sources were collected, and emission factors were obtained from local sources or adapted from the literature with proper adjustments. It was found that about 4,337.8 kg of anthropogenic Hg was emitted: 52 % from biomass open burning and 40 % from combustion sources. Mercury speciation was also investigated. Hg0 was found to be dominant at 73 %, followed by Hg2+ at 18 % and HgP at 9 %. Maps were produced using the Spatial Geographic Information System, which showed that the emissions were relatively dense in the Central region of Thailand where most industrial activities take place and in the Northern and Northwestern regions as a result of biomass open burning. Hg emissions were also observed to mainly take place during the early months of the year, particularly in March and April. These two months were determined to account for 47 % of the total annual emissions of Hg, mostly due to increased combustion activities and biomass open burning. The output of the present work is a Hg emission database that can serve as model to investigate the status of Hg and the risks it poses to human health and ecosystems as well as support policy makers in developing emission control strategies.

Semantic Segmentation of Satellite Images: A Deep Learning Approach Integrated with Geospatial Hash Codes

Satellite images are always partitioned into regular patches with smaller sizes and then individually fed into deep neural networks (DNNs) for semantic segmentation. The underlying assumption is that these images are independent of one another in terms of geographic spatial information. However, it is well known that many land-cover or land-use categories share common regional characteristics within a certain spatial scale. For example, the style of buildings may change from one city or country to another. In this paper, we explore some deep learning approaches integrated with geospatial hash codes to improve the semantic segmentation results of satellite images. Specifically, the geographic coordinates of satellite images are encoded into a string of binary codes using the geohash method. Then, the binary codes of the geographic coordinates are fed into the deep neural network using three different methods in order to enhance the semantic segmentation ability of the deep neural network for satellite images. Experiments on three datasets demonstrate the effectiveness of embedding geographic coordinates into the neural networks. Our method yields a significant improvement over previous methods that do not use geospatial information.

Introducing an Open-Source Regional Water Quality Data Viewer Tool to Support Research Data Access

Water quality data collection, storage, and access is a difficult task and significant work has gone into methods to store and disseminate these data. We present a tool to disseminate research in a simple method that does not replace but extends and leverages these tools. The tool is not geo-graphically limited and works with any spatially-referenced data. In most regions, government agencies maintain central repositories for water quality data. In the United States, the federal government maintains two systems to fill that role for hydrological data: the U.S. Geological Survey (USGS) National Water Information System (NWIS) and the U.S. Environmental Protection Agency (EPA) Storage and Retrieval System (STORET), since superseded by the Water Quality Portal (WQP). The Consortium of the Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) has developed the Hydrologic Information System (HIS) to standardize the search and discovery of these data as well as other observational time series datasets. Additionally, CUAHSI developed and maintains HydroShare.org (5 May 2021) as a web portal for researchers to store and share hydrology data in a variety of formats including spatial geographic information system data. We present the Tethys Platform based Water Quality Data Viewer (WQDV) web application that uses these systems to provide researchers and local monitoring organizations with a simple method to archive, view, analyze, and distribute water quality data. WQDV provides an archive for non-official or preliminary research data and access to those data that have been collected but need to be distributed prior to review or inclusion in the state database. WQDV can also accept subsets of data downloaded from other sources, such as the EPA WQP. WQDV helps users understand what local data are available and how they relate to the data in larger databases. WQDV presents data in spatial (maps) and temporal (time series graphs) forms to help the users analyze and potentially screen the data sources before export for additional analysis. WQDV provides a convenient method for interim data to be widely disseminated and easily accessible in the context of a subset of official data. We present WQDV using a case study of data from Utah Lake, Utah, United States of America.

Research on Integrated Management Platform of Smart Park Based on CIM

At present, the construction of smart cities and smart parks in China has been widely concerned, and the intelligent decision-making in the process of urban planning, design, construction and management is in urgent need of refined, visualized, dynamic and integrated expression of urban entity information. Therefore, this paper studies a technical theory of digital description and expression of cities, namely CIM (City Information Model). Our team independently innovates, developing smart park management platforms. This platform is based on BIM (Building Information Model), GIS, IOT (Internet of Things) and other new generation information technologies. BIM mainly studies parametric information inside buildings, while GIS focuses on external spatial geographic information. IOT Internet of Things technology realizes the transmission and processing between information and data. This platform integrates and upgrades these technologies to build a smart park platform. This paper explores the presentation and current situation of CIM theory and technology under the background of smart city construction, smart park construction and operation and maintenance management.