Limitations of single sensor and single cyanobacterial extraction method for long time series monitoring of cyanobacterial blooms in Taihu Lake, based on the multi-source image data of GF-1 and Landsat 8 from 2014 to 2023, the normalised vegetation index (NDVI) method, random forest (RF) method and random forest based on maximum interclass variance (Otsu-RF) method are used to extract cyanobacteria from Taihu Lake.Determination of the optimal cyanobacterial extraction method by comparative analysis the spatial and temporal characteristics of cyanobacterial blooms in Taihu Lake over the past ten years are revealed. The results show that:① The Otsu-RF method has the highest accuracy in extracting cyanobacterial blooms in different images, and it can better extract the sporadically distributed cyanobacteria; ② Compared with the GF-1 images, the texture of cyanobacterial pixels on the Landsat 8 fusion images is clearer, and the results of cyanobacterial bloom extraction are more accurate; ③ The intensity of cyanobacterial bloom outbreaks was higher in summer and fall and weaker in spring and winter in Taihu Lake from 2014 to 2023, of which the outbreaks were particularly severe in 2017 and 2020, with the annual average area of cyanobacteria in the whole area exceeding 300 km²;④ The cyanobacterial blooms of Taihu Lake in spring, summer, and fall were mostly found in the Zhushan Lake, Meiliang Lake, and the shore of the western Taihu Lake areas, and they occurred more often in the shore of the the southern Taihu Lake areas in winter.

The treatment of black-odor water bodies is an important task in implementing the rural revitalization strategy. However, their distribution is extensive, widespread, and shows seasonal variation, leading to unclear baseline figures and periodic odors, thus becoming a difficult point for governance and supervision. In this paper, we use GF-2 as the data source and adopt the DeepLabv3+ semantic segmentation method to extract fine water body in rural areas. We analyze the spectral curve features of black-odor water bodies and normal water bodies, and build various models to identify black-odor water bodies. We conduct precision verification in Xinxiang County, Luohe City, and Xiangcheng City,and the results show that the NDBWI model has high accuracy and universality. We use this model to dynamically monitor black-odor water bodies in Xinxiang County from 2022 to 2024. The study found that the number of black-odor water bodies in 2023 is 60% less than that in the previous year, and there is a slight rebound of 6.8% in 2024, indicating that the treatment of black-odor water bodies treatment in Xinxiang County has achieved significant results in the past three years. The research results can provide effective support for precise management of rural black-odor water bodies and the promotion of rural living environment improvement.

Forest land is the largest carbon pool in the terrestrial ecosystem. In order to solve the problem that the existing forest land field investigation methods are relatively backward and inefficient, this paper takes the core area of Changsha-Zhuzhou-Xiangtan Greenheart Central Park as the research area, and evaluates the vegetation carbon storage, soil carbon storage and their spatial distribution characteristics of the forest ecosystem in the core area in 2022 by using multiple techniques such as sample plot investigation, model simulation and remote sensing. The results show as follows:①The total carbon storage of forest ecosystem in the core area in 2022 is 1.22×10⁶MgC, and the average carbon density is 97.29 MgC/hm².②The forest vegetation carbon reserve in the core area is 6.72×10⁵MgC, and the average vegetation carbon density is 53.47 MgC/hm²,with the spatial distribution pattern of high in the west and low in the east.③The carbon storage of 0～20 cm surface soil of forest land in the core area is 5.50×10⁵MgC, and the average soil carbon density is 43.82 MgC/hm²,mainly distributing in Zhaoshan town and the southwest of Tiaoma town. This paper can provide technical reference for the background investigation of ecosystem carbon stocks and carbon sink monitoring, and provide scientific basis for realizing the goal of carbon peak and carbon neutrality on schedule.

Vegetation restoration is the key to the control of rocky desertification in Karst areas of southwest China. The fractional vegetation cover quantifies the dense degree of vegetation and can measure the status of surface vegetation, which is helpful to further explore the trend of vegetation restoration in rocky desertification areas. This paper takes Minfeng-Dayuan small watershed in western Guizhou as the research area. Based on the image data of ZY-3 satellite, this paper uses the pixel binary model, image difference method, coefficient of variation and transfer matrix analysis to analyze the spatio-temporal evolution characteristics, change trend and stability of fractional vegetation cover in 2016 and 2021. The results show that: ① During the past five years, the area of extremely high and high vegetation cover increased significantly in the study area, leading to the improvement of the comprehensive vegetation coverage of the whole region. The large-scale conversion from low vegetation cover to high vegetation cover indicates the change from non-vegetation covered surface types such as bare rock and gravel land to vegetation covered surface types such as grassland, shrubs, and even forest land in the study area. It further shows the progress achieved in the control of rocky desertification in this area.② During the five years, the overall change of vegetation cover in the study area showed an improvement trend, and the proportion of the improved area was slightly larger than that of the degraded area, and the overall vegetation cover was relatively stable. In the degraded areas, the slight degradation is slightly greater than the slight improvement, which is caused by the expansion of urbanization and the rapid economic development. Some forest land and cultivated land are converted into construction land, and the vegetation coverage in the construction land area is reduced.

The weak spectral response of trace elements such as soil heavy metals makes feature band extraction challenging,leading to lower accuracy in inversion models. To address these issues,this study intends to use a new approach that extracts spectral response prior knowledge from class-standard soil samples. Firstly,a total of 226 surface(0～20 cm) soil samples were collected from farmland surrounding a zinc smelting plant,with 53 samples used to prepare class-standard soil samples, and both soil zinc content and reflectance spectra were measured. Then,continuous wavelet transform (CWT) was employed to reconstruct the spectral reflectance,and the transfer component analysis (TCA) algorithm was applied to transfer the prior knowledge from class-standard soil samples to ZY-1-02D image spectra. Finally,quantitative inversion models for Zn content were fitted using random forest (RF) and extreme learning machine (ELM),and the optimal inversion model was determined based on accuracy evaluation. The results indicate that transfer learning can achieve spectral response enhancement and improve model accuracy.The paper provides a reference for the study of retrieving relevant soil parameters using prior knowledge.

The Dongdongting Lake Ouchi River East Branch Delta is the fastest developed beach in Dongting Lake in past 100 years. In order to analyze the erosion and deposition process of the Ouchi River East Branch Delta, predict its future development trend, and provide data support for the utilization of the East Dongting Lake beach and water and sediment management, this article uses remote sensing technology and combines years of water and sediment data to conduct a spatiotemporal analysis of the erosion and deposition of the Ouchi River East Branch Delta from 1967 to 2023 in past 55 years from three aspects: beach area, river channel changes, and vegetation changes. Research has shown that the 1960s to 1980s were a period of rapid expansion in the area of the eastern branch of the Ouchi River delta, with an average annual growth rate of 6.48 km². In the 1990s, the growth rate slowed down, with an average annual increase of 0.94 km². After the operation of the Three Gorges Reservoir in 2003, the average annual increase in area decreased to 0.13 km². Due to the influence of the hydrological environment inside the lake, the river channels in the delta first extend northeast and then turn southwest to form an “inverted U”-shaped reverse erosion and deposition. Sediments accumulate along both sides of the river channel, forming a slope shaped sandbar with high near the river channel and low far from it. Since 2003, the area of reeds on the sandbar has increased while the area of lake grass has decreased, indicating that the expansion of the sandbar area is tending to stop, but siltation is still continuing.

To clarifying the recovery mechanism of alpine grassland actual net primary productivity on the Qinghai-Xizang Plateau,this study firstly analyses the spatial, temporal and vertical changes of actual net primary productivity, then evaluate the relative contributions of climate change and anthropogenic activities to actual net primary productivity by multi-source data fusion,and finally combined the structural equation model to further explain the recovery mechanism. The results show that actual net primary productivity increased significantly from 2001 to 2020, and is concentrated in the altitude range of 3000～4000 m; 83.44% of the actual net primary productivity of grassland increased in the past 20 years, of which 71.29% is dominated by anthropogenic activities; Temperature and soil nutrients are the most important direct effects on the actual net primary productivity, and the meteorological factors also participated in soil nutrient cycling by regulating soil pH, which in turn promoted the increase of actual net primary productivity. The results provide the scientific basis for the scientific use and protection of alpine grassland on the Qinghai-Xizang Plateau.

The Kubuqi Desert has a large span in the east and west, and the natural environment is obviously different. In order to clarify whether the dominant driving factors of desertification in the east and west are consistent, this paper uses six Landsat TM/OLI satellite images covering the Kubuqi Desert from 2000 to 2023 to extract the degree of desertification in the Kubuqi Desert, and explains its changing rules and dominant driving factors through landscape pattern changes and geographical factor detectors.The results showed that the desertification degree of Kubuqi Desert showed a decreasing trend in time from 2000 to 2023, and the spatial distribution trend was “high in the west and low in the east”. The desertification in Jungar Banner, Dalad Banner and Hangjin Banner was improved. The boundary of extremely severe and mild desertification patches tends to be smooth and regular, and the boundary of severe, moderate and non-desertification patches tends to be complex and irregular. The dominant landscape is shifting from severe and extremely severe desertification to mild and non-desertification.Human factors have a great influence on the desertification of the whole Kubuqi Desert, among which land use type, precipitation, wind speed and livestock number are the dominant factors affecting desertification in this area. The dominant driving factors of Dalad Banner and Hangjin Banner are consistent with the whole region. Within the scope of Jungar Banner, land use type, precipitation, relative humidity and livestock number are the dominant driving factors.

Study the surface velocity and velocity distribution characteristics of the Purog Kangri Glacier from September to October in 2023. Based on two Sentinel-1 radar satellite images, the pixel offset tracking technique is used to extract surface velocity information of Purog Kangri Glacier Glacier and four typical glaciers and draw a glacier velocity field. The pixel offset tracking technique obtains the horizontal pixel offset of the corresponding pixels in the two SAR images by accurately registering the two images, thereby obtaining glacier surface velocity. The surface velocity and velocity distribution characteristics of Purog Kangri Glacier are analyzed based on the glacier velocity field. The results show that the surface velocity of Purog Kangri Glacier is generally slow, with an average velocity of about 0.05 m/d. The average flow velocity of the four typical glaciers in the northwest, northeast, central and southwest of Purog Kangri Glacier is about 0.20, 0.19, 0.15 and 0.04 m/d, respectively. The study found that the flow velocity characteristics of glaciers with different spatial distribution in Purog Kangri Glacier are also different, mainly manifested as the northeastern typical glacier have a faster flow velocity than the southwestern typical glacier.

Aiming at the problems of polarimetric synthetic aperture radar (PolSAR) image water and land segmentation with non-smooth and complete edges, and poor segmentation results in small water areas, this paper proposes a PolSAR image segmentation method based on multi-feature support vector machines (SVM) algorithm. Firstly, seven polarization scattering features of a pixel are extracted by Cloude and Yamaguchi target decomposition method, and eight-dimension feature vector of the pixel is constructed by combining with its Sentinel-1 dual-polarized water index (SDWI). All the pixels of the PolSAR image are traversed and obtain the feature set of the PolSAR image. Then, the training dataset are constituted by the feature and class sets of the training samples to train the SVM classifier, which is utilized to realize the water and land segmentation for PolSAR images. Finally, the water and land segmentation experiments of PolSAR images are carried out using the proposed and comparison methods. The experimental results show that the proposed method can realize the water and land segmentation better, the mean values of Kappa coefficients and total accuracy are 0.979 3 and 98.98%, respectively.

Three-dimensional ground penetrating radar (GPR) technology is a critical method for detecting latent road defects. Analyzing GPR data with intelligent defect recognition algorithms can enhance detection efficiency. However, existing algorithms have not fully utilized the multi-view information provided by GPR, resulting in lower recognition accuracy. To address this issue, this paper proposes a GPR multi-view fusion model for subgrade defect identification. The model employs a dual-channel parallel structure, utilizing the MobileViT network to extract high-dimensional features from both B-scan and C-scan images of GPR data, thereby tapping into the complementary information inherent in the dual views for feature learning. To effectively integrate information from these dual views, a feature interlaced weighted fusion method is proposed, arranging the high-dimensional features extracted from the dual views in proximity and dynamically optimizing the weight allocation of feature channels to highlight key features while suppressing irrelevant information. Experimental results demonstrate that the network model achieves an accuracy rate of 90.5% on the test set. Moreover, under Gaussian white noise interference, compared with baseline models, the overall degradation index of the model decreases by 13.51%, showcasing superior robustness.

With the increasing demand for green energy, the photovoltaic panel infrastructure in the highway area has become an important way to develop renewable energy. As an important part of the highway, the photovoltaic power generation of toll stations and service areas has also been paid attention to. This paper studies the technical method of using deep learning method to identify the information of photovoltaic panels at tollbooths and service areas in the highway road domain through high-resolution remote sensing images. Taking Jiangsu province as the research experimental area, Google 19 remote sensing image data of the whole province are downloaded. By making samples, the existing classical semantic segmentation networks HRNet, ResNet, FCN and U-Net are used to extract information from the experimental area, and the photovoltaic panel information extraction results are obtained. Ablation experiments confirm that the HRNet semantic segmentation network combined with CBAM attention mechanism proposed in this paper has the best extraction effect. This method provides technical support for the intelligent monitoring and management of photovoltaic panels in toll stations and service areas of expressways.

With the development of society, the demand for remote sensing data is increasing, and the shooting tasks that satellites need to complete are also increasing. Limited by the number of satellites and imaging capabilities, there will be a certain trade-off when shooting. Therefore, the definition proposed in this paper affects the characteristics of satellite observation effectiveness evaluation, generates a comprehensive evaluation model of observation effectiveness, and comprehensively forms observation effectiveness evaluation scores, and then makes reasonable and scientific decisions. Firstly, four description factors are generated for observation effectiveness evaluation. In order to make them comparable and normalize them, the genetic algorithm is used to iteratively select the optimal solution, so as to realize the evaluation of observation tasks. In this paper, the shooting tasks of ZY-3 02 satellite in 2022—2023 and ZY-3 03 satellite in 2022 are selected to verify this method. The experimental results show that the model has good adaptability and can scientifically reflect the comprehensive performance of multi-task observation.

Aiming at the problems of blur and distortion of glass curtain wall in urban 3D model, this paper proposes a 3D glass curtain wall segmentation method based on projection and feature activation. The method firstly extracts building facade information by layers using point cloud feature. Then the glass facade is projected onto the aerial image using a projection based segmentation method. Finally, the feature activation module is nested on U-Net network structure to achieve accurate extraction of glass curtain wall. By comparing the proposed method with U-Net and U²-Net models, the new method proposed in this paper can not only extract clear glass region boundaries, but also effectively reduce the influence of oblique aerial images on small glass regions and improve segmentation performance, which is of great significance for realizing low-cost fine modeling.

The ASIFT algorithm is an image feature extraction algorithm with complete affine invariance. It handles affine distortions in images by simulating variations in the camera optical axis, demonstrating strong robustness in images matching with large perspective changes.In order to study the influence of different tilt images on the feature matching of ASIFT algorithm in oblique aerial photography,this paper combines the two affine transformation parameters defined in the ASIFT algorithm: absolute tilt t and transition tilt τ. A method is proposed to calculate the absolute tilt of a single image and the tilt of multi-view images in oblique photography. By conducting ASIFT matching experiments under various tilt conditions, the paper analyzes and summarizes the effects of different tilt angles on feature matching,and validate the effectiveness of the multi-perspective image tilt calculation method.

The multi-constellation global navigation satellite system precise point positioning(GNSS PPP) technology has been extensively and deeply applied in the field of time transfer. In environments such as time comparison in time keeping laboratories and mobile time comparison tests at fixed sites, the precise coordinates of the receiver's antenna can be measured in advance. When performing GNSS PPP calculations under these conditions, the known high-precision coordinates can be used to accelerate the convergence time of the time transfer result. A method is proposed that uses the known 3D precise coordinates as the initial parameters of the Kalman filter. The corresponding system uncertainty, the elements of the Kalman filter covariance matrix, is set to small values. This reduces the impact of 3D coordinate calculation on the overall convergence process. The results show that, with known precise coordinates, the proposed method improved the convergence speed by approximately 30%～50% compared to traditional GNSS PPP time comparison methods. The accuracy and stability of the time comparison after convergence are comparable to traditional GNSS PPP. The RMS can reach the nanosecond level, and the stability can reach 1E-13 per hour.

To address the non-line-of-sight (NLOS) errors caused by people moving in indoor positioning using UWB, as well as the issue of immovable base stations after training conventional neural networks, this paper proposes an Chan algorithm based on bidirectional long short-term memory neural networks (BiLSTM-Chan). This algorithm processes UWB timing data through a bidirectional LSTM to provide error correction values for UWB timing data, and then calculates the final 3D coordinates using the Chan method. The bidirectional LSTM is capable of synthesizing past and future information, capturing the characteristics in the timing data more effectively. Integrating attention mechanisms into the network can aid in analyzing key features extracted by the BiLSTM layer, making the neural network's predictions more accurate. This paper compares the proposed method through simulation experiments and real-world experiments with the BiLSTM algorithm, Chan algorithm, and the LS algorithm. The real-world experiments demonstrate that compared to the BiLSTM, Chan, and LS algorithms, the accuracy of BiLSTM-Chan algorithm respectively improved by 30.66%, 61.78%, and 61.96%.

Mercury retrograde is an astronomical phenomenon caused by the relative motion of mercury and earth. Accurately describing and predicting the “mercury retrograde” phenomenon is of great significance for the calibration of astronomical observation instruments, the selection of launch and operation task time windows, etc. In this paper, based on high-precision ephemeris data (DE440), the position of mercury is obtained, and the trajectory of mercury in the equatorial inertial coordinate system relative to earth is simulated and modeled from 2024 to 2044. The retrograde time of mercury is calculated based on the rate of change of right ascension, and the spatial and temporal distribution characteristics of the retrograde trajectory are explored. The results show that the number of times that the “mercury retrograde” phenomenon occurs in different years is different, with 3～4 occurrences per year.The average duration of “mercury retrograde” is about 23 days (with a fluctuation range of 20～26 days), and the average interval between two consecutive retrograde periods is about 95 days (with a fluctuation range of 89～103 days).There is a similarity in the retrograde trajectories of mercury in different years, especially in the years that are 13 years apart (such as 2024 and 2037).In addition, there will be a retrograde of mercury that spans two calendar years on average every 4～5 years.

Scene reconstruction of hutong-style blocks plays a vital role in promoting and preserving the cultural heritage of China's historic streets. However, the unique characteristics of hutong-style neighborhoods, such as their narrow and elongated layouts, significant variations in imaging distances, and differences in resolution, result in suboptimal reconstruction outcomes. To address these challenges, this paper proposes a 3D reconstruction algorithm for narrow blocks based on neural radiation field (NeRF), capable of reconstructing high-quality scenes under the condition of outdoor panoramic images. To solve the problem of uneven panoramic image, the algorithm introduces a distortion-aware ray sampling method, which adjusts the sampling frequency to enhance model learning quality. Additionally, to address the narrowness of scenes in the hutong area, a direction-sensitive color-depth-structure joint loss function is constructed to improve the adaptability of the model to the narrow block and thereby enhancing reconstruction quality. Experimental results demonstrate that, compared to baseline methods that use distortion-aware ray sampling only, the proposed approach achieves higher peak signal-to-noise ratio (PSNR) values and delivers high-quality novel view synthesis.

With the intensification of China's aging society, the demand for elderly care services is growing, especially in mega-cities like Shanghai. This study takes the Caoyangxincun neighborhood in Putuo district, Shanghai, as a case study to explore how to optimize the spatial layout of elderly care facilities to meet the needs of the community's elderly population under the guidance of the“15-minute community life circle”concept. Based on the principle of supply and demand matching, the study analyzes elderly population data, the distribution of elderly care facilities, and their service coverage radii to assess the accessibility and supply-demand matching of community elderly care facilities. The results show that although the distribution of elderly care facilities in Caoyangxincun neighborhood is generally uniform and relatively accessible, there are still issues of unbalanced supply and demand. This paper proposes targeted suggestions for the optimization of elderly care facility layout and points out the room for improvement in research methods and future research directions.

Rail damage refers to various states that occur during the use of rails, such as breakage, cracks, and other conditions that affect and limit the performance of rail use. Conducting damage detection on railway tracks is a necessary method for maintaining railway transportation safety. This paper proposes a rail crack detection method based on an improved YOLOv5 model to address the problems of current railway maintenance relying mostly on manual visual inspection and poor real-time fault detection. Meanwhile, in response to the shortcomings of existing neural network models in identifying rail damage and inspired by image change detection, a rail change detection method based on scale invariant feature transformation (SIFT) is proposed to achieve rail damage recognition. Experiments have shown that the improved YOLO model has a 4.6% increase in average mean accuracy compared to the original model, and has good application prospects. The SIFT based algorithm has high detection accuracy and can effectively identify rail damage areas, meeting practical engineering needs.

With the continuous expansion of unmanned aerial vehicle(UAV) and digital twin technologies in engineering applications, new opportunities are provided to enhance engineering monitoring and management levels. Addressing the serious threats posed by adverse conditions in high-slope projects to engineering safety and the surrounding environment, this paper proposes a dynamic feedback method for slope construction safety based on UAV and digital twin technology, aiming to improve the safety and efficiency of slope engineering. Firstly, the method and steps for UAV to collect virtual multi-view digital images of slope engineering are provided through on-site testing. Slope image data is collected, and the SFM-MVS algorithm is used to construct a 3D digital model of the slope engineering.Next, by utilizing a voxel-based spatial geometric analysis algorithm, the excavation width and depth of the slope are dynamically calculated.Then, a point cloud slicing subdivision model is employed, and a structural surface orientation recognition algorithm that considers the spatial extension of the slope outcrop is used to obtain geometric parameters of the structural surface. Finally these data are mapped onto the digital model to establish a digital twin model corresponding to the actual slope.Through continuous data collection, analysis, and simulation, immediate assessment and feedback on slope stability changes caused by construction activities are conducted. The results show that this research method can enhance the timeliness and accuracy of slope stability analysis, providing new prevention and response mechanisms for slope engineering.

Dynamic pose measurement is the key technology of intelligent operation of cantilever roadheader. Aiming at the characteristics of high dust scene in tunnel operation, a method of dynamic pose measurement by pulling wire is proposed. In this method, six fixing points on the car body are connected with the fixing points in the tunnel by six pull wire devices, and the three-dimensional position and orientation information of the car body is obtained by measuring the distance of the pull wire between the fixing points. An iterative method based on singular value decomposition of matrix is proposed to solve the 6-DOF multiplicity of vehicle body pose. Simulation results show that the method meets the accuracy requirements of the position and attitude measurement of the tunnel cantilever tunneling machine, and provides a reference method for high-precision dynamic position and orientation measurement of large targets in special environments.

Monocular visual settlement monitoring represents a novel non-contact monitoring approach. Nevertheless, challenges arise from intricate backgrounds and stringent benchmark target installation prerequisites, occasionally impeding the efficacy of this method for settlement monitoring. This paper introduces a non-contact monocular vision method for high-precision automatic settlement monitoring. Initially, the measurement target is positioned within the designated monitoring area, and target identification is achieved utilizing the YOLOv10s target detection algorithm. Subsequently, the edge-based least squares ellipse fitting technique is employed to determine the pixel coordinates of the target's center point. Following this, leveraging the principles of camera imaging, a derivation process is conducted to establish the world coordinates corresponding to the pixel coordinates of the target's center point using an enhanced world coordinate computation approach. Ultimately, the actual displacement value is computed with the initial frame image of the monitored area serving as the reference. Through outdoor simulation settlement experiments, we compare the monocular visual calculated value with the electronic level measurement value, using their absolute error as the evaluation metric. By varying the distance between the camera and the target, we assess the monitoring accuracy of the proposed method. At a distance of 5 m, the maximum absolute error is 2.784 mm, and the minimum is 0.246 mm. When the distance is increased to 10 m, the maximum absolute error rose to 4.071 mm, with a minimum of 0.42 mm. The experimental results demonstrate that as the distance between the target and the camera increases, the accuracy decreases. However, within a distance range of up to 10 m and a settlement value of 250 mm, the average absolute error of the method stands at 1.543 mm.

In response to the common problem of projection deformation exceeding the limit in expressway plane control survey,this paper analyzs the causes and characteristics of projection deformation in the plane control network of east-west long span expressways.It proposes a method for determining the projection control threshold and this method has been test that it is convenient to determine the effective range of the compensatory projection plane.Two parameters are defined for the optimal compensatory projection plane.Furthermore,a method for quickly determining the optimal solution of the compensating projection plane is proposed.It designs a technical route for determining the optimal solution for compensating projection surfaces.The maximum projection deformation of the optimal compensatory projection surface determined by the proposed method is 17.7 millimeter per kilometer and the control effect of the projection deformation of the optimal compensatory projection surface on the reference ellipsoid surface is between 10.4~14.4 millimeter per kilometer based on the specific engineering example dataspecific data.It is shown that the optimal compensatory projection plane has a significant effect on controlling projection deformation compared to the limit of 25 millimeter per kilometer.The research results of this paper have certain practical value in the projection deformation control of expressway control networks.

The Ministry of Natural Resources proposes that the national, provincial, and municipal levels should collaborate to undertake the development of 3D realistic geospatial scene of China, create fundamental geo-entity data products, and establish a unified spatial positioning framework and analytical foundation for Digital China. Large-scale, multi-type, multi-scale and multi-granularity geo-entity modeling is a key technical problem in the 3D realistic geospatial scene of Chinese technology system. Based on the practice of basic surveying and mapping in China, this paper proposes a technical approach for engineering and large-scale modeling of national fundamental geo-entity, which involves clarifying the concept connotation, constructing the content system, designing the data model, and formulating the technical specifications. It studies and designs key technologies such as the concept connotation, content and classification, scale and granularity, and data model of fundamental geo-entity, and introduces three main technical methods for engineering implementation.

Based on geographic information spatial analysis and mathematical and statistical methods, this study takes a district in the south of Beijing as the research object, relies on data from nine types of underground pipelines, including water supply, reclaimed water, stormwater, sewage, gas, heating, electricity, telecommunications, and broadcasting, to analyze the spatial planning and design characteristics of these pipelines. The study finds that: ① According to the Pearson correlation coefficient, the underground pipelines of various specializations in the district exhibit a spatial distribution pattern of “sparser in the south and denser in the north, radiating outward from the center.” ②Based on the global and local Moran's I and Getis-Ord Gi^* indices, the pipelines in the district form two clusters of “high and low” along a northwest-southeast axis and one area with a significant dispersion trend. The distribution of cold and hot spots is largely consistent with the spatial clustering pattern, indicating a spatial dependency. ③The pipeline coverage in urban streets is significantly higher than in southern townships, showing a pronounced polarization. This study can provide important spatial analysis foundations and empirical data support for the management and planning of underground pipelines in the district, offering reference and guidance for enhancing the sustainable use of underground space, ensuring high-quality development of underground spaces, and optimizing infrastructure layout.

This article elaborates on the background and overall framework of the construction of the digital twin spatial base in Ningbo city,proposes a multi-sensor based multi-source heterogeneous data fusion model,integrates over 300 types of data,builds an integrated spatial base of above ground and underground, land and sea areas, indoor and outdoor and builds a city digital twin center.The high rendering technology using UE+streaming architecture achieves application empowerment, improves the flexibility and convenience of urban management.

Aiming at the construction and operation problems of the high-precision reference stations of the BeiDou Navigation Satellite System in GDCORS, an integrated BeiDou high-precision service station design scheme based on domestic board cards is proposed. The service station consists of a host and a pole. The integrated structure design is convenient for installation and maintenance, and can independently receive the signal of the BeiDou Navigation Satellite System. It has an independent power supply system and adopts a dual hot backup system, single BeiDou positioning, commercial cryptography and other designs to improve the reliability, fault tolerance and security application of the system. Through zero-baseline analysis of the observation data of the service station host and the comparative receiver for precise point positioning (PPP), the results show that the single BeiDou positioning mode precision of the service station host is equivalent to the positioning precision of the GPS system, which meets the design requirements. The research results can be applied to the construction of additional reference stations in the eastern, western and northern mountainous areas of Guangdong as well as in coastal and island areas, effectively reducing the construction cost and operation difficulty, and improving the ability of the system to run continuously and stably.

The urban space is gradually moving from the ground to the synergistic development of the ground and underground and from two-dimensional to three-dimensional visual management, which puts forward a new demand and high level of data collection, processing, analysis, and application of the underground space. The fusion of 3D models has always been a hotspot of scholars' research, and higher requirements have been put forward for the aesthetics of the models. In this paper, we propose a feasible technical route to construct a fine 3D model of urban underground space, fusing the 3D model constructed in pieces with high-precision and rendering the model surface with cloud rendering technology, to satisfy the data fusion and display requirements of underground space, to provide data support for the drawing of the “natural resources 3D three-dimensional map”,and to promote the exploitation and utilization of underground space and promote the sustainable development of the city. It promotes the development and utilization of underground space and the sustainable development of the city.