The current live 3D building model data volume is huge, which brings challenges to model storage and real-time loading display. However, the existing research mainly focuses on the monolithicization, lightweighting and visualization of the original building model, which is difficult to meet the needs of urban spatial analysis and management under different application scenarios. For this reason, this paper proposes a multi-dimensional monolithic model construction method for building solid models. The method is based on OSGB data generated by inclined photography modeling, and cuts out the monolithic model of the building by using the vector contour of the building, then simplifies the building model by using the quadic error metrics (QEM) algorithm, and determines the geometrical parameters and morphological features of the building models of different simplification levels by comparing the different simplification levels. By comparing the geometric parameters and morphological features of building models of different simplification levels, the degree of simplification between building models of different dimensions is determined, and the construction of building monolithic models of different dimensions is finally realized. The experimental results show that the multi-dimensional monolithic construction of the 3D building model meets the requirements of level of details (LOD) in city geography markup language (CityGML), and compares favorably with the traditional building information modeling (BIM),it has a higher degree of automation.The method can complete the conversion between building models with limited data, and can construct 3D building model monoliths with different dimensions and apply them to different levels and scenarios to meet the needs of urban spatial analysis and management.

To address the suboptimal performance of existing point cloud-based indoor 3D reconstruction methods when handling large-scale point cloud with significant density variations, we propose an improved indoor scene 3D Mesh model reconstruction method based on the POCO deep neural network, featuring enhanced training and reconstruction strategies. Firstly, we improve the training strategy by fine-tuning the original model using a small amount of simulated scene data and a very limited amount of real scene data. Secondly, we enhance the reconstruction strategy by introducing farthest point sampling and a strategy to ensure consistent bounding box scales. Finally, we restore the scale of the reconstructed 3D Mesh model. Experimental results demonstrate that our method outperforms the original POCO model in terms of reconstruction accuracy and model completeness, providing support for China's national 3D mapping program (3dRGLM).

Geo-entity of 3D real scene of China represents a significant advancement in the new fundamental surveying and mapping, introducing new technical requirements and challenges for quality inspection through innovations in concepts, technologies, products, and organizational models. This paper presents a method of constructing the automated quality inspection model for the fundamental geo-entity data, adopting the technology of SQLite database indexing and BeiDou grid location codes to establish the entity middleware inspection method, and the quality inspection rule engine is constructed. The prototype system is developed and the feasibility of the quality inspection model through application examples is validated.

Based on the difficulties in data acquisition,low accuracy,and lack of three-dimensional information in the process of ecological restoration in abandoned mines,this paper proposes a set of dynamic supervision methods for ecological restoration in abandoned mines based on real-life 3D scenarios. Firstly,UAV oblique photogrammetry is carried out for abandoned mines,and after processing,the DEM and DOM are obtained,and a realistic 3D model is established. Secondly,based on key technologies such as earthwork calculation,change detection,and supervised classification,ecological restoration parameters are extracted. Finally,using real-life 3D models and ecological restoration parameters,effectively monitor the engineering quantity and compliance of geological environment protection,terrain reshaping,vegetation reconstruction,and land reuse projects. The results indicate that based on realistic 3D,it is possible to timely obtain the changes in abandoned mines,assist in formulating more refined restoration plans,effectively monitor the effectiveness of ecological restoration,and update realistic 3D data,which is of great significance for realizing the application value of realistic 3D.

With the deepening of the construction goal of “Real 3D China”, the demand for component level real 3D products is increasing day by day. Starting from the top-level design, this study investigates the full process construction ideas and rapid construction methods of component level real 3D products, represented by high-precision road maps. It elaborates on the key technologies of the entity, semantics, and 3D features of high-precision road map products, constructs road component level realistic 3D products with “one code polymorphism” features, and verifies the feasibility of this technical architecture using a 5-kilometer road section in the South High tech Zone of Chengdu as the research area. The research results indicate that the final high-precision road map component products meet the specifications and design requirements in terms of accuracy, integrity, consistency, etc., which to a certain extent improves production efficiency and provides a reference basis for the development and construction of component level real 3D in other cities.

As an important part of hydrometry, river discharge monitoring plays an irreplaceable role in water resources management and scheduling, flood prevention and disaster reduction. Spatio-temporal image velocimetry (STIV) has the advantages of simple principle, high real-time and security. However, the measurement method also has certain limitations, such as the flow and velocity is small, and the tracing is not obvious, the spatio-temporal image can not produce an effective texture structure, which greatly affects the detection of main direction of the texture, thereby affecting the accuracy of the algorithm. In order to solve the above problems, in this paper, combination of inter-frame difference and the local Fourier maximum angle analysis is proposed, which uses the inter-frame difference method to calculate the pixel difference between two consecutive frames of images, captures and identifies the minor changes of the river, and superimposes the kinematic saliency map with the original video to generate spatio-temporal image (STI), and uses the method of local Fourier maximum angle analysis to calculate the main direction of the texture of the spatio-temporal image, which improves the robustness and accuracy of the spatio-temporal image velocimetry algorithm. In this paper, the results are calculated by using the video filmed in the river compared with the measurement results of the propeller current meter, which show that in the case of small velocity and river discharge and insufficient tracer, the accuracy of this paper's method in the calculation of the average velocity and river discharge has been significantly improved, the stability and real-time performance of the velocity measurement is good.

To address the problem of overgrowing regions in dynamic object detection using LiDAR range images, an optimized dynamic object detection method for urban environments is proposed in this paper. This method performs fast segmentation and rotation of 3D point cloud space in 2D range images, ensuring accurate and efficient growth of the correct object region without relying on the object model. Additionally, a classification characteristic analysis for dynamic objects in scene flow detection is conducted. The issues of missed detection of dynamic objects and false detection of excessively long objects are resolved through object clustering and object region growth. This significantly improves the detection precision and recall rate while maintaining good computational efficiency. Compared to algorithms that directly detect dynamic objects in 3D point clouds, this algorithm achieves an average frame calculation time of 1/11, with a 12.67% increase in precision and a 1.51% increase in recall rate.

The fusion of airborne laser point clouds and multi-spectral images is of great importance for applications in remote sensing image processing, environmental monitoring and urban planning. Aiming at the low efficiency and robustness of the existing fusion methods, this paper proposes a non-rigid probabilistic matching-based deep fusion method for laser point clouds and multi-spectral images. Line-CNN deep learning network is used to extract straight line segment features, which are sampled as 2D scatter points, and non-rigid CPD algorithm is utilized to match discrete points in different scale images, and then the airborne laser point cloud with multi-spectral information is obtained by fusing two modal images with high accuracy through the improved single response matrix. To comprehensively validate the performance of the method, airborne laser point clouds and multi-spectral images from a variety of scenarios are used as experimental data in this paper. Experiments show that the accuracy of discrete point matching under multiple complex scenes is as high as 90%, the fused image can well retain the features and information of the original image, the fusion correlation coefficient under multiple scenes is as high as 90% or more, and the algorithm is more efficient, which is conducive to the subsequent applications such as vegetation monitoring, environmental monitoring, and land analysis.

Giant deep-seated landslides are sudden and highly hazardous, making it difficult to grasp their spatial dynamics through ground surveys or single remote sensing methods. Taking the Nuole landslide as an example, this study employs multiple remote sensing technologies such as airborne LiDAR, optical remote sensing, and small baseline subset InSAR (SBAS-InSAR) for landslide interpretation. Initially, using LiDAR data, shaded relief maps are generated to interpret the development of the landslide, revealing three terraces, 29 cracks, 4 collapse areas, and 4 newly formed sliding areas. By analyzing optical images from different periods and DEM data, the horizontal displacement of typical feature points is explored to reveal the spatiotemporal evolution of planar deformation over an 11-year period. It is discovered that the lower part of the landslide area exhibits active deformation, with a maximum horizontal displacement of 6.2 meters. SBAS-InSAR technology is used to obtain line-of-sight (LOS) deformation from ascending and descending orbits between January 2019 and May 2023. Furthermore, slope direction data is introduced to decompose the LOS displacement results into two dimensions, revealing the true deformation of the landslide. The maximum vertical deformation is -650 mm, and the maximum horizontal deformation along the slope direction is 500 mm, indicating that the landslide has been in a long-term active state. Additionally, the study found that erosion affects the front edge of the landslide, while surface water infiltration affects the rear edge, resulting in deformation primarily along the slope and vertical directions, consistent with the mechanism of traction-induced landslide deformation. This research provides important references for identifying and studying the development mechanism of traction-induced landslides.

The accurate extraction of surface water information is of great significance for water resources research. In this paper, using the Sentinel-2 image for research data, we propose the improvement of the SVM water extraction algorithm by principal component analysis (PCA), random forest (RF) and support vector machine(SVM).Firstly, the dimension of the original band is reduced by PCA and the composition of gray level co-occurrence matrix (GLCM) texture and wavelet texture are calculated by using the moving window. Then, the original spectral data is used for feature optimization based on RF. Finally, the optimal texture calculation window is selected and the lake water is extracted based on the SVM algorithm. Results on the surface, the overall accuracy of this method is higher than that of other methods, and the overall accuracy and Kappa coefficient are 98.87% and 98.49% respectively, and the water information is more complete.

Poyang Lake as the study area and utilizes satellite altimetry data from GF-1 and ICESat-2. Based on the extraction of lake water surface area, a relationship model between lake water level and area is constructed using ICESat-2 altimetry data to estimate water volume changes in Poyang Lake from 2020 to 2023. It further analyzes the temporal variations in water volume and the primary influencing factors within the study area. The research results indicate that Poyang Lake experiences significant seasonal variations in water surface area, water level, and water volume, with the highest water storage in summer and the lowest in winter. Taking 2020 as an example, the maximum water volume change in Poyang Lake reached 15.6 km³ in August, while it decreased by 1.44 km³ from January to March of the same year, resulting in a maximum intra-annual difference of 17.04 km³. Over the past three years, Poyang Lake's water volume has shown a decreasing trend, with a decrease of 1.43 km³ from March 2020 to March 2023. Correlation analysis between water volume changes in Poyang Lake and regional precipitation and temperature reveals that precipitation is the primary meteorological factor influencing water volume changes in Poyang Lake, with a coefficient of determination (R²) of 0.78 between the two.

Aiming at environmental geological disasters like surface deformation caused by large-scale coal mining, this paper reviews the current research status of InSAR technology in coal mine surface deformation monitoring. The main issues with current InSAR technology include: severe phase decorrelation affects monitoring results; limited methods exist for accuracy verification; the accuracy of three-dimensional surface deformation monitoring in mining areas is restricted; and data analysis is insufficient. To address these issues, the paper proposes: ① Use SAR satellites with long wavelengths and short temporal baselines to reduce decorrelation. ② Strengthen DS-InSAR research for more precise monitoring. ③ Integrate multi-source remote sensing data to enhance accuracy. ④ Combine InSAR data with AI to support green and sustainable development in coal mines.

Rapid metro expansion is accompanied by frequent construction accidents, making deformation monitoring and interpretation crucial for ensuring construction safety. This paper optimized MT-InSAR for improving the coherence of construction areas according to the subway construction characteristics. Moreover, the InSAR and on-site measurements are combined to supplement the spatiotemporal and angular gaps of subsidence. The deformation process of the construction area of Shapu Station on Shenzhen Metro Line 12 is interpreted, and the deformation results are finally used for multi-parameter settlement risk assessment. The results show that: ① The number of points and coherence in the Shapu construction area are significantly improved; ② Combined analysis of groundwater level and leveling measurements reveals that excavation of foundation pits and tunnel lead to uneven settlement of surrounding structures by 10～30 mm, with three settlement funnels identified; ③ Combined analysis with machine vision monitoring indicates that the main structural deformation after backfilling is due to the load of backfill soil, with an average compression of approximately 4.31 mm in the backfill layer; ④ The deformation risk is higher along the sides of the tunnel and foundation pit, as well as in the 37～42 ring segments of the station.

The geological environment of the high-altitude cold mine slope is fragile,and the stability of the mine slope is easy to be destroyed under the action of frost,swelling,thawing and sinking.In order to obtain accurate 3D deformation characteristics under the condition of repeated freezing and thawing in high-altitude cold mines,and according to the features of slope materials sliding to the direction of maximum topographic drop under the action of gravity,this paper selects a certain slope in Jiama mine area in Tibet as the research object,takes slope direction constraint as prior condition,and constructs the 3D deformation model by jointing SBAS-InSAR.The results show that within more than two years the maximum cumulative sinking amount of the mine in east-west direction is -78 mm,the maximum cumulative sinking amount in vertical direction is -38 mm,and the maximum cumulative sinking amount in north-south direction is -25 mm.The main deformation occurs below the mining area,and the open-pit mining area is relatively stable.Through cutting out the mine profile line to analyse time-space evolution of sliding slope,it can be found that displacement gradually decreases with depth increase,and frost,swelling,thawing and sinking only occur on the surface of active layer.3D deformation analyses show that frost,swelling,thawing,and sinking and concentrated rainfall induced by temperature change have greater influence on the deformation of the slope in vertical and east-west directions,while the influence in north-south direction is relatively small.The calculation results of this paper are reliable,which provides some reference value for large-scale remote sensing slope monitoring in high-altitude cold mine areas.

To address the problems of large computation and low rendering efficiency in real-time rendering of the interior spaces of large-scale building information models, this paper proposes an adaptive perspective map generation method for lightweight visualization. The method reconstructs the solid parts of indoor scenes by integrating the spatial and semantic information of part divisions. It also designs a projection plane calculation method with visibility constraints to determine the texture projection plane, which better generates the plane texture map of each division space and maintains the three-dimensional sense of furniture parts. Finally, the plane textures are stitched together to obtain a perspective map similar to the original model interior. Through a design experiment, the real-time rendering efficiency and visualization effect of the perspective mapping were compared with the original model in the interior landscape of the building. The results show that the proposed method can achieve a visual experience similar to the original model while significantly reducing resource consumption, and can provide effective technical support for lightweight visualization and high-fidelity rendering of city-level BIM data.

Soil organic matter (SOM) is an important soil health and fertility indicator. Accurate estimation of SOM is crucial for sustainable agricultural development. Hyperspectral technology combined with machine learning provides the possibility for large-scale and non-destructive monitoring of SOM. However, existing studies have not fully explored the physical significance of spectral positions within the sensitive spectral range of soil active materials and the spectral effects of agricultural straw return activities. Therefore, this study proposed an innovative Pearson correlation coefficient and spectral position joint optimization algorithm (PAS) to solve this problem. The experimental results show that: In the PAS algorithm, the spectral position to correlation coefficient ratio is 3∶7, which can effectively improve the model performance.The characteristic bands near 1260 nm are a typical site of straw organic components. The characteristic bands near 1300 nm are a typical site of interaction between 1260 nm and SOM. The characteristic bands near 1320 nm are affected by 1260 nm, SOM, and 1340 nm. This study demonstrates the important influence of the spectral effect of agricultural straw return activities on SOM estimation and the approach in this study to potentially quantify the carbon sequestration effect of straw return activities.

With the increasing frequency and scale of extreme weather events in recent years, it is particularly important to utilize advanced technologies to enhance the accuracy and efficiency of mapping and geographic information systems, so as to improve the resilience of power systems in outdoor open-air conventional transmission and substation substations. In this paper, a multi-source spatial data fusion method based on self-attentive transform network is proposed for enhancing the monitoring and prediction capability of changes in complex geographic environments and natural resources. The method represents continuous weather radar echo data as a spatio-temporal sequence, captures long term time dependencies using a self-attentive mechanism encoder, and integrates multi-scale convolution to extract short term time dependencies. In addition, a graph attention network is introduced into the transformation network to deeply analyze the relationship between different spatial variables. To verify the effectiveness of the model, this paper conducts experiments based on precipitation data and remote sensing data from 2019 to 2022 in a region, and compares it with traditional convolutional neural networks, benchmark U-Net and long-short term memory networks. The experimental results show that the method is superior to the traditional model in terms of measurement accuracy and data fusion ability, especially in the fields of GIS construction, engineering survey, mine survey, cadastral mapping and ocean survey and mapping, etc., which has a wide application potential.

The existing indoor/outdoor navigation context perception models based on machine learning and smart phones have the problems with only utilizing GNSS signals and poor perception accuracy. With the promotion of China's independent and controllable audio positioning technology, new available signals have been provided for that. In this paper, 12 GNSS and audio signal features on the mobile phone are selected, then an indoor/outdoor navigation context perception model based on whale optimization algorithm (WOA) and random forest (RF) is designed. The result shows that compared with only using audio signal features and GNSS signal features, the accuracy of context perception is significantly improved. Compared with the five traditional methods of back propagation neural network (BPNN), convolutional neural network (CNN), support vector machine (SVM), long short term memory (LSTM), and RF, the results of the proposed method are the best, accuracy, precision, recall rate and F1 are all exceeding 96%. The calculation speed of the proposed algorithm is basically equivalent to that of traditional RF.

Aiming at the problem that the satellite data quality of Android smartphones is poor, some satellite observation data are only single-frequency data, and the traditional carrier phase smoothing pseudoranging algorithm is susceptible to the ionospheric delay leading to filter dispersion, a single/dual-frequency mixed carrier phase smoothing pseudoranging (MF-Hatch) algorithm is proposed. The method makes comprehensive use of the mixed data on the basis of Hetch filtering, adopts the ionosphere-free combination method for the dual-frequency data to eliminate the ionosphere delay, smoothes the combination pseudorange and the single-frequency pseudorange with the moving open window carrier phase smoothing pseudorange method, and avoids the dispersion of the filter due to the effects of the ionosphere anomaly and the circumferential hopping by setting a threshold value. The experiment proves that, relative to the result of directly using raw data for localization, the MF-Hatch algorithm improves the E, N, and U upward localization accuracy by 33.62%, 18.14%, and 26.78%, respectively, in single-point static localization, and improves the E, N, and U upward localization accuracy by 23.14%, 28.69%, and 16.91%, respectively, in dynamic localization in an urban environment.

In the realm of essential natural resources such as “mountains, rivers, forests, fields, lakes, grass, and sands,”conventional management and monitoring approaches often suffer from inefficiencies and lagging oversight. Moreover, these methodologies are prone to neglecting concealed hazard zones, thereby confronting an incontrovertible risk in monitoring. Against this backdrop, tower-mounted video surveillance is deemed to possess a comprehensive perspective and a heightened advantage in remote observation. Endowed with features such as real-time and high-definition capabilities, it emerges as a pivotal means for achieving intelligent perception. Particularly noteworthy is the automatic positioning technology within serving as the core of video technology, which necessitates extensive applications. Overcoming challenges associated with automatic positioning technology not only proves instrumental in significantly enhancing supervisory efficiency and reducing human intervention but also serves to transcend climatic impacts. Consequently, its application prospects span across various industries. Taking the case of tower-mounted video surveillance in Hunan province as an illustrative example, this paper proposes a video geo-localization method based on orthorectified remote sensing image. This method enables the accurate conversion of coordinates between tower-mounted video images and orthophoto images, effectively addressing the difficulty in precise positioning of natural resource regulation. The positioning accuracy of the plain area within 2 km of the video is better than 1 m, and the positioning accuracy of the hilly area with lees relief is better than 8 m.The proposed video geo-localization method outlined in this paper provides a feasible solution for the widespread application of tower-mounted video surveillance.

Anji Hynobius National Nature Reserve is a nature reserve of forest and wild animals, with rich biodiversity. There are several rare and endangered tree species distributed in the territory, such as witch hazel, liriodendron, fragrant fruit tree, Jinqian pine, southern Yew and so on. In recent years, as biodiversity conservation has been widely concerned and valued around the world, conservation monitoring and habitat analysis of important tree species in protected areas are also being intensified. Habitat suitability detection of important tree species is the basis and premise of tree species protection. Compared with the traditional survey methods of biodiversity, remote sensing can greatly improve the detection efficiency and prediction accuracy in the application of spatial quantitative analysis of habitat suitability. In this study, remote sensing and geographic information technology is used to calibrate and extract the distribution location and region of target tree species, and the maximum entropy model is used to analyze the suitable habitat of tree species, so as to determine the habitat preference of various tree species and predict their potential distribution range. Through ground survey and UAV verification, the average accuracy of tree species extraction can reach more than 75%. The results of suitable habitat prediction analysis provide important support for the exploration of potential distribution of important tree species, and provide a guarantee for reasonable and efficient protection of endangered tree species and spatio-temporal dynamic monitoring.

For the problem of inaccurate analysis of land subsidence caused by single monitoring method, the “air-surface-underground” monitoring system of land subsidence is adopted with platform and parameter collaboration in this paper. Then a fusion model for aerial and surface measurements is constructed and solved with the least squares method, to obtain high-precision subsidence data. Finally, these methods of visualization and spatiotemporal statistics are used to integrate air/surface and underground data to obtain the causes of subsidence. These techniques are applied to analyze the local area in Tianjin, and a three-dimensional monitoring system for land subsidence is constructed, combing InSAR, leveling, and layered markers. These indexes of maximum error and mean square error are used to elevate the fusion result, with 20% increase of mean square error. The conclusions that groundwater exploitation is the main cause of severe subsidence from both qualitative and quantitative analysis. It is suggested that the method proposed in this article are effective in improving the monitoring reliability of land subsidence, achieving accurate subsidence causes. The results are expected to provide a valuable reference for comprehensive prevention and control of land subsidence.

In order to accurately achieve the deformation of the surface displacements of Zhengzhou city, the DS-InSAR technology fusing with high coherence points and distributed targets is employed with 50 Sentinel-1A images acquired from January 2019 to December 2023. And thus the annual ground displacements rate and the cumulative displacements are obtained in the main urban districts. The results indicate that the increasing districts are mainly located in Zhengzhou central area, especially in extracting groundwater in prohibited areas, whereas the settlement regions are distributed in eastern regions and Zhongmu county. And then the deformation in typical area is further analyzed, which shows that the serious sinking regions are located in Baisha town, Zhongmu county, in which the maximum subsidence rate is up to 20.0 mm/a. Furthermore, other sinking districts are scattered over the areas in various villages and towns. The investigation in this paper provides reference for the urban planning and construction of Zhengzhou.

Focusing on the problem of surface deformation detection ability of L-SAR data in densely vegetated mountainous areas,taking the junction of Zhejiang,Jiangxi and Anhui provinces as the study area,based on the L-SAR data and high-resolution multispectral data,and with the negative correlation between vegetation coverage and interference coherence as the a priori condition,the Pearson's correlation coefficient method is adopted to obtain the sample data for coherence estimation,and the sample data are analyzed and evaluated under different vegetation coverage by means of statistical analysis and fitting. Pearson's correlation coefficient method is used to obtain the sample data for coherence estimation,and then analyzes and evaluates the interferometric capability of L-SAR data under different vegetation cover by means of statistical analysis and fitting. The study shows that:① There is a strong Logistic regression between L-SAR interference coherence and vegetation cover,in which the interference coherence coefficient decreases the fastest when the vegetation cover is from 0.2 to 0.4.② The interference effect of L-SAR data is good and stable under low and medium-low vegetation cover; the interference effect is relatively good but less stable under medium vegetation cover; the interference effect is unsatisfactory in the region with high vegetation cover. In the area of high vegetation cover,the interference effect is unsatisfactory.

With the increasing complexity of natural resource management, polygon assisted identification has become a key technology to improve the efficiency and accuracy of investigation and monitoring. This paper aims to study a polygon assisted identification method based on knowledge database similarity retrieval to provide efficient and practical technical solutions for natural resource investigation and monitoring work.Firstly,This paper focuses on the construction and management of the knowledge database,designs and constructs a knowledge database framework covering a wide range of natural resource features,realize massive sample data to feature expression, organizational management, updating and maintenance, and effective utilization.Secondly, this paper proposes a novel similarity retrieval algorithm to optimize the execution efficiency of the algorithm, and verifies its effectiveness through application research. The research results show that this method effectively supports the needs of polygon assisted identification and enhances the accuracy and efficiency of investigation and monitoring work.

The high-precision data has the characteristics of irregular lane lines and segmented measurement, leading to problems such as bending and twisting in the connection of main roads. To solve this problem, an algorithm based on slime mold optimization is proposed to achieve optimal serial fusion of main roads. First, a solution space for serial concatenation of main roads is proposed based on feature vectors; then, according to the principle that main roads should not exceed lane lines as much as possible, considering the length of each section of the center line of lanes, and making the overall main road as close as possible to the center lines of all lanes, an evaluation function based on key nodes of lane lines and center lines of lanes is proposed; next, solutions are proposed for the hybridization and direction outlier problems of lane center lines with intersections and lane center lines; finally, combining slime mold optimization, detailed flow of the algorithm is given, and in order to accelerate convergence efficiency, a neighborhood solution strategy is introduced into the stage of slime mold food capture. Experimental results show that the proposed algorithm shows good robustness in various serial fusion scenarios; introducing neighborhood solution strategy can improve convergence efficiency; compared with other optimization algorithms, it has higher search accuracy and convergence speed.

The quantitative assessment of the relationship between land use(LULC) changes and ecosystem service values (ESV) is of great significance for optimizing land use structures and formulating sustainable land use policies. This study takes the Yunnan-Guizhou Plateau as the study area, and uses the ESV equivalence coefficient to quantitatively account for the value of ecosystem services in the Yunnan-Guizhou Plateau during the period of 2001—2020 and to analyze its LULC dynamics and its impact on ESV. The results show that the LULC patterns in the Yunnan-Guizhou Plateau have undergone significant changes over the past two decades, with a notable increase in forest area.The distribution of ESV exhibites marked spatial and temporal heterogeneity; the total ESV increased significantly from 638.1 billion yuan in 2001 to 746.99 billion yuan in 2020, mainly driven by forest restoration, the conversion of grassland to forest, and farmland to grassland. Therefore, we conclude that LULC changes resulting from ecological restoration measures have had a significantly positive impact on the total ESV of the Yunnan-Guizhou Plateau.

In order to carry out the recheck of reservoir capacity curve,this article takes a medium-sized reservoir in Shandong Province as an example.Fourth-grade-leveling survey is used to measure the elevation around the reservoir to the national high-level benchmark.Then,unmanned aerial vehicle low altitude aerial photography and unmanned ship underwater measurement are used to obtain the three-dimensional spatial coordinate data of the reservoir terrain,and a three-dimensional model of the reservoir area is generated.With the help of the 3D model,a topographic map and contour lines of the reservoir area are drawn.The calculated storage capacity directly extracted from the 3D model of the reservoir area is compared with the storage capacity calculated by the DEM grid method generated by the terrain points and feature points around the reservoir area.The results show that the area and storage capacity calculated by the two methods are basically consistent,which verifies the accuracy of the contour line method calculation results extracted from the model and provides technical reference for the review of the reservoir capacity curve.

This paper discusses the construction and implementation of the “competition-education integration” teaching system in the photogrammetry course. By integrating the elements of academic competitions into classroom teaching, it aims to enhance students' practical abilities and innovative thinking, and strengthen their understanding and application of professional knowledge. This paper proposes a new teaching system for the photogrammetry course, which integrates competition elements through pre-class and in-class group discussions, adopts formative and summative assessments. Finally, we introduces the construction of a competition-education practice platform based on unmanned aerial vehicle (UAV) virtual simulation technology, enriching the teaching and virtual simulation competition resources, and enhancing students' comprehensive qualities and employability. The research shows that the “competition-education integration” model effectively improves students' learning interest and practical abilities, laying a foundation for cultivating professional talents that meet social needs.

The construction of curriculum ideological and political education is an important measure to fulfill the fundamental task of cultivating people with integrity. In response to the phenomenon of focusing on knowledge teaching and neglecting ideological and political education in professional course teaching in university, this paper takes “remote sensing introduction” course as an example to deeply explore the necessity of ideological and political education in professional courses. Combining the unique advantages of remote sensing course content in ideological and political education,the paper deeply mines the key points of integrating ideological and political education into the course,and builds a collaborative ideological and political education model for the “remote sensing introduction” course that serves the revitalization of Heilongjiang province. It also proposes the “three-classroom integration” ideological and political education teaching model. In the implementation process,we base ourselves on the first classroom,paying attention to the integration of ideological and political education,solidifying the theoretical foundation; strengthen the second classroom by combining practice,training and competitions,and devote to improve the students' innovative ability; expand the third classroom by guiding with culture,and nurturing people with culture. Through the implementation of the three-classrooms,we achieve the organic integration of knowledge education,practice education,and cultural education,and embed patriotism,ideals and beliefs,and the unity of knowledge and action throughout the entire course of professional course teaching,effectively achieving the course teaching goals and ideological and political education goals,and comprehensively enhancing the effect of education.

In response to the incomplete evaluation elements of existing point cloud accuracy and the inability to effectively control the accuracy of point cloud data processing, a comprehensive evaluation method for point cloud acquisition and data processing accuracy based on point, line, and surface elements is proposed for the first time to meet the application requirements of point cloud data in water and drought disasters. The point cloud plane dimension proposes a comprehensive evaluation of the point cloud plane accuracy based on factors such as point plane accuracy, feature lines of land features, single point plane position of land features, edge length and area of land features.The elevation dimension of point clouds is proposed to comprehensively evaluate the accuracy of point cloud elevation by considering factors such as single point elevation, terrain profile, and terrain surface model, including the enclosing area of terrain profiles and terrain volume. The results indicate that the laser point cloud comprehensive accuracy evaluation method based on water and drought disasters can comprehensively, objectively, and accurately evaluate the accuracy of point clouds from the plane and elevation dimensions, as well as the entire process of point cloud data acquisition, point cloud filtering, and data application. It can be extended to other fields of laser point cloud applications and has good reference significance.

The Jurassic coalfield in Northern Shaanxi is located in a region of China that is ecologically sensitive. Long-term coal mining activities have had a profound impact on the ecology of this area. By monitoring and analyzing the patterns of surface movement, it is possible to assess the extent of the affected areas and the regions that are most severely damaged, thereby formulating targeted remediation measures. This study utilizes actual mining data from a coal mine in Shaanxi to calculate and analyze the specific values of surface subsidence and horizontal displacement, as well as the dynamic and static angular parameters of surface movement. Based on the principle of the probability integration method, probability integration parameters are determined using Matlab for parameter fitting. The findings of this study provide a reference for understanding the patterns of surface movement over shallow coal mines in the Northern Shaanxi region and for the management of the ecological environment.