Three-dimensional geographic scene dynamic updating method and device based on space-time interpolation, equipment and medium

Abstract

The invention discloses a three-dimensional geographic scene dynamic updating method, device and equipment based on space-time interpolation and a medium, and the method comprises the steps: collecting multi-source heterogeneous data, including a satellite image, an unmanned plane remote sensing image and ground sensor data; calculating the weight of a single data source according to the spatial resolution and the time resolution of the data; carrying out spatial interpolation and time interpolation calculation on spatial points of the three-dimensional scene in combination with the multi-source data weight; performing nonlinear compensation on the spatial interpolation and the time interpolation to obtain a compensated spatial point update value of the three-dimensional scene; and comparing interpolation results at different time points, detecting local change, and updating the change area of the three-dimensional scene. According to the method, the fusion quality is guaranteed through reasonable weights, and the weights are configured by integrating spatial resolution, time resolution, data quality and reference data similarity, so that three-dimensional scene basic data fusion is more fit for real geographic features.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic updating of three-dimensional geographic scenes, and in particular to a method, device, equipment and medium for dynamic updating of three-dimensional geographic scenes based on spatiotemporal interpolation. Background Art

[0002] With the rapid development of remote sensing technology and three-dimensional geographic information systems (3DGIS), the construction of three-dimensional geographic scenes has been widely used in various industries. For complex application scenarios, such as real-life 3D, city maps, and real-time monitoring of the natural environment, the update of dynamic scenes places higher demands on data fusion and processing. Existing technologies mainly use triangulated network (TIN) algorithms to match the elevation of models and terrain, achieve full analysis and coordinate conversion of new and old data sources, combine linear interpolation methods to estimate data changes in time or space, and directly render the updated scene. Existing technologies have obvious shortcomings in the following aspects:

[0003] (1) A single data source usually has a specific coverage range (e.g., satellite remote sensing data mainly covers large-scale areas, while drone remote sensing data focuses on small areas). In scenarios that require both global and local refined modeling, a single data source cannot simultaneously meet multi-scale requirements. (2) The full reconstruction method reloads and renders the updated 3D model and terrain data. This method requires a lot of computing resources and takes a long time to update. (3) The linear interpolation method can only be used for simple changing scenarios, and cannot accurately describe real-time scenarios for complex nonlinear dynamic processes (such as disasters and ecological changes).

[0004] For example, invention application publication number CN118689889A discloses a method and device for updating full-factor map data based on multi-technology integration. This method uses drones to collect and update full-factor data for regional maps along a predetermined route. However, this invention uses a single drone data source, limiting it to updating three-dimensional map data within a small area. Furthermore, updating full-factor regional maps cannot rapidly update local data, requiring significant computing resources and resulting in lengthy updates.

[0005] Based on the technical flaws exposed by the above methods, a dynamic update method for 3D geographic scenes based on spatiotemporal interpolation is needed. This method is suitable for high-quality fusion and dynamic weighting of multi-source data and is suitable for scenarios such as urban planning, disaster response, and environmental monitoring. Summary of the Invention

[0006] The purpose of the present invention is to provide a method, device, equipment and medium for dynamic updating of three-dimensional geographic scenes based on spatiotemporal interpolation, so that the fusion of basic data of three-dimensional scenes can be more in line with real geographic features, and the scene update can provide high-quality basic data.

[0007] Embodiments of the present invention provide a method, apparatus, device and medium for dynamically updating a three-dimensional geographic scene based on spatiotemporal interpolation.

[0008] A first aspect: A method for dynamically updating a three-dimensional geographic scene based on spatiotemporal interpolation, comprising:

[0009] S1. Collect multi-source heterogeneous data, including satellite images, UAV remote sensing images, and ground sensor data;

[0010] S2. Calculate the weight of a single data source based on the spatial resolution and temporal resolution of the data;

[0011] S3, for the spatial points of the three-dimensional scene, spatial interpolation and temporal interpolation calculation are performed in combination with multi-source data weights;

[0012] S4. Perform nonlinear compensation on the spatial interpolation and the temporal interpolation to obtain a compensated spatial point update value of the three-dimensional scene;

[0013] S5. Compare the interpolation results at different time points, detect local changes, and update the changed areas of the three-dimensional scene.

[0014] Furthermore, the quality coefficient and similarity metric are used in S2 to dynamically evaluate the weight of each data source, which can be expressed as follows:

[0015]

[0016] where R i is the spatial resolution of the i-th data source, which is the number of pixels within the spatial area; where T i is the time resolution of the i-th data source, which is the time interval of data collection; Q i is the data quality coefficient, which is used to consider factors such as noise and missingness of the data source; Sim(D i ,D ref ) is a data similarity metric used to measure the degree of matching with the reference data; α, β, and γ are used to adjust the indicator weights.

[0017] Furthermore, in step S3, spatial interpolation calculation is performed, and the formula is:

[0018]

[0019] where ω i is the weight of the i-th data source, f i (x, y, z) is the attribute value of the i-th point in the three-dimensional data (x, y, z), Δ i (x, y, z) is the deviation correction function of the i-th data source based on multi-scale decomposition and error statistics.

[0020] Furthermore, the deviation correction function of the data source is expressed as follows:

[0021]

[0022] Among them C il is the correction coefficient of the i-th data source at the l-th scale, φ l (x,y,z) is the lth scale basis function.

[0023] Furthermore, the time interpolation calculation in S3 adopts an improved Lagrange interpolation formula, which is expressed as follows:

[0024]

[0025] δ k is the dynamic adjustment coefficient of the kth time period, is the nonlinear basis function of time period k, L k (t) is the basis function of Lagrangian interpolation.

[0026] Furthermore, in S4, nonlinear compensation is performed on spatial interpolation and temporal interpolation, and the formula is expressed as follows:

[0027]

[0028] Where C(t,x,t,z) is the updated value of the 3D scene after compensation, S(x,y,z) is the spatial interpolation result, T(t) is the temporal interpolation result, ω i is the weight of the i-th data source, f i (x, y, z) is the attribute value of the i-th point in the three-dimensional data (x, y, z), θ and μ are adjustment coefficients, and the hyperbolic tangent function tanh(x) is introduced to compress the extreme values ​​of the data.

[0029] Furthermore, in S5, the interpolation results at different time points are compared to detect local changes. The formula is expressed as:

[0030]

[0031] where R p is the pth block area, ∈ p For region R p The change detection base threshold, σ p For region R p The uncertainty coefficient of the change.

[0032] A second aspect: A three-dimensional geographic scene dynamic updating device based on spatiotemporal interpolation, comprising:

[0033] Data acquisition module, used to collect multi-source heterogeneous data;

[0034] Weight configuration module, used to configure the weight of each data source;

[0035] Interpolation calculation module, used to combine multi-source data weights to perform spatial interpolation and temporal interpolation calculations;

[0036] A compensation calculation module is used to obtain the updated spatial point values ​​of the compensated three-dimensional scene based on spatial interpolation and temporal interpolation;

[0037] The detection and update module is used to detect local changes in the three-dimensional scene and update the changed areas of the three-dimensional scene.

[0038] A third aspect: An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method provided in the first aspect are implemented.

[0039] A fourth aspect: A non-transitory computer-readable storage medium having a computer program stored thereon, which implements the steps of the method provided in the first aspect when executed by a processor.

[0040] Beneficial effects of the present invention:

[0041] 1. This invention ensures fusion quality through reasonable weighting. By assigning weights based on spatial resolution, temporal resolution, data quality, and similarity to reference data, this approach allows heterogeneous data from multiple sources, such as satellites, drones, and sensors, to "live their best." High-spatial-resolution data is weighted appropriately for local, detailed modeling, while high-temporal-resolution data is advantageous for dynamic monitoring. This approach addresses the precision loss associated with traditional, one-size-fits-all fusion of multi-source data, ensuring that the fusion of basic 3D scene data more closely matches actual geographic features, laying a solid foundation for subsequent modeling and updates.

[0042] 2. This invention uses a joint interpolation model to model dynamic changes in space and time through joint interpolation optimization and dynamic characterization, breaking the limitations of traditional separate spatial and temporal interpolation. On the one hand, it collaboratively processes spatiotemporal data, reduces repeated calculations and error transmission, and improves interpolation efficiency (such as shortening modeling time and adapting to high-frequency dynamic update scenarios). On the other hand, it more accurately captures the coupling laws of "spatial distribution + temporal evolution" of three-dimensional scenes (such as the spatial growth of urban buildings over time and the spatiotemporal development of terrain disasters), making the interpolation results more consistent with the real dynamics of the geographical scene and providing high-quality basic data for subsequent change detection and scene updates.

[0043] 3. This invention overcomes traditional bottlenecks by learning complex nonlinear variations through nonlinear correction. This approach corrects for dynamic details that are difficult to capture with traditional interpolation, thus addressing the shortcomings of linear interpolation in depicting nonlinear features in geographic scenes. For example, traditional methods often miss key details due to linear assumptions, such as the nonlinear uplift of terrain due to geological movement and the nonlinear seasonal evolution of wetland ecosystems. This strategy, through nonlinear correction, allows dynamic updates of 3D scenes to more accurately reproduce real geographic processes, enhancing the application value of these scenes.

[0044] 4. This method eliminates the redundant full-data reconstruction model and instead employs local change detection and targeted update mechanisms, reducing update time from 2 hours to 12 minutes. This allows for high-frequency response to dynamic scenario changes, meeting the timeliness requirements of real-time monitoring, emergency response, and other applications. Local updates avoid recalculating full data, significantly reducing computing power and storage resource consumption. When processing large-scale scenarios, dynamic updates are maintained at a lower computational cost, adapting to diverse hardware environments and lowering the barriers to project implementation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 Schematic diagram of the process of the dynamic updating method of a three-dimensional geographic scene based on spatiotemporal interpolation of the present invention;

[0046] Figure 2 A schematic diagram of the structure of a three-dimensional geographic scene dynamic updating device based on spatiotemporal interpolation according to the present invention;

[0047] Figure 3 Schematic diagram of the structure of the electronic device of the present invention. DETAILED DESCRIPTION

[0048] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar symbols throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0049] Dynamic updating of three-dimensional geographic scenes faces challenges: first, the coverage of a single data source is limited and cannot meet the needs of multi-scale modeling; second, the full reconstruction method is computationally intensive and time-consuming to update; finally, the linear interpolation method is not suitable for complex nonlinear dynamic scenes.

[0050] In order to solve the above problems, the present invention provides a method for dynamically updating a three-dimensional geographic scene based on spatiotemporal interpolation. Figure 1 A schematic flow chart of a method for dynamically updating a three-dimensional geographic scene based on spatiotemporal interpolation provided by an embodiment of the present invention includes:

[0051] S1. Collect multi-source heterogeneous data.

[0052] Multi-source heterogeneous data types include satellite images, drone remote sensing images, and ground sensor data.

[0053] Satellite imagery has a wide coverage area and is suitable for obtaining geographic information on large-scale areas, such as the overall layout of cities and large-scale topography. Its spatial resolution is relatively low (for example, 1 meter in this example), but its temporal resolution can be longer, making it suitable for macro-level data support.

[0054] UAV remote sensing has a high spatial resolution (0.1 meters in this example), which allows for fine data collection over small areas, such as building details and road textures. Its temporal resolution is relatively short, allowing for more frequent acquisition of the latest data for a local area.

[0055] Ground sensor data has an extremely high temporal resolution (e.g., 0.1 hour in this example) and can collect data at specific locations in real time or at high frequency, such as ground elevation changes and environmental parameters. However, its spatial coverage is limited (e.g., the spatial resolution is 5 meters in this example) and is typically used for precise monitoring of local key points.

[0056] The use of multi-source heterogeneous data can meet multi-scale needs. Satellite images are used to cover the entire world, while drone remote sensing and ground sensors focus on the local area. The combination of the three can achieve multi-scale modeling from macro to micro, solving the problem that a single data source cannot simultaneously meet the needs of global and local refined modeling.

[0057] The use of multi-source heterogeneous data improves data reliability. The characteristics of different data sources complement each other, such as satellite imagery providing macro context, drone remote sensing supplementing details, and ground sensors verifying local accuracy. This can reduce the errors and limitations of a single data source and improve the overall quality and reliability of the data.

[0058] The use of multi-source heterogeneous data can adapt to complex scenarios. In complex scenarios such as urban planning, disaster emergency response, and environmental monitoring, multi-source data can more comprehensively reflect the dynamic changes of geographical scenes and provide a richer information basis for subsequent dynamic updates.

[0059] S2. Calculate the weight of a single data source based on the spatial resolution and temporal resolution of the data.

[0060] Weight calculation is to dynamically allocate the contribution ratio of multi-source data by quantifying four major indicators: spatial resolution, temporal resolution, data quality, and similarity measurement, so as to solve the problem in existing technologies that a single data source cannot adapt to multi-scale requirements.

[0061] The weight calculation result directly affects the accuracy of subsequent spatiotemporal interpolation and is one of the keys to achieving local updates rather than full reconstruction. The formula is expressed as:

[0062]

[0063] where R i is the spatial resolution of the i-th data source, which is the number of pixels within the spatial area and measures the spatial fineness of the data; R i The smaller it is (the higher the resolution), the higher the weight.

[0064] Where T i is the time resolution of the i-th data source, which is the time interval of data collection; for example, in the example, the ground sensor T3 = 0.1, T i The smaller it is (the higher the update frequency), the higher the weight.

[0065] Q i It is the data quality coefficient, which is used to consider factors such as noise and missingness of the data source. For example, in the drone data example, Q2 = 0.95. The higher the value, the higher the weight.

[0066] Sim(D i ,D ref ) is a data similarity metric used to measure the degree of matching with reference data, such as the similarity between satellite images and reference data. The higher the similarity, the higher the weight.

[0067] α, β, and γ are used to adjust the indicator weights. The manually set indicator weights (such as α = 0.4, β = 0.3, γ = 0.2 in the example) can flexibly adjust the importance of each indicator according to scenario requirements.

[0068] S3. For the spatial points of the three-dimensional scene, spatial interpolation and temporal interpolation calculations are performed in combination with multi-source data weights.

[0069] Based on the weights of multi-source data, the three-dimensional attribute values ​​(such as elevation) of multi-source data such as satellites, drones, and sensors are integrated into precise values ​​under a unified spatial coordinate in the spatial dimension; in the time dimension, the dynamic changes of the same spatial point at different times are captured.

[0070] The calculation formula for spatial interpolation is:

[0071]

[0072] where ω i is the weight of the i-th data source, f i (x, y, z) is the attribute value of the i-th point in the three-dimensional data (x, y, z), Δ i (x, y, z) is the deviation correction function of the i-th data source based on multi-scale decomposition and error statistics.

[0073] This formula follows the principle that high-weight data dominates the interpolation results. For example, UAV data (high spatial resolution) dominates the interpolation of local details. Theoretical innovation of deviation correction is carried out, and the deviation correction function is introduced. The formula is expressed as:

[0074]

[0075] Among them C il is the correction coefficient of the i-th data source at the l-th scale, φ l (x,y,z) is the lth scale basis function.

[0076] The bias correction function is used to solve the inherent error of the data source, and the error is decomposed into components of different spatial scales (such as meters and centimeters). Hierarchical correction is achieved through linear combination to reduce the system error.

[0077] Based on the above spatial interpolation calculation, the consistency constraint of multi-source data is realized: through the weight ω i and bias correction Δ i (x, y, z), forcing the attribute values ​​of different data sources at spatial points to meet weighted consistency, for example, solving the scale conflict problem between satellite (macro) and drone (local) data.

[0078] Traditional Lagrange interpolation is only applicable to linear change scenarios. Its theoretical assumption is that the time series satisfies polynomial smoothness and cannot describe sudden changes.

[0079] The present invention performs time interpolation calculation using an improved Lagrange interpolation formula, which is expressed as:

[0080]

[0081] Among them, δ k is the dynamic adjustment coefficient of the kth time period, is the nonlinear basis function of time period k, L k (t) is the basis function of Lagrangian interpolation.

[0082] Use dynamic adjustment coefficient δ k ,Based on the variance analysis of time series, the weight of each time period can be adjusted in real time,δ k Increasing will strengthen the impact of the latest data; nonlinear basis function Can fit dynamic processes.

[0083] The calculation of spatial interpolation and temporal interpolation forms a spatiotemporal coupling relationship. The spatial interpolation result provides the spatial benchmark of the current moment for the temporal interpolation, and the temporal interpolation result provides the historical change trend for the spatial interpolation. The two form a bidirectional constraint through weights.

[0084] S4. Perform nonlinear compensation on the spatial interpolation and the temporal interpolation to obtain the compensated spatial point update value of the three-dimensional scene.

[0085] The obtained spatial interpolation results and temporal interpolation results are input into the nonlinear mapping function, and the interpolation results are corrected by the nonlinear compensation algorithm to obtain a more accurate 3D scene update value. The nonlinear compensation of spatial interpolation and temporal interpolation is performed, and the formula is expressed as follows:

[0086]

[0087] Where C(t,x,t,z) is the updated value of the 3D scene after compensation, S(x,y,z) is the spatial interpolation result, T(t) is the temporal interpolation result, ω i is the weight of the i-th data source, f i (x, y, z) is the attribute value of the i-th point in the three-dimensional data (x, y, z), θ and μ are adjustment coefficients, and the hyperbolic tangent function tanh(x) is introduced to compress the extreme values ​​of the data.

[0088] Spatial interpolation S(x,y,z) reflects the distribution of attribute values ​​of a 3D scene in the spatial dimension. Time interpolation T(t) reflects the changing trend of data in the temporal dimension. Data source weight ω i Used to measure the contribution of different data sources to nonlinear compensation. Adjustment coefficients θ and μ adjust the intensity and method of nonlinear compensation and can be set based on the actual application scenario. tanh(x) is the hyperbolic tangent function, which compresses extreme values ​​of the data, making the compensated results more stable and reasonable.

[0089] First, add the spatial interpolation result S(x,y,z) and the temporal interpolation result T(t) multiplied by the adjustment coefficient θ to obtain the preliminary compensation basis. Then, calculate ω i f i (x,y,z), where f i (x, y, z) represents the attribute value of the i-th point in the 3D data. This product reflects the combined influence of the data source weight and the attribute value. This product is then substituted into the hyperbolic tangent function, tanh(x), which compresses the data extremes. The result is then multiplied by the adjustment coefficient μ and added to 1 to obtain a correction factor. Finally, the temporal interpolation result, T(t), is multiplied by this correction factor and then added to the spatial interpolation result, S(x, y, z), to obtain the final compensated 3D scene update value, C(t, x, t, z).

[0090] The output result is the compensated 3D scene update value C(t,x,t,z). This value comprehensively considers the information of spatial and temporal dimensions and corrects possible nonlinear changes through a nonlinear compensation algorithm. It can more accurately reflect the actual state of the 3D geographic scene and provide a more reliable data basis for subsequent local change detection and scene updates.

[0091] S5. Compare the interpolation results at different time points, detect local changes, and update the changed areas of the three-dimensional scene.

[0092] By comparing the spatiotemporal interpolation results at different time points, we can accurately identify the local change areas in the 3D geographic scene and perform targeted updates only on the changed areas, avoiding full reconstruction and thus improving update efficiency and accuracy. The formula is expressed as:

[0093]

[0094] where R p is the pth block area, ∈ p For region R p The change detection base threshold, σ p For region R p The uncertainty coefficient of the change.

[0095] Calculate the spatiotemporal interpolation result C1(t1,x,t,z) at the current time point and the interpolation result C2(t2,x,t,z) at the historical time point

[0096] Divide the 3D scene into multiple block regions R p (p is the region number), each region corresponds to an independent change detection threshold ∈ p and the uncertainty coefficient σ p ,The threshold is set according to the scenario requirements (such as terrain complexity, data accuracy).

[0097] By calculating the absolute value of the difference between the current and historical interpolation results in each block area, if the value exceeds the threshold, it is determined that there is a significant change in the area and it is included in the set of areas to be updated D.

[0098] In the second embodiment, the present invention also provides a three-dimensional geographic scene dynamic update device based on spatiotemporal interpolation, such as Figure 2 As shown, the device includes a data acquisition module, a weight configuration module, an interpolation calculation module, a compensation calculation module and a detection update module, among which:

[0099] The data acquisition module is used to collect multi-source heterogeneous data; the weight configuration module is used to configure the weights of each data source; the interpolation calculation module is used to perform spatial interpolation and temporal interpolation calculations based on the weights of multi-source data; the compensation calculation module is used to obtain the spatial point update values ​​of the compensated three-dimensional scene based on spatial interpolation and temporal interpolation; the detection and update module is used to detect local changes in the three-dimensional scene and update the changed areas of the three-dimensional scene.

[0100] The above-mentioned device, with its various modules working in tandem, addresses the low efficiency of full reconstruction and insufficient linear interpolation accuracy found in traditional technologies. By integrating five modules—data acquisition, weight configuration, interpolation calculation, compensation correction, and change detection—the device achieves high-precision, high-efficiency dynamic updates of three-dimensional geographic scenes, making it suitable for a variety of scenarios, including urban planning and disaster response.

[0101] The data acquisition module collects heterogeneous data from multiple sources, providing foundational information for subsequent processing. Supported data sources include satellite imagery (large-scale spatial coverage), drone remote sensing (high spatial resolution), and ground sensors (high-frequency temporal sampling), covering multi-scale and multi-temporal data needs.

[0102] The weight configuration module dynamically calculates the weight of each data source according to the data characteristics to achieve weighted fusion of multi-source data.

[0103] Weight calculation formula:

[0104]

[0105] R i is the spatial resolution (number of pixels), T i is the time resolution (time interval); Q i is the data quality coefficient (measurement of noise and missingness), Sim(D i ,D ref ) is the similarity measure with the reference data, and α, β, and γ are weight adjustment coefficients.

[0106] For example, the original weights of satellite imagery, drones, and ground sensors are 0.6618, 4.2925, and 3.31, respectively, which are normalized to 0.08, 0.52, and 0.40, reflecting the weight advantages of high spatial resolution (drone) and high temporal resolution (sensor) data.

[0107] The interpolation calculation module combines the data source weights to perform spatial interpolation and temporal interpolation respectively to construct a spatiotemporal dynamic model of the three-dimensional scene.

[0108] The spatial interpolation formula is:

[0109]

[0110] where fi (x, y, z) is the data source attribute value, Δ i (x, y, z) is the deviation correction function, which is corrected through multi-scale decomposition and error statistics calculation (for example, the drone data deviation correction Δ2 = 0.5 meters in the example improves interpolation accuracy).

[0111] Time interpolation is obtained through the improved Lagrange interpolation formula, which is expressed as:

[0112]

[0113] Among them L k (t) is the Lagrangian basis function, δ k is the dynamic adjustment coefficient, is a nonlinear basis function (for example, the interpolation result from time period t0 to t1 in the example is 52.0 meters).

[0114] The compensation calculation module compensates the spatiotemporal interpolation results through a nonlinear mapping function, correcting the nonlinear changes that cannot be captured by traditional linear interpolation.

[0115] The nonlinear compensation formula is expressed as:

[0116]

[0117] Where θ and μ are adjustment coefficients (θ = 0.3 and μ = 0.7 in this example). The hyperbolic tangent function tanh(x) is used to compress the extreme values ​​of the data.

[0118] The detection and update module compares the interpolation results at multiple time points, detects local changes, and updates only the changed areas to avoid full reconstruction.

[0119] Change Detection Formula

[0120]

[0121] where R p is the pth block area, ∈ p For region R p The change detection base threshold, σ p For region R p The uncertainty coefficient of the change. (In this example, area A has a threshold of 0.5 meters, a coefficient of 0.1, and a difference of 2.18 meters > 0.55 meters, so it is determined to be a changing area).

[0122] The present invention utilizes change detection to reload data only for the changed areas, perform interpolation and compensation, and in practice, the update time is shortened from 2 hours of the traditional method to 12 minutes, and the elevation error is reduced from 8% to 1.5%.

[0123] The device described in this paper adapts to multi-source data from satellites, drones, and sensors through a weight configuration module, resolving the problem of a single data source failing to address multi-scale requirements. The detection and update module implements local updates, reducing computational complexity by over 90% compared to traditional full reconstructions, while also improving accuracy through nonlinear compensation. This approach is suitable for scenarios requiring frequent updates, such as urban planning, where new building elevation data can be dynamically updated; disaster emergency response, where real-time monitoring of terrain changes in disaster-stricken areas, such as landslides and floods; and environmental monitoring, where nonlinear processes such as vegetation cover and landform evolution in ecological regions can be tracked.

[0124] Application examples:

[0125] For example, the 3D elevation data of a certain urban area is dynamically updated to detect changes in newly built buildings (area A).

[0126] Collect multi-source heterogeneous data, including satellite imagery, drone remote sensing, and ground sensor data. For example, collect satellite imagery, drone remote sensing, and ground sensor data sources separately:

[0127] Satellite imagery: spatial resolution R1 = 1 meter, temporal resolution T1 = 168 hours, quality factor Q1 = 0.9, similarity Sim1 = 0.8.

[0128] UAV remote sensing: spatial resolution R2 = 0.1 m, temporal resolution T2 = 24 hours, quality coefficient Q2 = 0.95, similarity Sim2 = 0.9.

[0129] Ground sensor: spatial resolution R3 = 5 meters, temporal resolution T3 = 0.1 hours, quality factor Q3 = 0.8, similarity Sim3 = 0.7.

[0130] Set α=0.4, β=0.3, and γ=0.2.

[0131] The weight of each data source is calculated based on the spatial and temporal resolution of the data. Substituting the weights into the formula, we get: 0.6618 for satellite imagery, 4.2925 for drone remote sensing, and 3.31 for ground sensors. After normalization, we get:

[0132] ω1=0.6618 / (0.6618+4.2925+3.31)=0.08;

[0133] ω2=4.2925 / 8.2643=0.52;

[0134] ω3=3.31 / 8.2643=0.40;

[0135] For spatial points of three-dimensional scenes, spatiotemporal interpolation is performed by combining multi-source data.

[0136] Calculate spatial interpolation: Input data, satellite elevation f1 = 50.0 meters, drone f2 = 52.3 meters, sensor f3 = 51.8 meters, bias correction Δ2 = 0.5 meters (drone data), the bias correction of satellite data and sensor is 0, and the calculation is:

[0137] s(x,y,z)=0.08×50.0+0.52×(52.3+0.5)+0.40×51.8=4.0+27.456+20.72=52.176 meters

[0138] Calculation time interpolation: using the improved Lagrange interpolation formula, using the time period t0 to t1 (7 days), dynamic adjustment coefficients σ1 = 0.6, σ2 = 0.4, the interpolation result T(t) = 52.0 meters.

[0139] Input the spatial interpolation and temporal interpolation into the nonlinear mapping function to obtain the compensated result. Set μ = 0.7, θ = 0.3, and substitute the temporal interpolation, spatial interpolation, and weight values ​​into the formula to calculate the nonlinear compensation:

[0140] C(t,x,t,z)=tanh(0.7×52.176+0.3×52.2)=tanh(52.177)≈52.18 meters

[0141] Compare the interpolation results at different time points, detect local changes, and update the changed areas. According to the above calculation steps, the original elevation is 50 meters, and the updated elevation is 52.18 meters. According to the formula:

[0142]

[0143] Set the threshold for area A to 0.5 meters and the uncertainty coefficient to 1.1.

[0144] Judgment: |52.18-50.0|=2.18>0.5×1.1=0.55, so this area needs to be updated.

[0145] According to the above steps, area A needs to be dynamically updated.

[0146] At the same time, performance verification was carried out, and the data support shown in Table 1 was obtained:

[0147] Table 1 Comparison of elevation error and update time between the traditional method and the method of the present invention

[0148] index Traditional methods The present invention Elevation error 8% (full reconstruction) 1.5% (partial update) Update Time 2 hours 12 minutes

[0149] The above examples can verify that the present invention can achieve high-precision and high-efficiency dynamic update of three-dimensional scenes through multi-source data fusion and spatiotemporal interpolation, which can improve the accuracy and efficiency of computers in processing three-dimensional scenes and realize efficient update of three-dimensional scenes.

[0150] The present invention also provides an electronic device, Figure 3 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 3 As shown, the electronic device may include: a processor, a communications interface, a memory, and a communications bus, wherein the processor, the communications interface, and the memory communicate with each other via the communications bus. The processor may call logic instructions in the memory, for example, to execute the following method: (method steps).

[0151] S1. Collect multi-source heterogeneous data, including satellite images, UAV remote sensing images, and ground sensor data.

[0152] S2. Calculate the weight of a single data source based on the spatial resolution and temporal resolution of the data.

[0153] S3. For the spatial points of the three-dimensional scene, spatial interpolation and temporal interpolation calculations are performed in combination with multi-source data weights.

[0154] S4. Perform nonlinear compensation on the spatial interpolation and the temporal interpolation to obtain a compensated spatial point update value of the three-dimensional scene;

[0155] S5. Compare the interpolation results at different time points, detect local changes, and update the changed areas of the three-dimensional scene.

[0156] In addition, the logical instructions in the above-mentioned memory can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0157] An embodiment of the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method provided in each of the above embodiments is implemented, for example, including:

[0158] S1. Collect multi-source heterogeneous data, including satellite images, UAV remote sensing images, and ground sensor data.

[0159] S2. Calculate the weight of a single data source based on the spatial resolution and temporal resolution of the data.

[0160] S3. For the spatial points of the three-dimensional scene, spatial interpolation and temporal interpolation calculations are performed in combination with multi-source data weights.

[0161] S4. Perform nonlinear compensation on the spatial interpolation and the temporal interpolation to obtain a compensated spatial point update value of the three-dimensional scene;

[0162] S5. Compare the interpolation results at different time points, detect local changes, and update the changed areas of the three-dimensional scene.

[0163] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0164] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for dynamically updating a three-dimensional geographic scene based on spatiotemporal interpolation, characterized in that: Including steps: S1. Collect multi-source heterogeneous data, including satellite images, UAV remote sensing images, and ground sensor data; S2. Calculate the weight of a single data source based on the spatial resolution and temporal resolution of the data; S3, for the spatial points of the three-dimensional scene, spatial interpolation and temporal interpolation calculation are performed in combination with multi-source data weights; S4. Perform nonlinear compensation on the spatial interpolation and the temporal interpolation to obtain a compensated spatial point update value of the three-dimensional scene; S5. Compare the interpolation results at different time points, detect local changes, and update the changed areas of the three-dimensional scene.

2. A method for dynamically updating a three-dimensional geographic scene based on spatiotemporal interpolation according to claim 1, characterized in that: In S2, the quality coefficient and similarity metric are used to dynamically evaluate the weight of each data source, which is expressed as follows: Among them, R i is the spatial resolution of the i-th data source; T i is the time resolution of the i-th data source; Q i is the data quality coefficient; Sim(D i ,D ref ) is the data similarity measure; α, β, and γ are used to adjust the indicator weights.

3. The method for dynamically updating a three-dimensional geographic scene based on spatiotemporal interpolation according to claim 1, characterized in that: In step S3, spatial interpolation calculation is performed, and the formula is: Among them, ω i is the weight of the i-th data source, f i (x, y, z) is the attribute value of the i-th point in the three-dimensional data (x, y, z), Δ i (x, y, z) is the deviation correction function of the i-th data source based on multi-scale decomposition and error statistics.

4. The method for dynamically updating a three-dimensional geographic scene based on spatiotemporal interpolation according to claim 3, characterized in that: The deviation correction function of the data source is expressed as follows: Among them, C il is the correction coefficient of the i-th data source at the l-th scale, φ l (x,y,z) is the lth scale basis function.

5. The method for dynamically updating a three-dimensional geographic scene based on spatiotemporal interpolation according to claim 4, characterized in that: The time interpolation calculation in S3 adopts the improved Lagrange interpolation formula, which is expressed as follows: Among them, δ k is the dynamic adjustment coefficient of the kth time period, is the nonlinear basis function of time period k, L k (t) is the basis function of Lagrangian interpolation.

6. A method for dynamically updating a three-dimensional geographic scene based on spatiotemporal interpolation according to any one of claims 2 to 5, characterized in that: In S4, nonlinear compensation is performed on spatial interpolation and temporal interpolation, and the formula is expressed as follows: Among them, C(t,x,t,z) is the updated value of the 3D scene after compensation, S(x,y,z) is the spatial interpolation result, T(t) is the time interpolation result, ω i is the weight of the i-th data source, f i (x, y, z) is the attribute value of the i-th point in the three-dimensional data (x, y, z), θ and μ are adjustment coefficients, and tanh(x) is the hyperbolic tangent function.

7. The method for dynamically updating a three-dimensional geographic scene based on spatiotemporal interpolation according to claim 6, characterized in that: In S5, the interpolation results at different time points are compared to detect local changes. The formula is expressed as: where R p is the pth block area, ∈ p For region R p The change detection base threshold, σ p For region R p The uncertainty coefficient of the change.

8. A three-dimensional geographic scene dynamic updating device based on spatiotemporal interpolation applied to the method according to any one of claims 1 to 7, characterized in that: The device comprises: Data acquisition module, used to collect multi-source heterogeneous data; Weight configuration module, used to configure the weight of each data source; Interpolation calculation module, used to combine multi-source data weights to perform spatial interpolation and temporal interpolation calculations; A compensation calculation module is used to obtain the updated spatial point values ​​of the compensated three-dimensional scene based on spatial interpolation and temporal interpolation; The detection and update module is used to detect local changes in the three-dimensional scene and update the changed areas of the three-dimensional scene.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of a method for dynamically updating a three-dimensional geographic scene based on spatiotemporal interpolation are implemented as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for dynamically updating a three-dimensional geographic scene based on spatiotemporal interpolation as claimed in any one of claims 1 to 7 are implemented.

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