DEM precision improvement method based on multi-source data fusion

Abstract

The invention discloses a DEM (Digital Elevation Model) precision improvement method based on multi-source data fusion, and the method provided by the invention inspects the characteristics of original data from different sources, and carries out the selection and rejection of ground control points for correcting an undetermined model of a DEM when the ground control points are determined. The second reference area can reflect the influence of the human activities on the landform features and the earth surface features, and the ground control points based on the second reference area can reflect the influence of the environment on the human activities while improving the precision of the model, so that the model obtained by the application can be more targeted during online application. Particularly, when the model obtained by the method is applied to a risk prediction scene, the precision of a prediction result can be improved. That is to say, according to the method provided by the invention, not only can the fusion among the multi-source data be realized, but also the fusion between the model and the application scene can be realized through the technical means, and the use effect is improved.

Description

Technical Field

[0001] This application relates to the technical field of data processing applicable to management, supervision, or prediction purposes, and particularly to a method for improving the accuracy of DEM based on multi-source data fusion. Background Art

[0002] DEM (Digital Elevation Model) is a digital simulation of the ground terrain through limited terrain elevation data, and is a kind of solid ground model representing ground elevation in the form of an ordered numerical array. It is a branch of the Digital Terrain Model (DTM) and is specifically used to describe ground elevation information.

[0003] Naturally, improving the accuracy of DEM is conducive to improving its representation effect of terrain, landform and other conditions, which also makes the pursuit of high-precision DEM one of the goals pursued by researchers. However, improving DEM should only be a means, rather than the ultimate goal. The accuracy of DEM should also be combined with its application scenarios. Further, when certain conditions in its application scenarios change, the accuracy of DEM should also be adjusted adaptively to improve the usage effect.

[0004] In view of this, how to improve the accuracy of DEM in combination with its application scenarios has become an urgent problem to be solved. Summary of the Invention

[0005] The embodiments of this application provide a method for improving the accuracy of DEM based on multi-source data fusion to at least partially solve the above technical problems.

[0006] The embodiments of this application adopt the following technical solutions: In a first aspect, the embodiments of this application provide a method for improving the accuracy of DEM based on multi-source data fusion, and the method includes: Obtain the original data of the area to be processed; the original data includes: geomorphic measurement data, lidar measurement data, and surface mapping data; Divide the first reference area and the second reference area on the area to be processed; the first reference area is the area where the geomorphic measurement data belongs; the second reference area is the area in the area to be processed where there are artificial institutions and / or vehicle traffic is allowed; Fit the first reference area and the second reference area to obtain the first available area and the second available area; the first available area is the overlapping area of the first reference area and the second reference area; the second available area is the area in the first reference area that does not overlap with the second reference area; Based on the geomorphic measurement data, ground control points for each of the first available area and the second available area are determined such that the density of the ground control points in the first available area is greater than the density of the ground control points in the second available area; The DEM obtained by using GIS software based on the original data is used as a to-be-determined model; Each ground control point in the first available area is used to correct the to-be-determined model to obtain a target model.

[0007] In an optional embodiment of this specification, determining the ground control points for each of the first available area and the second available area based on the geomorphic measurement data includes: Based on the geomorphic measurement data, the ground control points of the first available area are determined as first to-be-determined points; Based on the geomorphic measurement data, the ground control points of the second available area are determined as second to-be-determined points; When the number of the second to-be-determined points is greater than the number of the first to-be-determined points, based on at least one of the lidar measurement data and the surface mapping data of the first available area, the ground control points of the first available area are determined as third to-be-determined points; The third to-be-determined points are traversed, and those among the third to-be-determined points whose distance from the adjacent first to-be-determined points is greater than a preset distance threshold are added to the first to-be-determined points until the density of the ground control points in the first available area is greater than the density of the ground control points in the second available area.

[0008] In an optional embodiment of this specification, the method further includes: The distance threshold is positively correlated with the ratio of the areas of the first available area and the second available area respectively.

[0009] In an optional embodiment of this specification, the method further includes: When the adopted surface mapping data includes fitted remote sensing mapping data, the third to-be-determined points are those among the ground control points of the first available area determined based on the lidar measurement data and the surface mapping data of the first available area, whose position similarity with the nearest first to-be-determined point is less than a preset similarity threshold; The fitted remote sensing mapping data is obtained by fitting the remote sensing mapping data collected in a historical time period. The fitting of the remote sensing mapping data is used to remove the ground control points in the remote sensing mapping data that vary significantly according to seasonal patterns.

[0010] In an optional embodiment of this specification, the method further includes: If after traversing the third to-be-determined point, it is still impossible to satisfy that the density of the ground control points in the first available area is greater than the density of the ground control points in the second available area, then the second available area is divided into a plurality of sub-areas at a preset step size, perpendicular to the trend of the distribution of the artificial mechanism or perpendicular to the trend allowing the vehicle to pass through; Traverse the sub-areas, and add the one with the maximum distance from the sub-areas to the first available area adjacent to the second available area to the first available area until the density of the ground control points in the first available area is greater than the density of the ground control points in the second available area.

[0011] In an alternative embodiment of the present specification, the method further includes: When the second reference area includes an area allowing the vehicle to pass through, the step size is positively correlated with the number of bends included in the passage allowing the vehicle to pass through and negatively correlated with the average turning angle of each bend; When the second reference area includes an artificial mechanism, the step size is negatively correlated with the number of artificial mechanisms.

[0012] In an alternative embodiment of the present specification, the method further includes: When the second reference area includes an area allowing the vehicle to pass through, predict the usage frequency of the vehicle for the second reference area; If the usage frequency is greater than a preset frequency threshold, after correcting the to-be-determined model with each ground control point in the first available area, further correct it with each ground control point in the second available area to obtain a target model.

[0013] In an alternative embodiment of the present specification, the method further includes: The geomorphic measurement data includes at least one of the following: total station measurement data, ground triangulation data, and leveling data.

[0014] In an alternative embodiment of the present specification, the method further includes: The artificial mechanism includes at least one of the following: wind turbines, high-voltage power towers, water conservancy projects, communication base stations, radar stations, satellite ground stations, and navigation signs.

[0015] In an alternative embodiment of the present specification, the method further includes: The vehicle includes at least one of the following: vehicles, drones, and cable cars.

[0016] In a second aspect, an embodiment of the present application further provides a DEM accuracy improvement device based on multi-source data fusion, and the device is used to implement the method steps in the first aspect.

[0017] In a third aspect, an embodiment of the present application further provides an electronic device, including: a processor; and a memory arranged to store computer-executable instructions, which when executed cause the processor to execute the method steps described in the first aspect.

[0018] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium storing one or more programs, which when executed by an electronic device including a plurality of application programs, cause the electronic device to execute the method steps described in the first aspect.

[0019] The above at least one technical solution adopted in the embodiments of the present application can achieve the following beneficial effects: On the one hand, the method in the present application examines the characteristics of the original data from different sources, and makes choices when determining the ground control points for calibrating the DEM to-be-determined model, so that relatively stable factors such as geomorphic features can play a greater role. And during the modeling process, data that can represent at least one of geomorphic features and surface features will still be used, which is conducive to more comprehensively reflecting the actual situation. On the other hand, the characteristics shown by the second reference area are also adopted when determining the ground control points. The second reference area can reflect the impact of human activities on geomorphic features and surface features. The ground control points based on it can improve the model accuracy and also reflect the impact of the environment on human activities, making the model obtained through the present application more targeted when applied online. Especially when the model obtained through the present application is applied in scenarios such as risk prediction, the accuracy of the prediction results can be improved. That is to say, the method in the present application can not only achieve the fusion between multi-source data through technical means, but also achieve the fusion between the model and the application scenario, improving the use effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings described herein are used to provide a further understanding of the present application, and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application, and do not constitute an improper limitation to the present application. In the drawings: Figure 1 is a process schematic diagram of a method for improving the accuracy of DEM based on multi-source data fusion provided by an embodiment of this specification; Figure 2 is a structural schematic diagram of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0021] The present invention will be further described in detail below in conjunction with the specific embodiments and the accompanying drawings. Similar elements in different embodiments are denoted by related similar element numbers. In the following embodiments, many detailed descriptions are provided to enable a better understanding of the present application. However, those skilled in the art can easily recognize that some of the features can be omitted in different situations, or can be replaced by other elements, materials, or methods. In some cases, some operations related to the present application are not shown or described in the specification, in order to avoid overwhelming the core part of the present application with excessive descriptions. For those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations based on the descriptions in the specification and the general technical knowledge in the art.

[0022] In addition, the features, operations, or characteristics described in the specification can be combined in any appropriate manner to form various embodiments. At the same time, the steps or actions in the method description can also be reordered or adjusted in a manner obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for clearly describing a certain embodiment, and do not mean that they are the necessary sequences, unless it is stated that a certain sequence must be followed.

[0023] The serial numbers assigned to the components in this document, such as "first", "second", etc., are only used to distinguish the described objects and do not have any sequential or technical meaning. And the "connection" and "coupling" mentioned in this application, unless otherwise specified, both include direct and indirect connection (coupling).

[0024] The technical solutions provided by the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings.

[0025] As Figure 1 shown, the method for improving the DEM accuracy based on multi-source data fusion in this specification includes the following steps: S100: Obtain the original data of the area to be processed.

[0026] The original data in this specification is multi-source, that is, multi-source data. In the related art, data that can be used to generate DEM, regardless of its source, can be used as the original data in this specification under the condition of permission.

[0027] According to the nature of the characteristics represented by the data, the original data in this specification is divided into: geomorphic measurement data, surface mapping data, and separately listed lidar measurement data.

[0028] Geomorphic survey data is used to characterize geomorphic features more precisely (geomorphic features such as rocks, soil, and rivers, which are inherent and change little over time). Generally, it is also more difficult to obtain geomorphic survey data. In an optional embodiment of this specification, the geomorphic survey data includes at least one of the following: total station survey data, ground triangulation data, and leveling data.

[0029] Surface mapping data is used to characterize surface features (such as the vegetation coverage situation of surface features). In some cases, surface mapping data can also be used to a certain extent to characterize geomorphic features, and the acquisition method of surface mapping data is relatively flexible. In an optional embodiment of this specification, the surface mapping data includes at least one of the following: surface unmanned aerial vehicle mapping data (which can only characterize surface features), and remote sensing mapping data (which can characterize both surface features and geomorphic features, but with limited accuracy).

[0030] LiDAR measurement data can more precisely represent both surface features and geomorphic features, but the two are usually fused together, and separating the two may cause a certain degree of error.

[0031] In addition, in other optional embodiments, before these collected mapping data become raw data, they may also undergo processing such as cleaning and redundancy removal, which will not be elaborated here.

[0032] S102: Divide a first reference area and a second reference area on the area to be processed.

[0033] The area to be processed in this specification is the area that the DEM target model to be obtained should cover.

[0034] The first reference area is the area where the geomorphic survey data belongs. Due to the limitations of survey conditions, some places in the area to be processed are inaccessible by humans, or for cost considerations, it is impossible to traverse the entire area to be processed. This makes the first reference area not equivalent to the area to be processed. In some cases, the first reference area can be a subset of the area to be processed. In some cases, the first reference area and the area to be processed have an intersection, but the area to be processed cannot completely cover the entire first reference area.

[0035] In an alternative embodiment of this specification, the process of determining the first reference area may be as follows: Determine the coordinate points to which the geomorphic measurement data belongs (for example, how many degrees east longitude and how many degrees north latitude, or it can also be achieved through self-modeling). For each coordinate point, determine its weight value. The weight value is negatively correlated with the average distance between it and other surrounding coordinate points, and positively correlated with the number of other surrounding coordinate points. For example, if three points A, B, and C form an acute triangle, then these three points are each other's surrounding points. If these three points can be connected in a straight line in the order of A, B, and C, or the angle with B as the vertex is acute, then A and C cannot be regarded as surrounding each other, while for B, both A and C are in its surrounding. For each coordinate point, draw a circle with its corresponding target radius. After all coordinate points have drawn circles, the largest area outlined by these circles is the first reference area. The target radius corresponding to a certain coordinate point is positively correlated with its weight value.

[0036] The second reference area in this specification is the area in the area to be processed where there are artificial institutions and / or where vehicle passage is allowed. In an alternative embodiment of this specification, artificial institutions such as wind turbines, high-voltage power towers, water conservancy projects, communication base stations, radar stations, satellite ground stations, and navigation signs. Areas where vehicle passage is allowed such as roads, bridges, tunnels, and cableways; vehicles such as cars, drones, and cable cars. Nowadays, the low-altitude economy has begun to be piloted in some cities in our country, and the technical solutions in this specification conform to the development direction of the low-altitude economy.

[0037] The second reference area can be obtained through drawings such as construction drawings and planning drawings, or artificial experience can also be combined during the process of determining the second reference area. This also enables the technical solutions in this specification to be used for predicting possible risks after completion before the second reference area planning is completed. It is beneficial to the expansion of application scenarios.

[0038] It can be seen that the second reference area in this specification is not naturally formed, but is formed due to human activities. This makes it very likely that there are people in the second reference area, and also makes the second reference area have a higher value and is more worthy of protection. It is conceivable that in the case where the second reference area includes a road, if an event such as a landslide occurs near the road, it is very likely to affect traffic safety. Especially in scenarios such as road risk prediction and road supervision and management, the situation of the second reference area is worthy of attention.

[0039] S104: Fit the first reference area and the second reference area to obtain a first available area and a second available area.

[0040] The fitting in this step can be carried out by aligning coordinates.

[0041] The first available area is an area where the first reference area and the second reference area overlap, and the second available area is an area of ​​the first reference area that does not overlap with the second reference area.

[0042] The subsequent steps will process the original data of the first available area and the second available area. On the one hand, based on the situation of the second reference area, some geomorphological measurement data will be eliminated, which is equivalent to "data cleaning" achieved in combination with the second reference area, making the geomorphological measurement data more targeted. In the scenario where the DEM needs to reflect the correlation between different locations in the same area (for example, the impact of landslides near the road on the road), this "data cleaning" can more clearly reflect this correlation. And weakening the relationship between those locations with less correlation is conducive to the protrusion of features.

[0043] In some cases, there may be an intersection between the first reference area and the second reference area, but the two do not overlap. There may also be a situation where the two completely overlap. If the two do not overlap at all, it can be considered to redefine the second reference area based on manual experience, or to supplement the survey and mapping to update the original data.

[0044] S106: Determine ground control points of the first usable area and the second usable area based on the geomorphic measurement data.

[0045] Ground Control Points (GCPs) are key reference points used in the field of Geographic Information Systems (GIS, such as ArcGIS, QGIS) and remote sensing to improve the accuracy of geographic data. They are points on the ground with precisely known coordinates and are usually used for calibration, verification, and accuracy improvement of geographic data.

[0046] The ground control points in this specification are mainly used for the correction of DEM, so the rationality of the ground control points will affect the effect of DEM. In the related technology, the technical means of determining ground control points based on geomorphic measurement data are applicable to this specification when conditions permit. For example, the ground control point determination script provided by GIS software; another example is the technical means of determining ground control points based on manual experience.

[0047] GIS software is a technical means that can use the regularity between the existing and surveyed landforms and surface features data to build models, and can be regarded as a technical means to apply the natural laws contained in landforms and surface features. Since the technical solution in this specification is based on GIS software, the natural laws it applies will also be reflected in the modeling process involved in this application, and will also be reflected in the target model obtained in this application.

[0048] The goal to be achieved in this step is to make the density of ground control points in the first available area greater than that in the second available area, so as to further enhance the distinctness of the influence of the interaction between regions. The ground control points in the first available area are used for calibration. The more ground control points there are in the first available area, the better the calibration effect. However, it is not the case that the more ground control points for calibration, the better. If all the ground control points in the entire area to be processed are used for calibration, the interaction relationship between the landform, the surface and artificial institutions, roads will be weakened. The technical means in this specification aim to improve the accuracy of the DEM on the one hand, and on the other hand, to reflect this interaction relationship for the online use of the model.

[0049] If the goal cannot be achieved after determining the ground control points, it can be achieved by adjusting the parameters of the GIS software. Or the ground control points can be determined manually based on artificial experience. In addition, this specification also proposes a more reasonable method for determining ground control points, which will be introduced in subsequent alternative embodiments.

[0050] The technical solutions in this specification mainly examine the data in the first available area, aiming to pursue the correlation between regions such as landform, surface conditions, artificial institutions, and roads. The data in the second available area is not unavailable for modeling. However, excessive use of it for calibration may dilute this correlation. In the current low-altitude economy scenarios being piloted in our country, safety and low risk should still be the main considerations. Emphasizing this correlation is conducive to risk prediction and thus ensures safety. The method in this specification can, through technical means, reflect this correlation in the selection process of ground control points, and thus improve the application conditions for scenarios that emphasize safety such as the low-altitude economy.

[0051] S108: Use the DEM obtained from the original data by the GIS software as the model to be determined.

[0052] Modeling based on data is a basic function of GIS software. In actual operation, the original data can be imported into the GIS software and the operations can be carried out step by step according to the instructions provided by the software system. The model to be determined obtained through this step also has a certain degree of accuracy and usability, but the use effect will be improved after calibration.

[0053] S110: Use each ground control point in the first available area to calibrate the model to be determined to obtain the target model.

[0054] The GIS software comes with a script for the calibration program. In this step, by importing the coordinates of the ground control points obtained in the previous step into the GIS software, calibration can be automatically achieved, and thus a target model with improved accuracy can be obtained.

[0055] On the one hand, the method in this application examines the characteristics of the original data from different sources, and makes a selection when determining the ground control points for calibrating the DEM to-be-determined model, so that relatively stable factors such as geomorphic features can play a greater role. And during the modeling process, data that can represent at least one of the geomorphic features and surface features will still be used, which is conducive to more comprehensively reflecting the actual situation. On the other hand, the characteristics shown by the second reference area are also adopted when determining the ground control points. The second reference area can reflect the impact of human activities on geomorphic features and surface features. The ground control points based on it can improve the model accuracy while also reflecting the impact of the environment on human activities, making the model obtained through this application more targeted when applied online. Especially when the model obtained through this application is applied in scenarios such as risk prediction, the accuracy of the prediction results can be improved. That is to say, the method in this application can not only achieve the fusion of multi-source data through technical means, but also achieve the fusion of the model and the application scenario, improving the usage effect.

[0056] Now, in the optional embodiments of this specification, how to more reasonably determine the ground control points will be introduced.

[0057] In an optional embodiment of this specification, the process of determining the ground control points can be: based on the geomorphic measurement data, determine the ground control points of the first available area as the first to-be-determined points (this step can use GIS software or manual experience to determine the first to-be-determined points). Based on the geomorphic measurement data, determine the ground control points of the second available area as the second to-be-determined points (this step can use GIS software or manual experience to determine the second to-be-determined points).

[0058] Then, when the number of the second to-be-determined points is greater than the number of the first to-be-determined points, based on at least one of the lidar measurement data and surface mapping data of the first available area, determine the ground control points of the first available area as the third to-be-determined points. Although the lidar measurement data and surface mapping data are confused by surface attachments, they can also reflect the geomorphic situation to a certain extent. The third to-be-determined points determined thereby can also represent the geomorphology to a certain extent.

[0059] After that, traverse the third to-be-determined points. Among the third to-be-determined points, those with a distance greater than a preset distance threshold from the adjacent first to-be-determined points (these points usually have certain characteristics and have not been observed by measurement means such as total station. For example, these points may be more densely vegetated relative to the surrounding vegetation and may be hidden geographical changes) are added to the first to-be-determined points until the density of the ground control points in the first available area is greater than the density of the ground control points in the second available area. Optionally, traverse the third to-be-determined points starting from a position close to the second reference area.

[0060] In a further optional embodiment, the distance threshold is positively correlated with the ratio of the areas of the first available area and the second available area respectively. The larger this ratio is, it indicates that the area of the first available area is larger and the calibration points are sufficient. Increasing the distance threshold is beneficial to reducing the selected points and making the selected points close to the second reference area, reducing the resource consumption of subsequent calibration steps while improving the representation of the relevance between the area to be processed and the second reference area.

[0061] In addition, in a further optional embodiment of this specification, when the surface mapping data used includes fitted remote sensing mapping data, the third to-be-determined points are those among the ground control points of the first available area determined based on the lidar measurement data and the surface mapping data of the first available area, whose position similarity with the nearest first to-be-determined point is less than a preset similarity threshold. Since the accuracy of remote sensing mapping data is low and its ability to represent features is limited, the idea of adding the third to-be-determined points at this time should be to make the ground control points as scattered as possible to avoid the situation of reduced global representation ability caused by over-concentration of ground control points. The position similarity in this specification is positively correlated with at least one of the following similarities in addition to reflecting the similarity degree of coordinates: slope and aspect similarity, terrain undulation degree similarity, terrain type (such as hilly, karst landform) similarity, hydrological feature similarity, historical disaster situation similarity.

[0062] The fitted remote sensing mapping data in this specification is obtained by fitting the remote sensing mapping data collected within a historical time period (including at least one vegetation growth cycle, such as one year). The fitting process performed on the remote sensing mapping data is used to remove the ground control points in the remote sensing mapping data that change significantly according to seasonal rules. In this way, even though the accuracy of the remote sensing mapping data is low, it can provide two aspects of data, more information and more time characteristics, and the method in this specification can effectively utilize this time characteristic.

[0063] In some cases, it may also occur that after traversing the third to-be-determined point, the density of the ground control points in the first available area still cannot meet the requirement of being greater than the density of the ground control points in the second available area. In view of this situation, in an optional embodiment of this specification, the second available area is divided into several sub-areas at a preset step size, perpendicular to the distribution direction of the artificial mechanism or perpendicular to the direction allowing vehicle passage. The sub-areas obtained through this step are as scattered as possible, which is conducive to improving the calibration accuracy.

[0064] Traverse the sub-areas, and add the sub-area with the maximum distance from the first available area adjacent to the second available area to the first available area until the density of the ground control points in the first available area is greater than the density of the ground control points in the second available area. This will result in a reduction in the area of the second available area. In some extreme cases, it may even incorporate the entire second available area into the first available area.

[0065] In addition, when the second reference area includes an area allowing vehicle passage, the step size is positively correlated with the number of bends included in the vehicle passage channel (the presence of vehicle passage indicates a lower anti-risk ability. If a risk accident occurs, the losses may be very large, even resulting in casualties. This step aims to divide as much of the second available area as possible into the first available area to increase the calibration range and improve the overall investigation of the second reference area) and negatively correlated with the average turning angle of each bend (a small turning angle leads to a higher probability of a risk accident, so it is necessary to improve the overall investigation of the second reference area).

[0066] When the second reference area includes an artificial mechanism, the step size is negatively correlated with the number of artificial mechanisms. Taking a wind turbine as an example, the fewer the number of wind turbines, the worse the ground construction conditions. If a risk accident occurs, the difficulty of rescuing it is also greater. Increasing the step size is conducive to expanding the first available area and improving the ability to characterize it, which is beneficial for both risk prediction and rescue.

[0067] In addition, in a further optional embodiment, when the second reference area includes an area allowing vehicle passage, predict the usage frequency of the vehicle for the second reference area (this usage frequency can be characterized by information such as traffic flow). If the usage frequency is greater than a preset frequency threshold (which can be an empirical value), then after calibrating the to-be-determined model using the ground control points in the first available area, use the ground control points in the second available area for further calibration to obtain the target model.

[0068] The calibration that separately uses the first available area and the second available area in sequence is different from the "one-time" calibration that mixes the two together. The former first examines the data in the first available area, mainly pursuing the correlation between areas such as landforms, surface conditions, and roads. Then, the target model obtained through the former can better reflect this correlation, laying a foundation for online use. For the latter, this "one-time" calibration is more difficult to reflect this correlation.

[0069] Figure 2 is a schematic structural diagram of an electronic device according to an embodiment of the present application. Please refer to Figure 2 , at the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. Among them, the memory may include a memory, such as a high-speed random access memory (Random-Access Memory, RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory, etc. Of course, the electronic device may also include other hardware required for other services.

[0070] The processor, network interface, and memory can be interconnected through an internal bus, and the internal bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, Figure 2 only a bidirectional arrow is used in

[0071] The memory is used to store programs. Specifically, the program may include program code, and the program code includes computer operation instructions. The memory may include a memory and a non-volatile memory, and provide instructions and data to the processor.

[0072] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a DEM accuracy improvement device based on multi-source data fusion at the logical level. The processor executes the program stored in the memory and is specifically used to execute any one of the aforementioned DEM accuracy improvement methods based on multi-source data fusion.

[0073] The above is as described in the present application Figure 1A method for improving the accuracy of DEM based on multi-source data fusion disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by the integrated logic circuit of the hardware in the processor or by instructions in the form of software. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. It can implement or execute various methods, steps, and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in combination with the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or completed by a combination of hardware and software modules in the decoding processor. The software module may be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. This storage medium is located in the memory, and the processor reads the information in the memory and combines its hardware to complete the steps of the above method.

[0074] The electronic device can also execute Figure 1 a method for improving the accuracy of DEM based on multi-source data fusion, and implement Figure 1 the functions of the illustrated embodiment. The embodiments of the present application will not be elaborated herein.

[0075] The embodiments of the present application also propose a computer-readable storage medium that stores one or more programs. The one or more programs include instructions that, when executed by an electronic device including a plurality of application programs, execute any of the foregoing methods for improving the accuracy of DEM based on multi-source data fusion.

[0076] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0077] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0078] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0079] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or the combination of blocks.

[0080] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0081] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash RAM. The memory is an example of computer-readable media.

[0082] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0083] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0084] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0085] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for improving the accuracy of DEM based on multi-source data fusion, characterized in that, The method includes: Obtaining the original data of the area to be processed; the original data includes: geomorphic survey data, lidar survey data, and surface mapping data; Dividing a first reference area and a second reference area on the area to be processed; the first reference area is the area to which the geomorphic survey data belongs; the second reference area is the area in the area to be processed where there are artificial institutions and / or vehicle passage is allowed; Fitting the first reference area and the second reference area to obtain a first available area and a second available area; the first available area is the overlapping area of the first reference area and the second reference area; the second available area is the area in the first reference area that does not overlap with the second reference area; Based on the geomorphic survey data, determining the ground control points of the first available area and the second available area respectively, such that the density of the ground control points in the first available area is greater than the density of the ground control points in the second available area; Taking the DEM obtained based on the original data using GIS software as a to-be-determined model; Using each ground control point in the first available area to correct the to-be-determined model to obtain a target model.

2. The method according to claim 1, wherein Based on the geomorphic survey data, determining the ground control points of the first available area and the second available area respectively, includes: Based on the geomorphic survey data, determining the ground control points of the first available area as first to-be-determined points; Based on the geomorphic survey data, determining the ground control points of the second available area as second to-be-determined points; In the case where the number of the second to-be-determined points is greater than the number of the first to-be-determined points, based on at least one of the lidar survey data and the surface mapping data of the first available area, determining the ground control points of the first available area as third to-be-determined points; Traversing the third to-be-determined points, adding those with a distance greater than a preset distance threshold from their neighboring first to-be-determined points to the first to-be-determined points until the density of the ground control points in the first available area is greater than the density of the ground control points in the second available area.

3. The method according to claim 2, wherein The method further includes: The distance threshold is positively correlated with the ratio of the areas of the first available area and the second available area respectively.

4. The method according to claim 2, wherein The method further includes: In the case where the adopted surface mapping data includes fitted remote sensing mapping data, the third to-be-determined points are those among the ground control points of the first available area determined based on the lidar survey data and the surface mapping data of the first available area, and the position similarity with the nearest first to-be-determined point is less than a preset similarity threshold; The fitted remote sensing mapping data is obtained by fitting the remote sensing mapping data collected in a historical time period. The fitting of the remote sensing mapping data is used to remove the ground control points in the remote sensing mapping data that change significantly according to seasonal laws.

5. The method according to claim 2, wherein The method further includes: If after traversing the third to-be-determined point, it is still impossible to satisfy that the density of ground control points in the first available area is greater than the density of ground control points in the second available area, then divide the second available area into several sub-areas at a preset step size, either perpendicular to the trend of the distribution of the artificial mechanism or perpendicular to the trend allowing the vehicle to pass; Traverse the sub-areas, and add the one with the maximum distance from the sub-areas to the first available area adjacent to the second available area to the first available area until the density of ground control points in the first available area is greater than the density of ground control points in the second available area.

6. The method according to claim 5, characterized in that The method further includes: When the second reference area includes an area allowing the vehicle to pass, the step size is positively correlated with the number of bends included in the passage allowing the vehicle to pass and negatively correlated with the average turning angle of each of the bends; When the second reference area includes an artificial mechanism, the step size is negatively correlated with the number of artificial mechanisms.

7. The method according to claim 1, wherein The method further includes: When the second reference area includes an area allowing the vehicle to pass, predict the usage frequency of the vehicle for the second reference area; If the usage frequency is greater than a preset frequency threshold, after correcting the to-be-determined model with each ground control point in the first available area, further correct it with each ground control point in the second available area to obtain a target model.

8. The method according to claim 1, wherein The method further includes: The geomorphic survey data includes at least one of the following: total station survey data, ground triangulation data, and leveling data.

9. The method according to claim 1, wherein The method further includes: The artificial mechanism includes at least one of the following: wind turbine, high-voltage power tower, water conservancy project, communication base station, radar station, satellite ground station, and navigation sign.

10. The method according to claim 1, wherein The method further includes: The vehicle includes at least one of the following: vehicle, unmanned aerial vehicle, and cable car.

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