A method for improving the accuracy of DEM based on multi-source data fusion

Abstract

The present application discloses a method for improving the accuracy of DEM based on multi-source data fusion. On the one hand, the method in the present application examines the respective characteristics of the original data from different sources, and makes choices when determining the ground control points for calibrating the to-be-determined DEM model. The second reference area can reflect the impact of human activities on landform 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, it can improve the accuracy of the prediction results. That is to say, the method in the present application can not only achieve the fusion of multi-source data through technical means, but also achieve the fusion between the model and the application scenario, improving the use effect.

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 entity 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 beneficial 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, not 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:

[0007] 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:

[0008] Obtain the original data of the area to be processed; the original data includes: geomorphic measurement data, lidar measurement data, and surface mapping data;

[0009] 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 passage is allowed;

[0010] Fit 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;

[0011] Based on the geomorphic measurement data, determine the ground control points of the first available area and the second available area respectively, so 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;

[0012] Take the DEM obtained based on the original data using GIS software as a to-be-determined model;

[0013] Use the ground control points in the first available area to correct the to-be-determined model to obtain a target model.

[0014] In an optional embodiment of the present specification, determining the ground control points of the first available area and the second available area respectively based on the geomorphic measurement data includes:

[0015] Based on the geomorphic measurement data, determine the ground control points of the first available area as the first to-be-determined points;

[0016] Based on the geomorphic measurement data, determine the ground control points of the second available area as the second to-be-determined points;

[0017] 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, determine the ground control points of the first available area as the third to-be-determined points;

[0018] Traverse the third to-be-determined points, and add those with a distance greater than a preset distance threshold from their adjacent 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.

[0019] In an optional embodiment of the present specification, the method further includes:

[0020] The distance threshold is positively correlated with the ratio of the areas of the first available area and the second available area respectively.

[0021] In an optional embodiment of the present specification, the method further includes:

[0022] In the case where the surface mapping data adopted includes fitted remote sensing mapping data, the third point to be determined is 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, and the position similarity with the nearest first point to be determined is less than a preset similarity threshold;

[0023] The fitted remote sensing mapping data is obtained by fitting the remote sensing mapping data collected during 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.

[0024] In an optional embodiment of the present specification, the method further includes:

[0025] If after traversing the third point to be determined, 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, then the second available area is divided into several sub-areas at a preset step size, either perpendicular to the trend of the artificial mechanism distribution or perpendicular to the trend allowing vehicle passage;

[0026] Traverse the sub-areas, and add the sub-area with the largest 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.

[0027] In an optional embodiment of the present specification, the method further includes:

[0028] 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 and negatively correlated with the average turning angle of each bend;

[0029] When the second reference area includes an artificial mechanism, the step size is negatively correlated with the number of artificial mechanisms.

[0030] In an optional embodiment of the present specification, the method further includes:

[0031] When the second reference area includes an area allowing vehicle passage, predict the usage frequency of the vehicle for the second reference area;

[0032] If the usage frequency is greater than a preset frequency threshold, after correcting the to-be-determined model using each ground control point in the first available area, further correct it using each ground control point in the second available area to obtain a target model.

[0033] In an optional embodiment of this specification, the method further includes:

[0034] The geomorphic survey data includes at least one of the following: total station survey data, ground triangulation data, and leveling data.

[0035] In an optional embodiment of this specification, the method further includes:

[0036] The artificial structures include 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 markers.

[0037] In an optional embodiment of this specification, the method further includes:

[0038] The vehicles include at least one of the following: vehicles, drones, and cable cars.

[0039] 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.

[0040] In a third aspect, an embodiment of the present application further provides an electronic device, including:

[0041] a processor; and

[0042] a memory arranged to store computer-executable instructions, and the executable instructions, when executed, cause the processor to execute the method steps described in the first aspect.

[0043] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, and the computer-readable storage medium stores one or more programs. When the one or more programs are executed by an electronic device including a plurality of application programs, the electronic device is caused to execute the method steps described in the first aspect.

[0044] The above at least one technical solution adopted by the embodiments of the present application can achieve the following beneficial effects:

[0045] On the one hand, the method in this application examines the respective characteristics of the original data from different sources, and when determining the ground control points for correcting the DEM pending model, it makes choices, so that the relatively stable factors such as landform characteristics can play a greater role. In the process of modeling, data that can characterize at least one of the landform characteristics and surface characteristics will continue to be used, which is conducive to a more comprehensive reflection of the actual situation. On the other hand, when determining the ground control points, the characteristics exhibited by the second reference area are also adopted. The second reference area can reflect the impact of human activities on landform characteristics and surface characteristics. While the ground control points based on it can improve the accuracy of the model, it can also reflect the impact of the environment on human activities, making the model obtained by this application more targeted when applied online. In particular, when the model obtained by this application is applied in a scenario such as risk prediction, the accuracy of the prediction results can be improved. In other words, the method in this 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

[0046] 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 on the present application. In the drawings:

[0047] Figure 1 A schematic diagram of a DEM accuracy improvement method based on multi-source data fusion provided in an embodiment of this specification;

[0048] Figure 2 This is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0049] The present invention will be further described in detail below with reference to the accompanying drawings by way of specific embodiments. Similar elements in different embodiments are numbered with associated similar elements. In the following embodiments, many detailed descriptions are provided to enable the present application to be better understood. However, those skilled in the art will readily appreciate that some of the features may be omitted under different circumstances, or may 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. This is to avoid overwhelm the core of the present application with excessive descriptions, and for those skilled in the art, it is not necessary to describe these related operations in detail. They will fully understand the related operations based on the description in the specification and the general technical knowledge in the art.

[0050] In addition, the features, operations, or characteristics described in the specification can be combined in any suitable 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 drawings are only for clearly describing a certain embodiment and do not mean a necessary sequence, unless it is stated otherwise that a certain sequence must be followed.

[0051] The serial numbers assigned to the components in this text, such as "first", "second", etc., are only used to distinguish the objects described 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).

[0052] The following will describe in detail the technical solutions provided by the embodiments of this application in conjunction with the accompanying drawings.

[0053] As Figure 1 shown, the method for improving the accuracy of DEM based on multi-source data fusion in this specification includes the following steps:

[0054] S100: Obtain the original data of the area to be processed.

[0055] 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.

[0056] According to the nature of the characteristics represented by the data, the original data in this specification is divided into: geomorphic survey data, surface mapping data, and separately listed lidar survey data.

[0057] Geomorphic survey data is used to more accurately represent geomorphic features (geomorphic features such as rocks, soil, and rivers, which are inherent and have a relatively small degree of change over time). The acquisition of geomorphic survey data is usually also more difficult. 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.

[0058] Surface mapping data is used to represent surface features (such as the vegetation coverage situation, etc.). In some cases, surface mapping data can also be used to represent geomorphic features to a certain extent, 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 drone mapping data (which can only represent surface features), remote sensing mapping data (which can represent both surface features and geomorphic features, but with limited accuracy).

[0059] Lidar measurement data can accurately represent both surface features and geomorphic features. However, the two are usually combined, and separating them may cause a certain degree of error.

[0060] In addition, in other alternative embodiments, before these collected surveying and mapping data become raw data, they may also undergo processes such as cleaning and redundancy removal, which will not be elaborated here.

[0061] S102: Divide the area to be processed into a first reference area and a second reference area.

[0062] The area to be processed in this specification is the area that the DEM target model to be obtained should cover.

[0063] The first reference area is the area where the geomorphic measurement data belongs. Due to the limitations of surveying 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.

[0064] In an alternative embodiment of this specification, the process of determining the first reference area can be: determining 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 by 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 is 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 each other's surrounding points, 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.

[0065] The second reference area in this specification is the area in the area to be processed where there are artificial institutions and / or areas allowing vehicles to pass. 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 allowing vehicles to pass 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 are in line with the development direction of the low-altitude economy.

[0066] The second reference area can be obtained from drawings such as construction drawings and planning diagrams, or manual experience can be used to determine the second reference area. This also allows the technical solution in this specification to be used to predict risks that may be faced after completion of the project before the second reference area planning is completed, which is conducive to the expansion of application scenarios.

[0067] It can be seen that the second reference area in this specification is not formed naturally, but is formed by human activities. This makes it very likely that there are people in the second reference area, which also makes the second reference area have higher value and more worthy of protection. It is conceivable that if the second reference area includes a road, if an event such as a landslide occurs near the road, it is very likely to affect driving safety. Especially in the scenarios of road risk prediction and road supervision and management, the situation in the second reference area deserves attention.

[0068] S104: Fitting the first reference area and the second reference area to obtain a first usable area and a second usable area.

[0069] The fitting in this step can be performed by coordinate alignment.

[0070] 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 in the first reference area that does not overlap with the second reference area.

[0071] Subsequent steps will involve processing the raw data for both the first and second available areas. On the one hand, some geomorphological data will be removed based on the second reference area. This is equivalent to "data cleaning" achieved by combining the second reference area, making the geomorphological data more targeted. In scenarios where the DEM needs to reflect the interrelationships between different locations in the same area (for example, the impact of a landslide near a road on the road), this "data cleaning" can more clearly demonstrate these interrelationships. Furthermore, the interrelationships between locations with less correlation are weakened, which helps to highlight the features.

[0072] In some cases, the first reference area and the second reference area may intersect but not overlap. Alternatively, they may completely overlap. If they do not overlap at all, consider re-determining the second reference area based on manual experience or performing additional surveying and mapping to update the original data.

[0073] S106: Determine ground control points of the first usable area and the second usable area based on the geomorphological measurement data.

[0074] Ground Control Points (GCPs) are critical reference points used in Geographic Information Systems (GIS, such as ArcGIS and QGIS) and remote sensing to improve the accuracy of geographic data. They are points on the ground with precisely known coordinates and are commonly used for calibration, verification, and accuracy improvement of geographic data.

[0075] The ground control points (GCPs) used in this specification are primarily used for DEM correction. The rationality of GCPs will affect the effectiveness of the DEM. Related technologies that can determine GCPs based on geomorphological data are applicable to this specification, as conditions permit. Examples include GCP determination scripts included with GIS software and GCP determination techniques based on manual experience.

[0076] GIS software is a technical means for modeling by leveraging the regularities between existing, surveyed landforms and surface features. It can be considered a technical means for applying the natural laws inherent in these landforms and surface features. Because the technical solutions described in this specification are based on GIS software, the natural laws it applies will also be reflected in the modeling process involved in this application and in the target model obtained in this application.

[0077] The goal to be achieved in this step is to make the density of ground control points in the first available area greater than the density of ground control points in the second available area, so as to further improve the distinctness of the impact of the interaction between areas. The ground control points in the first available area are used for correction. The more ground control points there are in the first available area, the better the correction effect. However, the more ground control points used for correction, the better. If all ground control points in the entire area to be processed are used for correction, the interaction between the landform, the surface and artificial structures and roads is weakened. The technical means in this manual, on the one hand, aim to improve the accuracy of DEM, and on the other hand, also reflect this interaction relationship in preparation for the online use of the model.

[0078] If the goal cannot be achieved after determining the ground control points, the goal can be achieved by adjusting the parameters of the GIS software. Alternatively, the ground control points can be manually determined based on human experience. In addition, this specification also proposes a more reasonable method for determining ground control points, which will be introduced in subsequent optional embodiments.

[0079] The technical solutions in this specification mainly examine the data in the first available area, with the main focus on pursuing the correlation between the landform, surface conditions, artificial structures, roads and other areas. The data in the second available area is not unusable for modeling. However, excessive use of it for calibration may dilute this correlation. In the current low-altitude economy scenarios piloted in our country, safety and low risk should still be the main priorities. 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 process of selecting ground control points, thereby improving the application conditions for scenarios that emphasize safety, such as the low-altitude economy.

[0080] S108: Use the DEM obtained from the original data based on GIS software as the to-be-determined model.

[0081] 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 to-be-determined model obtained through this step also has a certain degree of accuracy and usability, but its performance will be improved after calibration.

[0082] S110: Use each ground control point in the first available area to calibrate the to-be-determined model to obtain the target model.

[0083] 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.

[0084] The method in this application examines the characteristics of the original data from different sources on the one hand. When determining the ground control points for calibrating the DEM to-be-determined model, a selection is made, enabling relatively stable factors such as landform features to play a greater role. And data that can represent at least one of the landform features and surface features is still used during the modeling process, which is conducive to more comprehensively reflecting the actual situation. On the other hand, the characteristics shown in the second reference area are also used when determining the ground control points. The second reference area can reflect the impact of human activities on landform 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 this application more targeted when applied online. Especially when the model obtained through this application is used 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 between the model and the application scenario, improving the usage effect.

[0085] Now, in the optional embodiments of this specification, how to more reasonably determine ground control points will be introduced.

[0086] In an optional embodiment of this specification, the process of determining ground control points may be as follows: Based on the geomorphic measurement data, determine the ground control points in the first available area as the first points to be determined (this step can use GIS software or manual experience to determine the first points to be determined). Based on the geomorphic measurement data, determine the ground control points in the second available area as the second points to be determined (this step can use GIS software or manual experience to determine the second points to be determined).

[0087] Then, when the number of the second points to be determined is greater than the number of the first points to be determined, based on at least one of the lidar measurement data and the surface mapping data in the first available area, determine the ground control points in the first available area as the third points to be determined. Although the lidar measurement data and the surface mapping data are confused by surface attachments, they can also reflect the geomorphic situation to a certain extent. Therefore, the third points to be determined determined thereby can also be representative of the geomorphology to a certain extent.

[0088] After that, traverse the third points to be determined, and add those third points to be determined whose distance from the adjacent first points to be determined is greater than a preset distance threshold (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 than the surrounding vegetation and may be hidden geographical changes) to the first points to be determined 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, start traversing the third points to be determined from a position close to the second reference area.

[0089] 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.

[0090] 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 point is based on the lidar measurement data and the surface mapping data of the first available area to determine, among the ground control points of the first available area, those whose position similarity to 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 characterize features is limited, the idea of adding the third to-be-determined point at this time should be to make the ground control points as scattered as possible to avoid the situation of reduced global characterization ability caused by overly concentrated 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.

[0091] 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 vary 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, with more information and better time characteristics, and the method in this specification can effectively utilize this time characteristic.

[0092] In some cases, it may also occur that after traversing the third to-be-determined points, the density of the ground control points in the first available area still cannot be made greater than the density of the ground control points in the second available area. In response to 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 the vehicle to pass. The sub-areas obtained through this step are as scattered as possible, which is beneficial to improving the calibration accuracy.

[0093] Traverse the sub-areas, and add the sub-area with the largest 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, and in some extreme cases, the entire second available area may even be incorporated into the first available area.

[0094] 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 passage allowing vehicle passage (the presence of vehicle passage indicates a lower risk resistance ability. If a risk accident occurs, the potential losses may be significant, even involving 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 inspection of the second reference area), and is negatively correlated with the average turning angle of each of the bends (a smaller turning angle leads to a higher probability of a risk accident, so it is necessary to improve the overall inspection of the second reference area).

[0095] When the second reference area includes artificial structures, the step size is negatively correlated with the number of artificial structures. 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 beneficial to expanding the first available area and improving the ability to characterize it, which is beneficial for both risk prediction and rescue.

[0096] 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), after calibrating the to-be-determined model using each ground control point in the first available area, use each ground control point in the second available area for further calibration to obtain the target model.

[0097] The calibration performed using the first available area and the second available area in sequence is different from the "one-time" calibration by mixing the two together. The former first examines the data in the first available area, mainly pursuing the correlation between regions such as landforms, surface conditions, and roads. The target model obtained through the former can better reflect this correlation and lay the foundation for online use. For the latter, this "one-time" calibration is more difficult to reflect this correlation.

[0098] 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, such as at least one disk memory, etc. Of course, the electronic device may also include other hardware required for other services.

[0099] The processor, network interface, and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 only a two-way arrow is used in Figure 2 , but it does not mean that there is only one bus or one type of bus.

[0100] Memory, used to store programs. Specifically, the program can include program code, and the program code includes computer operation instructions. The memory can include a memory and a non-volatile memory, and provide instructions and data to the processor.

[0101] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a device for improving the accuracy of DEM 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 foregoing methods for improving the accuracy of DEM based on multi-source data fusion.

[0102] As described above in this 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 the ability to process signals. During implementation, each step of the above method can be completed by the integrated logic circuit in the hardware of the processor or instructions in software form. The above 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 and completed by a hardware decoding processor, or executed and 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.

[0103] [[ID=z]]This 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, which will not be elaborated herein in the embodiments of the present application.

[0104] 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 multiple application programs, execute any of the foregoing methods for improving the accuracy of DEM based on multi-source data fusion.

[0105] 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 completely hardware embodiment, a completely 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 storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0106] 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 flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0107] 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 flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0108] 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 flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0109] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0110] 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 memory (flash RAM). Memory is an example of computer-readable media.

[0111] A computer-readable medium includes permanent and non-permanent, removable and non-removable media and can implement information storage by any method or technology. The 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 technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transitory 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 transitory computer-readable media, such as modulated data signals and carrier waves.

[0112] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0113] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, system or 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 storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0114] The above are only the embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within 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 the to-be-determined model; Using each ground control point in the first available area to correct the to-be-determined model to obtain the target model.

2. The method according to claim 1, characterized in that 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 the first to-be-determined points; Based on the geomorphic survey data, determining the ground control points of the second available area as the 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 the 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 used 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, and the fitting of the remote sensing mapping data is used to remove the ground control points that change significantly according to the seasonal law in the remote sensing mapping data.

5. The method according to claim 2, wherein, The method further includes: If, after traversing the third to-be-determined point, the density of the ground control points in the first available area still cannot be made greater than the density of the ground control points in the second available area, then the second available area is divided into several sub-areas at a preset step size, either perpendicular to the trend of the distribution of the artificial structures or perpendicular to the trend allowing vehicle passage; Traverse the sub-areas, and add the one with the maximum distance from the first available area adjacent to the second available area among the sub-areas 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.

6. The method according to claim 5, wherein The method further includes: 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 and negatively correlated with the average turning angle of each bend; When the second reference area includes artificial structures, the step size is negatively correlated with the number of artificial structures.

7. The method according to claim 1, wherein The method further includes: When the second reference area includes an area allowing vehicle passage, 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 using each ground control point in the first available area, further correct it using 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 structures include 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.

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

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