Silt dam identification method and device, electronic equipment and storage medium

Abstract

The application relates to the technical field of data processing, in particular to a silt dam identification method and device, electronic equipment and a storage medium, wherein the silt dam identification method comprises the following steps: collecting position information and remote sensing images of a silt dam; marking the position and range of the silt dam in the remote sensing images according to the position information; training a pre-constructed target detection model by using the marked remote sensing images, and obtaining the trained target detection model after the training is completed; intercepting remote sensing images of a river network area of a to-be-identified watershed by using river network information of the to-be-identified watershed, inputting the remote sensing images into the trained target detection model, outputting an identification result of the silt dam in the to-be-identified watershed, removing the identification result that is a certain distance away from the river network of the to-be-identified watershed from the identification result of the silt dam, and obtaining a final identification result of the silt dam in the to-be-identified watershed. Thus, the problems of low accuracy, large error and large algorithm calculation amount in automatic identification of the position of the silt dam in the related art are solved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, electronic device and storage medium for identifying silt-retaining dams. Background Technology

[0002] The middle reaches of the Yellow River flow through the Loess Plateau, which is a key area for soil and water conservation. To prevent soil erosion in the Loess Plateau region, large-scale soil and water conservation measures have been implemented since the 1950s, mainly including: (1) controlling slope soil erosion: returning farmland to forest and grassland, afforestation of barren mountains; and building terraces. (2) controlling gully soil erosion: building silt-retaining dams, gully control and land reclamation, etc. Among them, silt-retaining dams refer to dam structures built in gullies at all levels in soil erosion areas for the purpose of intercepting silt and creating silt. It is an effective soil and water conservation engineering measure created by the people of the Loess Plateau region in their long-term struggle against soil erosion, which can both intercept sediment and conserve soil and water, and create silt to create farmland and increase grain production.

[0003] However, as the degree of siltation in silt-retention dams continues to increase, the underlying surface conditions of the dam site and surrounding areas are constantly changing, and since early silt-retention dams were built before the advent of mature digital technology, many silt-retention dams have either no digital information or incomplete information, making it difficult to achieve digital management.

[0004] In related technologies, DEM (Digital Elevation Model) data is used to identify silt-retaining dams over a large area, and then location characteristics are used to narrow down the image recognition range to quickly determine the location of the silt-retaining dam. However, since the degree of siltation and the underlying surface conditions of the dam site and surrounding area change over time, their related characteristics also change accordingly, resulting in errors in the located silt-retaining dam position. Furthermore, the DEM-based identification method depends on the accuracy of the DEM, and its effectiveness in detecting small silt-retaining dams needs to be verified.

[0005] Alternatively, a silt-retention dam database can be constructed by acquiring data and dynamically updated based on remote sensing data for timely detection and maintenance. However, the accuracy of the data acquisition methods cannot be guaranteed, resulting in data with a certain degree of error. Summary of the Invention

[0006] This application provides a method, apparatus, electronic device, and storage medium for identifying silt-retaining dams, in order to solve the problems of low accuracy and large error in the automatic identification of silt-retaining dam locations in related technologies.

[0007] The first aspect of this application provides a method for identifying silt-retaining dams, comprising the following steps: collecting location information and remote sensing images of the silt-retaining dams; marking the location and extent of the silt-retaining dams in the remote sensing images based on the location information; training a pre-constructed target detection model using the marked remote sensing images; obtaining a trained target detection model after training; extracting remote sensing images of the river network area of ​​the watershed to be identified using the river network information of the watershed to be identified; inputting the extracted remote sensing images into the trained target detection model; outputting the identification results of silt-retaining dams in the watershed to be identified; and removing identification results of silt-retaining dams that are located at a certain distance from the river network of the watershed to be identified using the river network information of the watershed to be identified, thereby obtaining the final identification results of silt-retaining dams in the watershed to be identified.

[0008] Optionally, the identification results include the latitude and longitude coordinates of the center point of the silt-retention dam and the length and width of the rectangle in the remote sensing image where the silt-retention dam is located.

[0009] Optionally, the step of using the river network information of the watershed to be identified to extract remote sensing images of the river network area of ​​the watershed to be identified includes: inputting the digital elevation model of the watershed to be identified into the digital river network extraction model, extracting a high-precision digital river network, and obtaining the spatial coordinates of all target points on the river network at preset distances based on the high-precision digital river network; and extracting remote sensing images of the river network area of ​​the watershed to be identified based on the spatial coordinates of all target points.

[0010] Optionally, the step of using the river network information of the watershed to be identified to remove identification results of silt-retaining dams that are outside a certain distance from the river network of the watershed to be identified, and obtaining the final identification result of silt-retaining dams in the watershed to be identified, includes: determining the river network within a preset range based on the spatial coordinates of the multiple target points and the latitude and longitude coordinates of the center point of the silt-retaining dam; calculating the deviation value between the location of the silt-retaining dam and the river segment based on the river segment attributes of the river network and the latitude and longitude coordinates of the center point of the silt-retaining dam; and using statistical methods to remove results in the identification results whose deviation exceeds a deviation threshold, thereby obtaining the final identification result of silt-retaining dams in the watershed to be identified.

[0011] Optionally, marking the location and extent of the silt-retaining dam in the remote sensing image based on the location information includes: selecting all the silt-retaining dams in the remote sensing image one by one using a rectangular frame, and saving the marking information.

[0012] A second aspect of this application provides a method for identifying silt-retaining dams, comprising: acquiring river network information of a watershed to be identified; using the river network information of the watershed to be identified to extract remote sensing images of the river network area of ​​the watershed to be identified; inputting the extracted remote sensing images into a trained target detection model; and outputting the identification results of silt-retaining dams in the watershed to be identified; and using the river network information of the watershed to be identified to remove identification results of silt-retaining dams that are located at a certain distance from the river network of the watershed to be identified, thereby obtaining the final identification results of silt-retaining dams in the watershed to be identified.

[0013] A third aspect of this application provides a silt-retaining dam identification device, comprising: a data acquisition module for acquiring location information of the silt-retaining dam and remote sensing images; a labeling module for labeling the location and extent of the silt-retaining dam in the remote sensing images based on the location information, training a pre-constructed target detection model using the labeled remote sensing images, and obtaining a trained target detection model after training; and a processing module for extracting remote sensing images of the river network area of ​​the watershed to be identified using river network information of the watershed to be identified, inputting the extracted remote sensing images into the trained target detection model, outputting the identification results of the silt-retaining dam in the watershed to be identified, and removing identification results of the silt-retaining dam that are located at a certain distance from the river network of the watershed to be identified using river network information of the watershed to be identified, thereby obtaining the final identification result of the silt-retaining dam in the watershed to be identified.

[0014] A fourth aspect of this application provides a silt-retaining dam identification device, comprising: an acquisition module for acquiring river network information of a watershed to be identified; a cropping module for cropping remote sensing images of the river network area of ​​the watershed to be identified using the river network information of the watershed to be identified, inputting the cropped remote sensing images into the trained target detection model, and outputting the identification results of silt-retaining dams in the watershed to be identified; and a removal module for removing identification results of silt-retaining dams that are located at a certain distance from the river network of the watershed to be identified using the river network information of the watershed to be identified, thereby obtaining the final identification results of silt-retaining dams in the watershed to be identified.

[0015] A fifth aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the silt-retaining dam identification method as described in the above embodiments.

[0016] A sixth aspect of this application provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the silt-retaining dam identification method as described in the above embodiments.

[0017] Therefore, this application has at least the following beneficial effects:

[0018] This application uses location information of silt-retaining dams and remote sensing imagery to mark the approximate range of silt-retaining dams in the remote sensing imagery. Based on the marked remote sensing imagery, a target detection model is trained. The remote sensing imagery of the watershed to be identified is input into the trained target detection model to obtain the latitude and longitude coordinates of the corresponding silt-retaining dam and the length and width of the rectangle in the remote sensing imagery. The model is then combined with the river network information of the watershed to be identified for auxiliary verification, thereby quickly locating and identifying the silt-retaining dams in the watershed, improving the accuracy of automatic identification of silt-retaining dams and reducing errors.

[0019] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0021] Figure 1 This is a flowchart of a silt-retaining dam identification method according to one embodiment of this application;

[0022] Figure 2 Here is a flowchart of a silt-retaining dam identification method according to another embodiment of this application;

[0023] Figure 3 This is an example diagram of a silt-retaining dam identification device according to one embodiment of this application;

[0024] Figure 4 This is an example diagram of a silt-retaining dam identification device according to another embodiment of this application;

[0025] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0026] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0027] The following description, with reference to the accompanying drawings, outlines a method, apparatus, electronic device, and storage medium for identifying silt-retaining dams according to embodiments of this application. Addressing the poor accuracy of automatic silt-retaining dam identification mentioned in the background section, this application provides a method for identifying silt-retaining dams. In this method, a detection model is constructed using silt-retaining dam location information and remote sensing imagery. Based on the trained detection model, the required watershed is analyzed and identified. River network information of the watershed is used for auxiliary verification, thereby quickly locating and identifying the silt-retaining dam in the watershed, improving the accuracy of automatic silt-retaining dam identification and reducing errors. This solves the problems of low accuracy and large errors in automatic silt-retaining dam location identification.

[0028] Specifically, Figure 1 This is a flowchart illustrating a method for identifying silt-retaining dams provided in an embodiment of this application.

[0029] like Figure 1 As shown, the method for identifying silt-retaining dams includes the following steps:

[0030] In step S101, the location information of the silt-retaining dam and remote sensing images are collected.

[0031] The location of the silt-retention dam can be in the Loess Plateau region, and no specific limitation is made here.

[0032] The remote sensing images are derived from high-precision maps, and no specific restrictions are imposed here.

[0033] It is understood that the embodiments of this application collect location information and remote sensing images of silt-retaining dams in order to subsequently mark the approximate range of the corresponding silt-retaining dams.

[0034] In step S102, the location and extent of the silt-retaining dam are marked in the remote sensing image based on the location information. The marked remote sensing image is used to train the pre-constructed target detection model, and the trained target detection model is obtained after the training is completed.

[0035] The pre-built object detection model can be a Faster R-CNN model based on deep learning, and no specific restrictions are made here.

[0036] It is understood that, in this application embodiment, the approximate location and extent of the silt-retaining dam are marked in the remote sensing image based on the location information, and the marked remote sensing image is used to train the target detection model so as to extract the remote sensing image of the area where the silt-retaining dam to be identified is located.

[0037] In this embodiment of the application, marking the location and extent of silt-retaining dams in remote sensing images based on location information includes: selecting all silt-retaining dams in the remote sensing images one by one using a rectangular frame, and saving the marking information.

[0038] It is understood that in this embodiment of the application, all silt-retaining dams within the range are selected by rectangular boxes and marked and saved so as to facilitate subsequent screening of silt-retaining dams.

[0039] In step S103, remote sensing images of the river network area of ​​the watershed to be identified are extracted using the river network information of the watershed to be identified. The extracted remote sensing images are input into the trained target detection model, and the identification results of silt-retaining dams in the watershed to be identified are output. The identification results of silt-retaining dams that are far away from the river network of the watershed to be identified are removed using the river network information of the watershed to be identified, so as to obtain the final identification results of silt-retaining dams in the watershed to be identified.

[0040] The identification results include the latitude and longitude coordinates of the center point of the silt-retention dam and the length and width of the rectangle in the remote sensing image where the silt-retention dam is located.

[0041] It is understood that this application embodiment will use the river network information of the watershed to be identified to extract remote sensing images of the river network area of ​​the watershed to be identified, and input them into the trained target detection model to output the identification results of silt-retaining dams. Based on the river network information of the watershed to be identified, the identification results that are outside a certain distance from the river network of the watershed to be identified will be eliminated. The detection model is constructed through remote sensing images and combined with the river network information of the watershed to be identified for auxiliary verification, thereby quickly locating and identifying the silt-retaining dams in the watershed, improving the accuracy of automatic identification of silt-retaining dams, reducing errors, reducing the number of input images, and reducing the amount of computation.

[0042] In this embodiment of the application, the step of using the river network information of the watershed to be identified to extract remote sensing images of the river network area of ​​the watershed to be identified includes: inputting the digital elevation model of the watershed to be identified into the digital river network extraction model, extracting a high-precision digital river network, and obtaining the spatial coordinates of all target points on the river network at preset distances based on the high-precision digital river network; and extracting remote sensing images of the river network area of ​​the watershed to be identified based on the spatial coordinates of all target points.

[0043] The preset distance can be set according to the actual situation of the river network, and no specific limit is made here.

[0044] It is understood that in this embodiment of the application, the digital elevation model of the watershed to be identified is input into the digital river network extraction model to extract a high-precision digital river network and obtain the spatial coordinates of all target points on the river network at certain intervals, so as to extract remote sensing images of the river network area of ​​the watershed to be identified based on the spatial coordinates of the target points.

[0045] In this embodiment of the application, the identification results of silt-retaining dams that are located at a certain distance from the river network of the watershed to be identified are removed using the river network information of the watershed to be identified, so as to obtain the final identification result of silt-retaining dams in the watershed to be identified. This includes: determining the river network within a preset range based on the spatial coordinates of multiple target points and the latitude and longitude coordinates of the center point of the silt-retaining dam; calculating the deviation value between the position of the silt-retaining dam and the river segment based on the river segment attributes of the river network and the latitude and longitude coordinates of the center point of the silt-retaining dam; and using statistical methods to remove the results in the identification results whose deviation exceeds the deviation threshold, so as to obtain the final identification result of silt-retaining dams in the watershed to be identified.

[0046] The preset range can be a range set by the user, and no specific limitation is made here.

[0047] The deviation threshold can be calculated as the average deviation or set according to the actual situation of the river section; no specific limitation is made here.

[0048] It is understood that, according to the spatial coordinates of multiple target points in the extracted river network information, the approximate location of the silt-retaining dam within the range is determined, and the location of the silt-retaining dam is verified with the assistance of river network and river section attributes and remote sensing images, and errors are eliminated to obtain an accurate silt-retaining dam identification result.

[0049] According to the silt-retaining dam identification method proposed in this application, the location information and remote sensing images of silt-retaining dams are collected. Based on the location information, the approximate location and range of the silt-retaining dam are marked in the remote sensing images. The marked remote sensing images are used to train the target detection model. The remote sensing images of the river network area of ​​the watershed to be identified are extracted using the river network information of the watershed to be identified and input into the trained target detection model. The latitude and longitude coordinates of the center point of the silt-retaining dam and the length and width of the rectangle in which the silt-retaining dam is located in the remote sensing image are output. The silt-retaining dam in the watershed to be identified is identified based on the identification results and the river network information of the watershed to be identified. The detection model is constructed based on the location information of the silt-retaining dam and the remote sensing images, and auxiliary verification is performed in combination with the river network information of the watershed to be identified. This allows for rapid location and identification of the silt-retaining dam in the watershed, improves the accuracy of automatic identification of silt-retaining dams, reduces errors, reduces the number of input images, and reduces the amount of computation.

[0050] It should be noted that the above embodiment trains the target detection model online using labeled remote sensing imagery, then inputs the remote sensing imagery of the watershed to be identified into the model to obtain the recognition result, and combines it with the river network information of the watershed to be identified for auxiliary verification, thereby quickly locating and identifying the silt-retaining dams in the watershed; while the following embodiment directly inputs the remote sensing imagery of the watershed to be identified into the trained target detection model offline to obtain the recognition result, as detailed below:

[0051] Figure 2This is a flowchart illustrating a method for identifying silt-retaining dams provided in an embodiment of this application.

[0052] like Figure 2 As shown, the method for identifying silt-retaining dams includes the following steps:

[0053] In step S201, river network information of the watershed to be identified is obtained.

[0054] It is understood that the embodiments of this application acquire river network information and remote sensing images of the watershed to be identified, so as to obtain the identification result of the watershed to be identified through the target detection model.

[0055] In step S202, remote sensing images of the river network area of ​​the watershed to be identified are extracted using the river network information of the watershed to be identified. The extracted remote sensing images are input into the trained target detection model, and the identification results of silt-retaining dams in the watershed to be identified are output.

[0056] It is understood that the embodiments of this application utilize the river network information of the watershed to be identified to extract remote sensing images of the river network area and input them into the trained target detection model to obtain the identification results of the watershed to be identified, so as to facilitate the subsequent identification of silt-retaining dams in the watershed to be identified.

[0057] In step S203, the river network information of the watershed to be identified is used to remove the identification results of silt-retaining dams that are a certain distance away from the river network of the watershed to be identified, so as to obtain the final identification results of silt-retaining dams in the watershed to be identified.

[0058] It is understood that the embodiments of this application utilize the river network information of the watershed to be identified to eliminate the identification results that are a certain distance away from the river network of the watershed, thereby enabling the rapid location of the specific location of the silt-retaining dam in the watershed and improving the accuracy of automatic identification of silt-retaining dams.

[0059] According to the silt-retaining dam identification method proposed in this application, river network information and remote sensing images of the watershed to be identified are obtained. The remote sensing images of the river network area are extracted using the river network information of the watershed to be identified and input into the trained target detection model to obtain the identification result of the watershed to be identified. The spatial coordinates of multiple target points in the river network information are extracted. The identification results that are far away from the river network of the watershed are eliminated using the river network information of the watershed to be identified. In this way, the specific location of the silt-retaining dam in the watershed can be quickly located, which improves the accuracy of automatic identification of silt-retaining dams and reduces errors.

[0060] The method for identifying silt-retaining dams will be described in detail below through specific embodiments. The specific steps are as follows:

[0061] S1. Collect location information and remote sensing images of silt-retaining dams on the Loess Plateau.

[0062] Among them, the location information of silt-retaining dams on the Loess Plateau is the information of silt-retaining dams in the Wuding River Basin from the national water conservancy census; the remote sensing images are from high-precision maps, such as Google Maps.

[0063] S2. Manually mark the location and extent of silt-retention dams in remote sensing images.

[0064] The manual annotation method involves using a rectangular frame to select all silt-retaining dams in a remote sensing image and saving the annotation information.

[0065] S3. Construct a deep learning-based object detection model.

[0066] The deep learning-based object detection model is the Faster R-CNN model; the feature extraction network used in the model is the ResNet50 network that integrates feature pyramid modules.

[0067] S4. Train the target detection model based on labeled remote sensing images.

[0068] The target detection model uses the SGD optimizer; during training, different combinations of learning rate and batch size are adjusted and the model with the best training effect is retained.

[0069] S5. Extract the digital river network of the watershed to be identified and obtain remote sensing images of the area where the river network is located.

[0070] S5.1. Input the DEM of the watershed to be identified into the digital river network extraction model to extract the high-precision digital river network.

[0071] S5.2 Utilize the algorithm to batch obtain the spatial coordinates of all points on the river network at certain intervals;

[0072] The algorithm can be selected according to the actual situation. For example, the ArcGIS algorithm or the GDAL library in Python can also be used to extract point set information in shapefiles. There is no specific limitation.

[0073] S5.3. Based on the coordinates of the point set, use the program to automatically extract remote sensing images from Google Maps.

[0074] S6. Input the remote sensing image into the target detection model to identify silt-retaining dams and export the identification results.

[0075] The identification results of silt-retention dams include: the latitude and longitude coordinates of the center point of the silt-retention dam and the length and width of the rectangle in the remote sensing image where the silt-retention dam is located.

[0076] S7. Use the KD tree algorithm to match the silt-retaining dam with the nearest river section and remove the identification results that are far away from the river network.

[0077] S7.1. Input the digital river network and silt-retention dam identification results into the KD tree algorithm to quickly match the silt-retention dam with the nearest river network;

[0078] S7.2 Calculate the deviation between the location of the silt-retaining dam and the river section based on the location information of the silt-retaining dam and the river section.

[0079] S7.3 Statistical methods are used to remove silt-retaining dam identification results with excessively large deviation values.

[0080] Next, the silt-retaining dam identification device proposed according to the embodiments of this application is described with reference to the accompanying drawings.

[0081] Figure 3 This is a block diagram of a silt-retaining dam identification device according to an embodiment of this application.

[0082] like Figure 3 As shown, the silt-retaining dam identification device 10 includes: a data acquisition module 110, a labeling module 120, and a processing module 130.

[0083] The acquisition module 110 is used to acquire the location information of the silt-retaining dams and remote sensing images; the annotation module 120 is used to annotate the location and extent of the silt-retaining dams in the remote sensing images based on the location information, train a pre-constructed target detection model using the annotated remote sensing images, and obtain the trained target detection model after training; the processing module 130 is used to extract remote sensing images of the river network area of ​​the watershed to be identified using the river network information of the watershed to be identified, input the extracted remote sensing images into the trained target detection model, output the identification results of the silt-retaining dams in the watershed to be identified, and remove the identification results of the silt-retaining dams that are at a certain distance from the river network of the watershed to be identified using the river network information of the watershed to be identified, so as to obtain the final identification results of the silt-retaining dams in the watershed to be identified.

[0084] It should be noted that the foregoing explanation of the embodiment of the silt-retaining dam identification method also applies to the silt-retaining dam identification device of this embodiment, and will not be repeated here.

[0085] According to the silt-retaining dam identification device proposed in this application, the location information and remote sensing images of silt-retaining dams are collected. Based on the location information, the approximate location and range of the silt-retaining dam are marked in the remote sensing images. The marked remote sensing images are used to train the target detection model. The remote sensing images of the river network area of ​​the watershed to be identified are extracted using the river network information of the watershed to be identified and input into the trained target detection model. The latitude and longitude coordinates of the center point of the silt-retaining dam and the length and width of the rectangle in which the silt-retaining dam is located in the remote sensing image are output. The silt-retaining dam in the watershed to be identified is identified based on the identification results and the river network information of the watershed to be identified. The detection model is constructed based on the location information of the silt-retaining dam and the remote sensing images, and auxiliary verification is performed in combination with the river network information of the watershed to be identified. This allows for rapid location and identification of the silt-retaining dam in the watershed, improving the accuracy of automatic identification of silt-retaining dams, reducing errors, reducing the number of input images, and reducing the amount of computation.

[0086] Next, the silt-retaining dam identification device proposed according to the embodiments of this application is described with reference to the accompanying drawings.

[0087] Figure 4 This is a block diagram of a silt-retaining dam identification device according to an embodiment of this application.

[0088] like Figure 4 As shown, the silt-retaining dam identification device 20 includes: an acquisition module 210, an interception module 220, and a rejection module 230.

[0089] The acquisition module 210 is used to acquire river network information of the watershed to be identified; the cropping module 220 is used to crop remote sensing images of the river network area of ​​the watershed to be identified using the river network information of the watershed to be identified, input the cropped remote sensing images into the trained target detection model, and output the identification results of silt-retaining dams in the watershed to be identified; the elimination module 230 is used to eliminate the identification results of silt-retaining dams that are outside the river network of the watershed to be identified using the river network information of the watershed to be identified, so as to obtain the final identification results of silt-retaining dams in the watershed to be identified.

[0090] It should be noted that the foregoing explanation of the embodiment of the silt-retaining dam identification method also applies to the silt-retaining dam identification device of this embodiment, and will not be repeated here.

[0091] According to the silt-retaining dam identification device proposed in this application, river network information and remote sensing images of the watershed to be identified are acquired. The remote sensing images of the river network area of ​​the watershed to be identified are extracted using the river network information of the watershed to be identified and input into the trained target detection model to obtain the identification result of the watershed to be identified. The identification results that are far away from the river network of the watershed are removed using the river network information of the watershed to be identified. In this way, the specific location of the silt-retaining dam in the watershed can be quickly located, which improves the accuracy of automatic identification of silt-retaining dams, reduces errors, reduces the number of input images, and reduces the amount of computation.

[0092] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0093] The memory 501, the processor 502, and the computer program stored on the memory 501 and capable of running on the processor 502.

[0094] When processor 502 executes the program, it implements the silt-retaining dam identification method provided in the above embodiments.

[0095] Furthermore, electronic devices also include:

[0096] Communication interface 503 is used for communication between memory 501 and processor 502.

[0097] The memory 501 is used to store computer programs that can run on the processor 502.

[0098] The memory 501 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0099] If the memory 501, processor 502, and communication interface 503 are implemented independently, then the communication interface 503, memory 501, and processor 502 can be interconnected via a bus to complete communication between them. The 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 address bus, data bus, control bus, etc. For ease of representation, Figure 5 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0100] Optionally, in a specific implementation, if the memory 501, processor 502, and communication interface 503 are integrated on a single chip, then the memory 501, processor 502, and communication interface 503 can communicate with each other through an internal interface.

[0101] Processor 502 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of this application.

[0102] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described silt-retaining dam identification method.

[0103] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0104] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0105] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0106] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (FPGAs), field-programmable gate arrays (FPGAs), etc.

[0107] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0108] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. A method for identifying silt-retaining dams, characterized in that, Includes the following steps: Collect location information and remote sensing images of silt-retaining dams; The location and extent of the silt-retaining dam are marked in the remote sensing image based on the location information. The pre-constructed target detection model is trained using the marked remote sensing image, and the trained target detection model is obtained after the training is completed. Remote sensing images of the river network area of ​​the watershed to be identified are extracted using river network information. These extracted images are then input into the trained target detection model, which outputs the identification results of silt-retaining dams in the watershed. The river network information of the watershed is used to remove identification results of silt-retaining dams located at a certain distance from the river network, resulting in the final identification results of silt-retaining dams in the watershed. These results include: The digital elevation model of the watershed to be identified is input into the digital river network extraction model to extract a high-precision digital river network, and the spatial coordinates of all target points on the river network at preset distances are obtained based on the high-precision digital river network. Remote sensing images of the river network area to be identified are extracted based on the spatial coordinates of all target points, including: determining the river network within a preset range based on the spatial coordinates of multiple target points and the latitude and longitude coordinates of the center point of the silt-retaining dam; and calculating the deviation between the location of the silt-retaining dam and the river segment based on the river segment attributes of the river network and the latitude and longitude coordinates of the center point of the silt-retaining dam. By using statistical methods to remove results from the identification results that have deviations greater than a deviation threshold, the final identification results of silt-retaining dams in the watershed to be identified are obtained.

2. The method according to claim 1, characterized in that, The identification results include the latitude and longitude coordinates of the center point of the silt-retention dam and the length and width of the rectangle in the remote sensing image where the silt-retention dam is located.

3. The method according to claim 1, characterized in that, The step of marking the location and extent of the silt-retention dam in the remote sensing image based on the location information includes: All silt-retaining dams in the remote sensing image are selected one by one using a rectangular frame, and the annotation information is saved.

4. A method for identifying silt-retaining dams, characterized in that, Includes the following steps: Obtain river network information for the watershed to be identified; Remote sensing images of the river network area of ​​the watershed to be identified are extracted using the river network information of the watershed to be identified. The extracted remote sensing images are then input into the trained target detection model, and the identification results of silt-retaining dams in the watershed to be identified are output. By using the river network information of the watershed to be identified, the identification results of the silt-retaining dams that are located at a certain distance from the river network of the watershed to be identified are removed, and the final identification results of the silt-retaining dams in the watershed to be identified are obtained, which include: The digital elevation model of the watershed to be identified is input into the digital river network extraction model to extract a high-precision digital river network, and the spatial coordinates of all target points on the river network at preset distances are obtained based on the high-precision digital river network. Remote sensing images of the river network area to be identified are extracted based on the spatial coordinates of all target points, including: determining the river network within a preset range based on the spatial coordinates of multiple target points and the latitude and longitude coordinates of the center point of the silt-retaining dam; and calculating the deviation between the location of the silt-retaining dam and the river segment based on the river segment attributes of the river network and the latitude and longitude coordinates of the center point of the silt-retaining dam. By using statistical methods to remove results from the identification results that have deviations greater than a deviation threshold, the final identification results of silt-retaining dams in the watershed to be identified are obtained.

5. A silt-retaining dam identification device, characterized in that, include: The data acquisition module is used to collect location information and remote sensing images of silt-retaining dams. The annotation module is used to annotate the location and extent of the silt-retaining dam in the remote sensing image based on the location information, train a pre-constructed target detection model using the annotated remote sensing image, and obtain the trained target detection model after the training is completed. The processing module is used to extract remote sensing images of the river network area of ​​the watershed to be identified using river network information of the watershed to be identified, input the extracted remote sensing images into the trained target detection model, output the identification results of silt-retaining dams in the watershed to be identified, and use the river network information of the watershed to be identified to remove the identification results of silt-retaining dams that are located at a certain distance from the river network of the watershed to be identified, so as to obtain the final identification results of silt-retaining dams in the watershed to be identified, which include: The digital elevation model of the watershed to be identified is input into the digital river network extraction model to extract a high-precision digital river network, and the spatial coordinates of all target points on the river network at preset distances are obtained based on the high-precision digital river network. Remote sensing images of the river network area to be identified are extracted based on the spatial coordinates of all target points, including: determining the river network within a preset range based on the spatial coordinates of multiple target points and the latitude and longitude coordinates of the center point of the silt-retaining dam; and calculating the deviation between the location of the silt-retaining dam and the river segment based on the river segment attributes of the river network and the latitude and longitude coordinates of the center point of the silt-retaining dam. By using statistical methods to remove results from the identification results that have deviations greater than a deviation threshold, the final identification results of silt-retaining dams in the watershed to be identified are obtained.

6. A silt-retaining dam identification device, characterized in that, include: The acquisition module is used to acquire river network information of the watershed to be identified; The interception module uses the river network information of the watershed to be identified to intercept remote sensing images of the river network area of ​​the watershed to be identified, inputs the intercepted remote sensing images into the trained target detection model, and outputs the identification results of silt-retaining dams in the watershed to be identified. The elimination module is used to eliminate identification results of silt-retaining dams that are located at a certain distance from the river network of the watershed to be identified, using the river network information of the watershed to be identified, to obtain the final identification results of silt-retaining dams in the watershed to be identified, which includes: The digital elevation model of the watershed to be identified is input into the digital river network extraction model to extract a high-precision digital river network, and the spatial coordinates of all target points on the river network at preset distances are obtained based on the high-precision digital river network. Remote sensing images of the river network area to be identified are extracted based on the spatial coordinates of all target points, including: determining the river network within a preset range based on the spatial coordinates of multiple target points and the latitude and longitude coordinates of the center point of the silt-retaining dam; and calculating the deviation between the location of the silt-retaining dam and the river segment based on the river segment attributes of the river network and the latitude and longitude coordinates of the center point of the silt-retaining dam. By using statistical methods to remove results from the identification results that have deviations greater than a deviation threshold, the final identification results of silt-retaining dams in the watershed to be identified are obtained.

7. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the silt-retaining dam identification method as described in any one of claims 1-4.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the silt-retaining dam identification method as described in any one of claims 1-4.

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