Social media data and SAR (Synthetic Aperture Radar) image integrated flood inundation drawing method and system

Abstract

The invention provides a flood inundation drawing method and system integrating social media data and SAR images, and relates to the technical field of emergency management, and the method comprises the steps: obtaining text data which is related to a rainstorm flood event and carries POI address information in social media; generating a rough flood inundation range map and a flood inundation probability map of the disaster area based on the DEM data, the POI inundation points and the corresponding water depth information; determining a flood inundation probability threshold interval based on the searched field picture and video data, and obtaining an intersection to obtain a flood inundation range map of the urban built-up area; according to the pre-disaster, in-disaster and post-disaster multi-scene SAR images, water body data in different periods are extracted, and a river flood inundation range map is generated; and combining the flood inundation range map of the urban built-up area and the flood inundation range map of the river channel to obtain a flood inundation range map of the affected area. According to the method, the advantages of social media data and time sequence SAR images in urban flood and river flood detection are fully utilized, and high-precision mapping of large flood events is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of flood disaster emergency management, and in particular to a flood inundation mapping method and system integrating social media data and SAR images. Background Art

[0002] As a natural disaster, floods have caused serious losses to human life, agricultural production and the ecological environment. In recent years, with the increase of global climate change and extreme weather events, the frequency and destructiveness of flood disasters have continued to increase. Therefore, timely and accurate monitoring and mapping of flood-inundated areas are of great significance for flood prevention and disaster reduction, disaster emergency response and post-disaster reconstruction.

[0003] With the popularity of social media, when people encounter natural disasters such as floods, they often publish relevant information through platforms such as Weibo, WeChat, and Douyin. These social media data are real-time, extensive, and diverse, providing a rich source of information for flood monitoring. At present, researchers have achieved certain results in social media data mining, flood event identification, and flood range prediction. People extract valuable information from a large amount of social media text, pictures, and videos. By mining social media data, we can obtain information such as the spatiotemporal distribution, impact range, and disaster severity of flood events, providing data support for flood monitoring. Researchers also process social media data by building text classification models, image recognition models, and other methods to achieve automatic identification of flood events.

[0004] Synthetic aperture radar (SAR) is an active remote sensing technology with all-weather, all-day, high-resolution characteristics. It can penetrate clouds and fog to obtain surface information. Time-series SAR images play an important role in flood monitoring and can be used to extract flood-inundated areas and assess flood impact ranges. Time-series SAR images can extract flood-inundated areas by analyzing SAR images at different time points. Researchers use threshold segmentation, edge detection, classification and other methods to process time-series SAR images and realize the automatic extraction of flood-inundated areas.

[0005] Although social media data and time-series SAR images have achieved remarkable results in flood monitoring, the existing methods do not sufficiently integrate social media data with SAR images, and have limitations in flood monitoring and inundation range prediction, making it difficult to meet the needs of real-time and accurate flood monitoring. Summary of the invention

[0006] To solve the above problems, the present invention provides a flood inundation mapping method integrating social media data and SAR images. The method realizes the rapid extraction and range mapping of flood inundation areas by fusing social media data and time-series SAR images, providing strong support for flood prevention, disaster reduction and disaster emergency response.

[0007] To achieve the above object, the present invention provides the following technical solutions.

[0008] The flood inundation mapping method integrating social media data and SAR images includes the following steps:

[0009] Obtain text data related to rainstorm and flood events with POI address information in social media, detect water depth information in the text data, and obtain a point set of multiple POI flooded points with water depth information;

[0010] Obtain DEM data of urban built-up areas; obtain the water level elevation value of each POI inundation point based on DEM data, POI inundation points and corresponding water depth information; use Kriging interpolation algorithm to generate a rough flood inundation range map of the affected area based on DEM data and the water level elevation value of each POI inundation point;

[0011] Traverse each grid center point of the DEM data of the urban built-up area one by one to determine the POI flood point closest to each grid center point; obtain the flood inundation probability at each grid center point based on the water level elevation value of the grid center point and the POI flood point closest to it, and construct a flood inundation probability map;

[0012] Based on the on-site pictures and video data obtained through the search, the typical flood inundation locations are determined; the typical flood inundation locations are superimposed on the flood inundation probability map, the flood inundation probability threshold interval is confirmed, and the flood inundation probability range belonging to the threshold interval is intersected with the rough flood inundation range map to obtain the flood inundation range map of the urban built-up area;

[0013] Based on the multi-view SAR images before, during and after the disaster, the water body data of different periods were extracted based on the random forest algorithm to generate the river flood inundation range map;

[0014] The flood inundation range map of urban built-up areas and the river flood inundation range map are combined to obtain the inundation range map of the affected areas of flood events.

[0015] Preferably, the step of obtaining text data related to rainstorm and flood events and containing POI address information from social media, and detecting water depth information in the text data, comprises the following steps:

[0016] Using keywords to obtain text data related to a specific rainstorm and flood event from social media; the social media is a Weibo platform;

[0017] The ChatGLM3-6B model is used to extract text data with POI address information;

[0018] A water depth lookup table is constructed to detect water depth information from text data with POI address information; the water depth lookup table detects water depth information based on four items, including directly describing the depth, using a person's height as a reference, using a building's height as a reference, and using other objects' height as a reference.

[0019] Preferably, the construction of the rough flood inundation range map comprises the following steps:

[0020] Obtain the DEM data of the urban built-up area, and calculate the water level elevation value at the POI inundation point based on the DEM data and the inundation depth of the POI inundation point;

[0021] Using these POI inundation points with water level elevation values, the ordinary Kriging interpolation algorithm is used to interpolate the flood water level surface of the affected area;

[0022] The interpolated flood water level surface of the affected area is subtracted from the DEM data, and the elevation position where the water level is higher than the DEM data is used as the global inundation range map;

[0023] The urban built-up area boundary data publicly released on the Internet was used and masked with the global inundation range map to obtain a rough flood inundation range map of the urban built-up area.

[0024] Preferably, the construction of the flood inundation probability map comprises the following steps:

[0025] Mark the grid row and column numbers of all POI submerged points with water depth information on the DEM data;

[0026] All grid center points on the DEM data are used as the flood inundation probability locations to be calculated, and the KD tree is used to index the POI inundation points, and the POI inundation points closest to each grid center point are determined and marked;

[0027] The flood inundation probability at each grid center point is calculated based on the nearest POI inundation point.

[0028] Preferably, the calculation of the flood inundation probability at each grid center point comprises the following steps:

[0029] The current grid center point to be calculated is j, and the nearest known POI flooding point to this point is i. The higher the elevation of point j, the smaller the possibility of point j being flooded. The flooding probability is negatively correlated with the distance between i and j.

[0030] Define the height difference between POI submerged point i and grid center point j as:

[0031]

[0032] Among them, H i and H j are the heights of POI submerged point i and grid center point j, DH ij is the height difference between POI submerged point i and grid center point j. If the position of point j is higher than point i, point j will not be submerged. i <H j When, DH ij The value of H is 0; i ≥H j When, DH ij The value of is the height difference between two points;

[0033] Then the probability P of the grid center point j being flooded is j for:

[0034]

[0035] Among them, d ij represents the Euclidean distance between POI submerged point i and grid center point j;

[0036] P j The area that is not 0 or 1 is normalized to the maximum and minimum values, and the calculation formula is:

[0037]

[0038] in, is the normalized value of the flooding probability of the grid center point j, max(P) represents the maximum value of the flooding probability of all locations, and min(P) represents the minimum value of the flooding probability of all locations.

[0039] Preferably, the generation of the river flood inundation range map comprises the following steps:

[0040] Preprocess SAR images, including radiation correction, speckle noise suppression, geometric terrain correction and backscatter coefficient conversion;

[0041] Select VV, VH, and SDWI bands, and construct training samples and test samples in a ratio of 7:3 from the preprocessed images for random forest model training;

[0042] The trained random forest model is used to extract water body data before, during and after the disaster. The water bodies before, during and after the disaster are superimposed and merged to obtain the river flood inundation range extracted from SAR images.

[0043] Preferably, the generation of the flood range map of the disaster-affected area includes the following steps:

[0044] The urban built-up area flood inundation range map and the river flood inundation range are raster-merged to generate a new binary raster map; the binary raster map is the flood range map of the affected area.

[0045] A flood inundation mapping system integrating social media data and SAR images, the system comprising:

[0046] processor;

[0047] a memory having stored thereon a computer program executable on the processor;

[0048] Wherein, when the computer program is executed by the processor, the steps of the flood inundation mapping method integrating social media data and SAR images are implemented.

[0049] A computer-readable storage medium having a data processing program stored thereon, wherein when the data processing program is executed by a processor, the steps of the flood inundation mapping method integrating social media data and SAR images are implemented.

[0050] Beneficial effects of the present invention:

[0051] The present invention proposes a flood inundation mapping method and system integrating social media data and SAR images. By integrating social media data and time-series SAR images, rapid extraction, probability judgment and range mapping of flood inundation areas are achieved, and a flood range map of the affected area is generated that combines the flood inundation range of urban built-up areas and the flood inundation range of river channels. The flood range of the affected area is accurately obtained, and the real-time and accurate flood monitoring needs can be met. The present invention makes full use of the advantages of social media data and time-series SAR images in urban flood and river flood detection, respectively, to achieve high-precision mapping of large-scale flood events, which is helpful for scientific research and judgment of the flood event process, and provides technical support for the government to make disaster management decisions. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 is a flow chart of a flood inundation mapping method integrating social media data and SAR images according to an embodiment of the present invention;

[0053] Figure 2 It is an example of Weibo data downloaded from the Weibo platform using flood event keywords;

[0054] Figure 3 is the flooding point extracted from the Weibo text of a flood event in a certain city; Figure 3 (a) is the flood inundation point, Figure 3 (b) is the flooded point with water depth information;

[0055] Figure 4It is a rough flood inundation range map obtained by spatial interpolation of DEM data and inundation points with water depth information;

[0056] Figure 5 It is a schematic diagram of the method for calculating the probability of flooding;

[0057] Figure 6 It is a flood inundation probability map generated by combining DEM data and inundation points with water depth information;

[0058] Figure 7 is based on the flooding probability map, Figure 7 (a)- Figure 7 (c) The possible flooding range maps generated using different thresholds;

[0059] Figure 8 It is a schematic diagram to verify the possible flooding range map based on the photos and video information in the news reports;

[0060] Fig. 9 It is a flood inundation range map of the urban built-up area obtained by taking the intersection of the confirmed final possible inundation range map and the rough flood inundation range map;

[0061] Fig.10 It is the SAR image data of a flood event in a certain city before, during and after the disaster. Fig.10 (a) is the SAR image data before the disaster. Fig.10 (b) is the SAR image data during the disaster. Fig.10 (c) is the post-disaster SAR image data;

[0062] Fig.11 is the water body range generated by the three-view SAR images before, during and after the disaster, among which, Fig.11 (a) is the water body range generated by SAR images before the disaster. Fig.11 (b) The water body range generated by SAR images during the disaster. Fig.11 (c) Water body extent generated for post-disaster SAR images;

[0063] Fig.12 It is the flood inundation range of a certain flood event in a certain city generated based on time-series SAR images;

[0064] Fig.13 It is the final flood inundation range map that is the union of the flood inundation range generated by time-series SAR images and the flood inundation range generated by social media data. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0066] Example 1

[0067] Social media data and time-series SAR images have achieved remarkable results in flood monitoring, but existing methods still have the following shortcomings: First, the quality of social media data is uneven, and there is a large amount of noise information, which affects the accuracy of flood monitoring. Second, the time-series SAR image processing method is complex, and requires high computing resources and professional knowledge, which limits its promotion in practical applications. Third, the integration of social media data and SAR images is not enough, and there is a lack of effective fusion methods, which affects the efficiency of flood monitoring. Fourth, existing methods have limitations in predicting flood inundation range, and it is difficult to meet the needs of real-time and accurate flood monitoring. To this end, the present invention provides a flood inundation mapping method that integrates social media data and SAR images. The specific process is as follows: Figure 1 As shown, the following steps are included:

[0068] S1: Obtain text data related to rainstorm and flood events in social media with POI address information, detect water depth information in the text data, and obtain a point set of multiple POI flooded points with water depth information.

[0069] S2: Obtain DEM data of urban built-up areas; obtain the water level elevation value of each POI inundation point based on the DEM data, POI inundation points and corresponding water depth information; use the Kriging interpolation algorithm to generate a rough flood inundation range map of the affected area based on the DEM data and the water level elevation values ​​of each POI inundation point.

[0070] S3: Traverse each grid center point of the DEM data of the urban built-up area one by one to determine the POI flood point closest to each grid center point; obtain the flood inundation probability at each grid center point based on the water level elevation value of the grid center point and the POI flood point closest to it, and construct a flood inundation probability map.

[0071] S4: Based on the on-site pictures and video data obtained through the search, determine the typical flood inundation locations; superimpose the typical flood inundation locations on the flood inundation probability map, confirm the flood inundation probability threshold interval, and intersect the range of flood inundation probability belonging to the threshold interval with the rough flood inundation range map to obtain the flood inundation range map of the urban built-up area.

[0072] S5: Based on the multi-scene SAR images before, during and after the disaster, the water body data of different periods are extracted based on the random forest algorithm to generate the river flood inundation range map.

[0073] S6: Combine the urban built-up area flood inundation range map and the river flood inundation range map to obtain the flood range map of the affected area of ​​the flood event.

[0074] In the prior art, Chinese patent CN202210633324.3 proposes a method for generating a flood inundation probability map in a flood inundation range estimation method based on multi-source data. However, the patent generates a flood inundation probability map independently for each POI inundation location. For example, if there are 100 flood inundation points in the disaster-stricken area, 100 flood inundation probability maps will be generated. In the present invention, a complete flood inundation probability map is generated for all POI inundation points with water depth information. At the same time, the present invention normalizes the flood inundation probability so that the flooding probability of the disaster-stricken area can be compared everywhere. In addition, the invention uses multi-source data to determine weights based on multiple flood inundation probability maps, and the final result is still a flood inundation probability map. The present invention uses photos and videos of real flooded locations published by online news reports on the basis of a flood inundation probability map to confirm the appropriate flood inundation probability interval as the real inundation range, which is essentially different in technology.

[0075] In this embodiment, the social media platform is the Weibo platform. In S1, keywords are specifically used to download Weibo texts related to specific rainstorm and flood events from the Weibo platform.

[0076] The data collection method for the Weibo texts related to the rainstorm and flood events here is mainly to use the API interface provided by Sina Weibo, combined with specific keywords such as "XX rainstorm" or "XX flood" characterized by a certain place name XX, and according to the time of the flood, download a large amount of Weibo text data posted by Weibo users from the Sina Weibo platform. Figure 2 As shown, there are multiple microblog data examples downloaded from the microblog platform, and the downloaded data includes information such as user name, microblog publishing time, and microblog content.

[0077] The ChatGLM3-6B large language model was used to extract flood event microblog texts with POI address information;

[0078] ChatGLM3-6B is an open source model in the ChatGLM3 series. It retains many excellent features of the previous two generations of models, such as smooth conversation and low deployment threshold, and also provides more diverse training data sets, more comprehensive training steps and more reasonable training strategies. It extracts POI address information from Weibo text by constructing appropriate prompt words, and provides specific examples to help the model better understand the expected output content.

[0079] The task instruction for the present invention to interact with ChatGLM3-6B is "Please identify and extract all specific place name information from the following Weibo text. The information I need is the specific place name, not the name of the administrative division such as province, city and county. Place categories include tourist attractions, life service facilities, hotel accommodation, shopping consumption, catering, medical care, companies and enterprises, etc."

[0080] For example, if the text of the Weibo post is "[Rescue of disabled people in the rain] At 20:00 on July 18, at the west gate of Yuandingyuan Community, Pingyuan Road in the jurisdiction, due to heavy rain, the road was flooded too deeply. A disabled lady in her 50s got her electric tricycle stuck in the water and couldn’t push it out. Patrol police XXX and auxiliary police XXX in patrol car No. 2 of the High-tech Branch helped the disabled woman push the electric tricycle out of the water, and then sent the disabled woman home", then the place name extracted by ChatGLM3-6B is 'Yuandingyuan Community, Pingyuan Road in the jurisdiction'. The extracted place names are output in JSON format. The format example is: {"Place 1":"Name","Place 2":"Name",...}. Do not output any other explanations. For example Figure 3 (a) shows a Weibo text about a flood event in a certain city. The ChatGLM3-6B large language model is used to analyze all flooded locations.

[0081] Furthermore, a water depth lookup table is established to detect water depth information from the microblog text of flood events with POI address information.

[0082] In the microblog texts related to heavy rain and floods, in addition to frequently mentioning the location where the flood occurred, the water depth range or maximum water depth is often included. According to their content, they are divided into four categories: (1) directly describing the depth, such as "the water depth is now 1.5 meters"; (2) using a person's height as a reference, such as "How heavy is this rain? It is well known that the intersection of Daoqing Road and Xinzhong Road is flooded every time it rains in Xinxiang. The water level here is already waist-high." The water depth at the intersection of Daoqing Road and Xinzhong Road in Xinxiang City is set to 1.2 meters; (3) using the height of buildings as a reference; (4) using the height of other specific objects as a reference. The program screens the microblog texts containing POI locations, and finally obtains the flooded POI locations containing water depth information. The water depth search expression is as follows:

[0083] Table 1 Water depth lookup table

[0084]

[0085]

[0086] like Figure 3 (b) shows a microblog text about a flood event in a certain city. By using the water depth lookup table, the water depth information is extracted from the microblog text with the flooded location. Figure 3 (b) shows the flooded locations with water depth information.

[0087] Furthermore, the construction of the rough flood inundation range map in S2 includes the following steps:

[0088] S2.1: Based on high-resolution DEM data (spatial resolution of 5 m and below), combined with the inundation depth at the POI inundation point, back-calculate the water level elevation value at the POI inundation point.

[0089] S2.2: Use these POI flood points with water level values ​​and the ordinary Kriging interpolation algorithm to interpolate the flood water level surface of the affected area. The Kriging interpolation algorithm is a spatial interpolation method based on statistics and probability theory, which can provide more accurate and reliable prediction results than other interpolation methods.

[0090] S2.3: Subtract the interpolated flood water level surface of the affected area from the high-resolution DEM data, and use the locations where the water level elevation is higher than the DEM elevation as the global inundation range map.

[0091] S2.4: Use the urban built-up area boundary data publicly released on the Internet and mask it with the global flood range map to obtain a rough flood inundation range map of the urban built-up areas in the disaster-stricken areas.

[0092] Figure 4 It is a rough inundation range map of a flood event in a certain city generated by combining high-resolution DEM data and flood inundation location information with water depth information using the Kriging interpolation algorithm.

[0093] Some areas in the flooded range calculated by S2 are far away from the flooded points with known water levels and are scattered, so the accuracy of water level inference is low. Therefore, it is necessary to calculate the flooding probability of each location to more accurately infer whether the location is flooded. Figure 6 It is a flood inundation probability map generated by combining DEM data and inundation points with water depth information. S3 specifically includes the following steps:

[0094] S3.1: Mark the grid row and column numbers of all POI flooded points with water depth information on the DEM of the disaster area.

[0095] S3.2: Take all grid center points on the DEM of the disaster area as the flood inundation probability positions to be calculated, and mark which POI inundation point position is closest to each grid center point. In order to speed up the processing and avoid the calculation being too slow due to the exhaustive method, the present invention uses a KD tree to index the POI inundation points, so that the POI inundation point closest to each grid center point can be calculated quickly.

[0096] S3.3: Traverse each grid center point one by one, find the POI flood point closest to the grid center point, and calculate the flood inundation probability at the grid center point. Figure 5 This is a schematic diagram of the method for calculating the probability of flooding. The calculation principle is as follows:

[0097] Assuming that the current grid center point to be calculated is j, and the nearest known POI flooding point to this point is i, the higher the elevation of point j, the smaller the possibility of point j being flooded, and the flooding probability is negatively correlated with the distance between i and j;

[0098] Define the height difference between POI submerged point i and grid center point j as:

[0099]

[0100] Among them, H i and H j are the heights of POI submerged point i and grid center point j, DH ij is the height difference between POI submerged point i and grid center point j. If the position of point j is higher than point i, point j will not be submerged. i <H j When, DH ij The value of H is 0; i ≥H j When, DH ij The value is the height difference between the two points.

[0101] Then the probability P of the grid center point j being flooded is j for:

[0102]

[0103] Among them, d ij represents the Euclidean distance between POI submerged point i and grid center point j;

[0104] P j The area that is not 0 or 1 is normalized to the maximum and minimum values, and the calculation formula is:

[0105]

[0106] in, is the normalized value of the flooding probability of the grid center point j, max(P) represents the maximum value of the flooding probability of all locations, and min(P) represents the minimum value of the flooding probability of all locations. After normalization, all probability values ​​are scaled to the range of 0 to 1 to ensure that the flooding probabilities of different locations are comparable and eliminate the dimensional differences introduced in the calculation process.

[0107] Furthermore, S4 mainly searches for photos and videos in real-time news reports on flood events through search engines, compares the flood site with the flood inundation probability maps under different thresholds, and finally selects the flood inundation probability map that best matches the on-site scenario. Figure 7 Maps of possible flooding range generated using different thresholds. Figure 8 This is a schematic diagram to verify the possible flood range map based on the photos and video information in the news reports. By investigating the flooding scenarios at different locations and comparing them with the flood inundation range maps under different probability thresholds, the most appropriate flood inundation range map under the flood probability threshold is finally confirmed.

[0108] like Fig. 9 shown.

[0109] Specifically, S5 performs the following steps:

[0110] Using multi-view images before, during and after a disaster, the random forest algorithm is used to extract water body data from different periods and generate the flood inundation range of the river channel;

[0111] This step is mainly implemented through the Sentinel-1GRD dataset provided by the GEE platform. The SAR images are preprocessed directly on the GEE platform, including radiation correction, coherent speckle noise suppression, geometric terrain correction, and backscatter coefficient conversion.

[0112] Select the VV, VH and SDWI bands. VV polarization is sensitive to surface roughness and can effectively distinguish water bodies from non-water bodies; VH polarization is sensitive to vegetation and can improve classification accuracy in areas covered by vegetation. SDWI is a water index based on the combination of VV and VH polarizations. Based on the characteristic that the backscatter coefficient of water bodies on SAR images is lower than that of soil and vegetation, dual polarization data multiplication is used to enhance the contrast between water bodies and non-water bodies and improve the recognition ability of water bodies.

[0113] SDWI=ln(10×VV×VH)

[0114] After preprocessing, training samples and test samples were constructed in a ratio of 7:3 for random forest model training. Finally, the water bodies before, during and after the disaster were superimposed and merged to obtain the flood inundation range of the river extracted from the SAR image. Fig.10 It is the SAR image data of three scenes before, during and after a flood event in a certain city; Fig.11 It is the water body range generated based on the three-view SAR images before, during and after the disaster using the above water body extraction index; Fig.12 It is the flood inundation range of a flood event in a certain city generated based on time-series SAR images. This range is obtained by taking the union of the water body ranges generated by the above three images.

[0115] In S6, the flood inundation range of the urban area and the flood inundation range of the river channel are combined to obtain the inundation range map of the final flood event.

[0116] Since the urban area flood inundation range map and the river flood inundation range are both binary raster data, this step mainly involves directly merging the two sets of data into grids to generate a new binary raster map. Fig.13 It is the final flood inundation range map that is the union of the flood inundation range generated by time-series SAR images and the flood inundation range generated by social media data.

[0117] The above is a flood inundation mapping method integrating social media data and SAR images provided by one embodiment of the present embodiment. Based on the same idea, the present embodiment also provides a corresponding flood inundation mapping system integrating social media data and SAR images. The specific definition of the flood inundation mapping system integrating social media data and SAR images can be found in the above definition of the flood inundation mapping method integrating social media data and SAR images, which will not be repeated here. Each module in the above-mentioned flood inundation mapping system integrating social media data and SAR images can be implemented in whole or in part through software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0118] This embodiment also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 A flood inundation mapping method integrating social media data and SAR imagery is provided.

[0119] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0120] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A flood inundation mapping method integrating social media data and SAR images, characterized in that: The following steps are involved: Obtain text data related to rainstorm and flood events with POI address information in social media, detect water depth information in the text data, and obtain a point set of multiple POI flooded points with water depth information; Obtain DEM data of urban built-up areas; Based on DEM data, POI inundation points and corresponding water depth information, the water level elevation value of each POI inundation point is obtained; based on DEM data and the water level elevation value of each POI inundation point, a rough flood inundation range map of the affected area is generated using the Kriging interpolation algorithm; Traverse each grid center point of the DEM data of the urban built-up area one by one to determine the POI flood point closest to each grid center point; obtain the flood inundation probability at each grid center point based on the water level elevation value of the grid center point and the POI flood point closest to it, and construct a flood inundation probability map; Based on the on-site pictures and video data obtained through the search, the typical flood inundation locations are determined; the typical flood inundation locations are superimposed on the flood inundation probability map, the flood inundation probability threshold interval is confirmed, and the flood inundation probability range belonging to the threshold interval is intersected with the rough flood inundation range map to obtain the flood inundation range map of the urban built-up area; Based on the multi-view SAR images before, during and after the disaster, the water body data of different periods were extracted based on the random forest algorithm to generate the river flood inundation range map; The flood inundation range map of urban built-up areas and the river flood inundation range map are combined to obtain the inundation range map of the affected areas of flood events.

2. The flood inundation mapping method integrating social media data and SAR images according to claim 1, characterized in that: The method of obtaining text data related to rainstorm and flood events and containing POI address information from social media and detecting water depth information in the text data includes the following steps: Using keywords to obtain text data related to a specific rainstorm and flood event from social media; the social media is a Weibo platform; The ChatGLM3-6B model is used to extract text data with POI address information; A water depth lookup table is constructed to detect water depth information from text data with POI address information; the water depth lookup table detects water depth information based on four items, including directly describing the depth, using a person's height as a reference, using a building's height as a reference, and using other objects' height as a reference.

3. The flood inundation mapping method integrating social media data and SAR images according to claim 1, characterized in that: The construction of the rough flood inundation range map includes the following steps: Obtain the DEM data of the urban built-up area, and calculate the water level elevation value at the POI inundation point based on the DEM data and the inundation depth of the POI inundation point; Using these POI inundation points with water level elevation values, the ordinary Kriging interpolation algorithm is used to interpolate the flood water level surface of the affected area; The interpolated flood water level surface of the affected area is subtracted from the DEM data, and the elevation position where the water level is higher than the DEM data is used as the global inundation range map; The urban built-up area boundary data publicly released on the Internet was used and masked with the global inundation range map to obtain a rough flood inundation range map of the urban built-up area.

4. The flood inundation mapping method integrating social media data and SAR images according to claim 1, characterized in that: The construction of the flood inundation probability map includes the following steps: Mark the grid row and column numbers of all POI submerged points with water depth information on the DEM data; All grid center points on the DEM data are used as the flood inundation probability locations to be calculated, and the KD tree is used to index the POI inundation points, and the POI inundation points closest to each grid center point are determined and marked; The flood inundation probability at each grid center point is calculated based on the nearest POI inundation point.

5. The flood inundation mapping method integrating social media data and SAR images according to claim 4, characterized in that: The calculation of the flood inundation probability at each grid center point includes the following steps: The current grid center point to be calculated is j, and the nearest known POI flooding point to this point is i. The higher the elevation of point j, the smaller the possibility of point j being flooded. The flooding probability is negatively correlated with the distance between i and j. Define the height difference between POI submerged point i and grid center point j as: Among them, H i and H j are the heights of POI submerged point i and grid center point j, DH ij is the height difference between POI submerged point i and grid center point j. If the position of point j is higher than point i, point j will not be submerged. i <H j When, DH ij The value of H is 0; i ≥H j When, DH ij The value of is the height difference between two points; Then the probability P of the grid center point j being flooded is j for: Among them, d ij represents the Euclidean distance between POI submerged point i and grid center point j; P j The area that is not 0 or 1 is normalized to the maximum and minimum values, and the calculation formula is: in, is the normalized value of the flooding probability of the grid center point j, max(P) represents the maximum value of the flooding probability of all locations, and min(P) represents the minimum value of the flooding probability of all locations.

6. The flood inundation mapping method integrating social media data and SAR images according to claim 1, characterized in that: The generation of the river flood inundation range map includes the following steps: Preprocess SAR images, including radiation correction, speckle noise suppression, geometric terrain correction and backscatter coefficient conversion; Select VV, VH, and SDWI bands, and construct training samples and test samples in a ratio of 7:3 from the preprocessed images for random forest model training; The trained random forest model is used to extract water body data before, during and after the disaster. The water bodies before, during and after the disaster are superimposed and merged to obtain the river flood inundation range extracted from SAR images.

7. The flood inundation mapping method integrating social media data and SAR images according to claim 1, characterized in that: The generation of the flood range map of the disaster-affected area includes the following steps: The urban built-up area flood inundation range map and the river flood inundation range are raster-merged to generate a new binary raster map; the binary raster map is the flood range map of the affected area.

8. A flood inundation mapping system integrating social media data and SAR images, characterized in that: The system comprises: processor; a memory having stored thereon a computer program executable on the processor; Wherein, when the computer program is executed by the processor, the steps of the flood inundation mapping method integrating social media data and SAR images as described in any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a data processing program, and when the data processing program is executed by a processor, the steps of the flood inundation mapping method integrating social media data and SAR images are implemented as described in any one of claims 1 to 7.

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