Disaster prevention and mitigation support systems, disaster prevention and mitigation support methods, and disaster prevention and mitigation support programs.
The disaster prevention and mitigation support system addresses inefficiencies in conventional disaster risk assessment by leveraging mobile mapping technology for high-precision data collection and real-time risk assessment, supporting automated warnings and efficient response planning to enhance community safety.
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Applications
- Current Assignee / Owner
- THE CHUGOKU ELECTRIC POWER CO INC
- Filing Date
- 2024-12-05
- Publication Date
- 2026-06-17
Smart Images
Figure 2026098288000001_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to a disaster prevention and mitigation support system, a disaster prevention and mitigation support method, and a disaster prevention and mitigation support program that utilize mobile mapping technology to support disaster prevention and mitigation measures by performing real-time evaluation of disaster risks in disaster risk areas.
Background Art
[0002] In order to identify natural disaster risks such as floods and landslides and minimize damage, various data collection methods and risk assessment methods have been conventionally used. In the collection of topographic data, ground surveys and aerial photographs have been mainly used. A ground survey is a method in which field surveyors directly measure the topography and geology, and high-precision information can be obtained. On the other hand, aerial photographs utilize wide-range images taken from an aircraft, and a wide-area topography can be grasped. In risk assessment, a method of evaluating the disaster risk of a region using existing maps and statistical data is common. In addition, the past disaster history is referred to, and the possibility of the occurrence of a similar disaster is also evaluated. Based on these data, the disaster risk for each region is identified, and a plan for necessary disaster prevention measures is formulated. Regarding warning systems, a mechanism in which a person in charge issues a warning when an abnormality is detected is widely used. In addition, information is transmitted using disaster prevention radios and regional bulletin boards, and is utilized as a means of transmitting information to residents during disasters. Note that the following patent documents and the like are known as systems for appropriately evaluating disaster risks related to the occurrence of disasters.
Prior Art Documents
Patent Documents
[0003]
Patent Document 1
Summary of the Invention
Problems to be Solved by the Invention
[0004] However, conventional flood risk assessment and warning systems have the following challenges: First, conventional methods suffer from low efficiency and accuracy in data collection. While conventional ground surveys can provide high-precision data, their survey range is limited, making it difficult to collect data over a wide area, and requiring considerable time and effort. Methods using aerial photographs can acquire data over a wide area, but their low resolution limits the acquisition of detailed topographic information. Furthermore, conventional methods lack sufficient high-precision data, making it difficult to adequately improve the accuracy of risk assessments. Furthermore, conventional methods have challenges in terms of the accuracy and update frequency of risk assessments. Traditional methods rely on existing, outdated maps and statistical data, often failing to accurately reflect the current situation. Assessments based on past disaster history also lack reliability because they do not consider current topography and weather conditions. Additionally, risk assessments are not conducted in real time, making rapid response difficult in situations where a disaster is imminent. Furthermore, conventional methods have limitations in the speed and range of warning systems. Traditional manual warning systems can experience delays in transmission, potentially delaying the securing of residents' safety. In addition, communication methods such as disaster prevention radio and community bulletin boards make it difficult to immediately transmit information to all residents, making it challenging to encourage appropriate action during a disaster. Moreover, when data is managed on paper, information sharing takes time, and misunderstandings and confusion are likely to occur when multiple organizations are coordinating their responses. As described above, many challenges exist in terms of the efficiency and accuracy of data collection, the real-time nature of risk assessment, the speed of warning systems, and information sharing and response during disasters.
[0005] This invention was made in view of the above circumstances, and aims to streamline high-precision, wide-area data collection in disaster risk areas, thereby enabling real-time disaster risk assessment. Furthermore, it aims to support evacuation and response by automatically issuing warnings based on the assessment results and quickly transmitting information to residents and relevant organizations. In addition, it aims to minimize disaster damage by strengthening cooperation among disaster prevention organizations through integrated management of collected data and information sharing. [Means for solving the problem]
[0006] To achieve the above objectives, the disaster prevention and mitigation support system according to the present invention A terrain data acquisition unit that acquires 3D point cloud terrain data obtained by 3D scanning terrain data of disaster risk areas, The image acquisition unit acquires image data obtained by imaging the aforementioned disaster risk area, A positioning data acquisition unit that acquires positioning data, comprising at least a GPS receiver and an inertial measuring instrument, A mobile body equipped with the terrain data acquisition unit, the image acquisition unit, and the positioning data acquisition unit, A hazard data acquisition unit is provided in a manner separate from the mobile body for evaluating the risk of natural disasters in the aforementioned disaster risk area, and acquires hazard data (flood risk data, sediment disaster risk data), A data integration storage unit that integrates and stores the 3D point cloud terrain data, image data, and positioning data acquired while the moving body is moving or while the moving body is stationary, and the hazard data acquired by the hazard data acquisition unit. A disaster risk assessment unit evaluates the disaster risk in the disaster risk area based on the data integrated in the aforementioned data integration storage unit, It is characterized by possessing the following features.
[0007] Here, the mobile unit may include at least a mobile mapping vehicle, and the disaster risk assessment unit may include the assessment of flood risk or landslide risk in the disaster risk area. Therefore, according to the disaster prevention and mitigation support system of the present invention, by utilizing a terrain data acquisition unit, an image acquisition unit, and a positioning data acquisition unit mounted on a mobile vehicle, it is possible to acquire data over a wide area of disaster risk zones more efficiently and precisely than in conventional methods. Furthermore, by utilizing a hazard data acquisition unit installed separately from the mobile vehicle, diverse information regarding flood risk and sediment-related disaster risk can be combined to realize a multifaceted risk assessment.
[0008] Furthermore, by managing this data comprehensively in the integrated data storage unit, disaster risk can be assessed in real time based on the latest topographic information and weather conditions. This real-time capability enables rapid and accurate decision-making in situations where a disaster is imminent, providing crucial information for taking countermeasures.
[0009] The above system may further include an alarm issuing unit that issues an alarm when the disaster risk assessed by the disaster risk assessment unit is determined to be at or above a predetermined risk level. By incorporating a system that automatically issues an alarm when the disaster risk assessment department detects a risk exceeding a threshold, information can be quickly disseminated to residents and relevant organizations. The automated alarm function significantly reduces time loss compared to conventional manual operation, supporting faster evacuation actions and initial response.
[0010] Furthermore, the system may also include a risk map creation unit that visualizes areas where the disaster risk exceeds a predetermined level as a risk map. By incorporating such a risk map creation function, it becomes possible to intuitively grasp areas with a high risk of disaster. This allows residents and relevant organizations to quickly confirm evacuation routes and identify safe locations, and it can also be used as foundational information for systematically implementing disaster prevention measures before a disaster occurs. Furthermore, risk maps are also effective as an information provision tool for local residents, and are useful for education and awareness campaigns to improve disaster prevention awareness during peacetime and encourage appropriate actions during disasters. Furthermore, the maps generated by the risk mapping department can be shared as electronic data, making them an important tool for strengthening collaboration with local governments, disaster prevention agencies, and infrastructure managers. This will enable more efficient urban planning, shelter placement, and infrastructure development prioritization based on disaster risks.
[0011] Furthermore, the above system may also include a countermeasure planning unit that formulates disaster prevention and mitigation measures based on the degree of risk assessed by the disaster risk assessment unit. By having such a disaster response planning department, it becomes possible to quickly and efficiently plan appropriate disaster prevention and mitigation measures according to the degree of disaster risk. For example, in cases of moderate disaster risk, it is possible to propose evacuation drills and disaster prevention education in specific areas, and in cases of high risk, it is possible to plan to prioritize evacuation advisories and the emergency installation of disaster prevention equipment. Furthermore, it becomes possible to automatically generate specific action guidelines for residents and emergency response measures for administrative agencies based on the results of the risk assessment.
[0012] Furthermore, the disaster response planning department can dynamically adjust countermeasures according to the type and scale of the disaster, enabling flexible responses to different disaster risks such as floods and landslides. For example, if the risk of flooding is high, it can propose levee inspections and strengthened water level monitoring, and if the risk of landslides is high, it can prioritize recommending slope stabilization work and drainage channel development, thus deriving specific measures tailored to each risk. Furthermore, disaster prevention and mitigation measures formulated by the disaster response planning department are shared with residents and relevant organizations in conjunction with risk maps and collected data, thereby enhancing the effectiveness of the overall disaster prevention plan. This allows for the development of a unified action plan across the entire region, minimizing confusion during a disaster. The disaster response planning department further analyzes past disaster history and current topographic and meteorological data, contributing to the formulation of long-term disaster prevention strategies. This enables planned disaster countermeasures aimed at improving the safety of local communities and sustainable development. Therefore, by incorporating the disaster response planning department into this system, the entire disaster prevention and mitigation process is expected to become more efficient, and the disaster response capabilities of local communities will be significantly improved.
[0013] Furthermore, the disaster risk assessment unit of this system may perform the disaster risk assessment based on a disaster risk profile obtained by inputting input data, including 3D point cloud terrain data acquired by the terrain data acquisition unit, image capture data acquired by the image capture unit, and hazard data acquired by the hazard data acquisition unit, into a learning model that has been pre-trained to recognize the correlation between the input data and a disaster risk profile (disaster occurrence locations and their impact areas, as well as the frequency and scale of disaster occurrences, etc.) for predicting disaster risk. By adopting this configuration, it becomes possible to significantly improve the accuracy and efficiency of disaster risk assessment. By using a learning model that predicts the degree of disaster risk based on a wide variety of high-dimensional input data, such as 3D point cloud terrain data, image data, and hazard data, an objective and consistent assessment that does not depend on human subjectivity can be achieved.
[0014] Furthermore, the machine learning-based risk assessment unit can automatically identify potential factors and correlations of disaster risk by learning from past disaster data and actual disaster histories. This allows for flexible responses to complex causal relationships that are often overlooked in conventional rule-based risk assessments, as well as to newly emerging risk scenarios. Furthermore, since the learning model can be retrained as the collected data is updated, it becomes possible to perform dynamic risk assessments that take into account the latest topographic changes and weather conditions. This makes it possible to strongly support real-time risk assessment and rapid decision-making in situations where a disaster is progressing moment by moment. In addition, by automating the disaster risk assessment process, the time and labor required for the assessment work are reduced, and the risk assessment of a wide area can be efficiently performed. Further, the assessment results are immediately passed on to the risk map creation section and the countermeasure planning section, realizing the speeding up and efficiency improvement of the entire disaster response. Thus, by providing the risk determination section using the learning model, this system is useful for improving the accuracy of disaster risk assessment, prompt response, and formulation of long-term disaster prevention plans, and can greatly contribute to improving the safety of the local community.
Effects of the Invention
[0015] As described above, according to the disaster prevention and mitigation countermeasure support system, disaster prevention and mitigation countermeasure support method, and disaster prevention and mitigation countermeasure support program according to the present invention, the following effects can be obtained. First, it becomes possible to collect high-precision and wide-range data. By acquiring 3D point cloud terrain data, imaging image data, and positioning data by a moving body utilizing mobile mapping technology, detailed and comprehensive information on the disaster risk area can be quickly collected. In addition, by utilizing the hazard data acquisition section installed other than the moving body, flood risk and landslide disaster risk can be comprehensively grasped, and a multi-faceted risk assessment can be realized. Second, by integrally managing the collected data in the data integration storage section and evaluating the disaster risk in real time, a dynamic risk assessment reflecting the latest terrain information and weather conditions becomes possible. As a result, quick and accurate judgment becomes possible, and basic information for taking appropriate action before the occurrence of a disaster is provided. Third, based on the risk assessment result in the disaster risk assessment section, by automatically issuing an alarm through the alarm issuing section, quick information transmission to residents and related organizations becomes possible, and evacuation behavior and initial response are supported. In addition, by visualizing the disaster risk on a map by the risk map creation section, local residents can intuitively grasp the risk, and it is promoted to take appropriate actions. Fourthly, by providing a policy-making department for formulating disaster prevention and mitigation measures, it becomes possible to formulate specific and efficient disaster prevention plans according to the degree of disaster risk. Furthermore, by the policy-making department's contribution to the construction of long-term disaster prevention strategies, the safety of sustainable local communities can be improved. Fifthly, by adopting a risk judgment unit that utilizes a learning model, the evaluation accuracy of disaster risks is improved, enabling objective evaluations that do not depend on human subjectivity. Also, since the learning model is dynamically updated as new data is added, flexible risk evaluations that reflect the latest terrain and weather conditions are possible. Sixthly, by integrating the entire system, the collected data and evaluation results are electronically managed and quickly and accurately shared among relevant institutions, thus strengthening the cooperation system during disasters. As a result, the efficiency of disaster response can be improved, and it becomes possible to minimize damage. As described above, the present invention comprehensively solves the problems of conventional disaster prevention and mitigation systems, and by realizing high-precision data collection, real-time risk assessment, rapid warning issuance, and efficient formulation of disaster prevention plans, it is possible to greatly improve the safety and disaster prevention capabilities of local communities.
Brief Explanation of Drawings
[0016] [Figure 1] It is a schematic configuration diagram showing the overall configuration of the disaster prevention and mitigation measure support system according to the present invention. [Figure 2] It is a flowchart showing a series of processing flows including the risk assessment of disasters. [Figure 3] It is a diagram showing a configuration example of the machine learning device used in the present invention.
Embodiments for Carrying out the Invention
[0017] Hereinafter, embodiments of the disaster prevention and mitigation measure support system according to the present invention will be described with reference to the accompanying drawings. Figure 1 shows the overall configuration of the disaster prevention and mitigation support system S. This disaster prevention and mitigation support system S utilizes a mobile data acquisition system (MDCS) which includes a mobile body M that can move within disaster risk areas, an on-board sensor mounted on the mobile body M to collect various data to support disaster prevention and mitigation measures, and a positioning sensor 3 mounted on the mobile body M to collect positioning data of the mobile body. As the mobile device M, it is possible to use manned or unmanned vehicles or unmanned aerial vehicles F such as drones, but here we will explain the case where a vehicle (mobile mapping vehicle) is used.
[0018] This disaster prevention and mitigation support system S integrates and stores various data collected by on-board sensors mounted on the mobile mapping vehicle M, as well as various data collected by sensors installed elsewhere, for example, on a cloud server 40, to identify areas with a high risk of disaster.
[0019] 1. Sensors and devices used in mobile mapping vehicles to assess disaster risks (flood risk, landslide risk, etc.) In Figure 1, the main sensors mounted on the mobile mapping vehicle M are the following: LiDAR1, high-resolution camera2, and positioning sensor3 (GPS and inertial measurement device for collecting positioning data). (1)LiDAR (Light Detection and Ranging) LiDAR1 is a sensor that uses laser light to accurately measure the elevation differences, slope gradients, and structural shapes of the terrain in disaster-risk areas. It is installed on the roof and sides of the mobile mapping vehicle M and is used to collect terrain data and generate detailed 3D models of buildings and road infrastructure. This sensor provides detailed information on terrain characteristics that affect the risk of floods and landslides. The data that can be collected by this LiDAR1 primarily includes the following: (a) Topographic data (DEM: Digital Elevation Model) (b) Topographic elevation, gradient, unevenness, and road infrastructure data (c) Location, width, gradient, and condition of the road (d) Infrastructure data (e) Infrastructure information such as bridges, embankments, and tunnels
[0020] (2) High-resolution camera High-resolution camera 2 is a device that records surrounding visual information in high definition. It is mounted on the roof or side of mobile mapping vehicle M and photographs buildings, vegetation, and infrastructure along roads and in target areas. This makes it possible to understand land use and evaluate the condition of infrastructure. For example, it can capture roadside scenery, buildings, and vegetation in high-resolution images and provide supplementary data necessary for flood risk assessment. The data that can be collected by this high-resolution camera 2 mainly includes the following: (a) Road data (b) Visual images of roads (c) Vegetation data (d) Distribution, types, and density of vegetation (e) Building data (f) Visual information on the location and structure of the building (g) Infrastructure data (h) Image data of infrastructure facilities
[0021] (3)GPS(Global Positioning System) GPS is a device that precisely determines the location of a vehicle. It is installed on the roof of the mobile mapping vehicle M and is used to correlate data collected by LiDAR1 and camera2 to a precise geospatial location. This ensures accurate location information for the collected data. In other words, it is used to map the collected data to a precise geospatial location. By measuring the vehicle's latitude, longitude, and altitude, geographical information and data are accurately linked. The data that can be collected by GPS primarily includes the following: (a) Location information (b) Precise geographical coordinates assigned to data collected by LiDAR or cameras
[0022] (4)IMU(Inertial Measurement Unit) An IMU (Infrared Measurement Unit) is a device that measures the movement of a vehicle. It is installed inside a mobile mapping vehicle and is used to correct the position of data acquired by LiDAR1 and camera2. In other words, by measuring the acceleration and angular velocity of the mobile mapping vehicle, it plays a role in assisting the alignment of the data, thereby improving the accuracy of the collected data. The data that can be collected by the IMU mainly includes the following: (a) Movement and posture information (b) Vehicle movement (acceleration, rotation rate): This is recorded and used for geographical correction of the data.
[0023] 2. Sensors and devices installed outside of mobile mapping vehicles (installed in a manner not belonging to mobile mapping vehicles) that are used to assess flood risk. The main sensors and equipment used, other than in mobile mapping vehicles, vary depending on the disaster risk to be evaluated. A. Sensors and devices used to assess flood risk, etc. First, the sensors and devices used to assess flood risk, as shown in Figure 1, include river observation devices such as rain gauges, weather radar, weather satellites, and water level gauges, as well as satellite imaging devices, drainage pump monitoring devices, and drones.
[0024] (1)Rain gauge Rain gauge 20 is a fundamental sensor for collecting rainfall data in flood risk assessment. This sensor measures rainfall in real time and has the function of recording rainfall patterns and extreme weather events. The data acquired by the rain gauge shows rainfall and its change patterns in detail, and is important basic information when analyzing the risk of flood occurrence. For example, it is used for purposes such as monitoring whether rainfall exceeds a threshold. These devices are installed at observation points and ground weather stations in areas with a high risk of flooding, and collect data stably and continuously. The data that can be collected by rain gauge 20 mainly consists of the following: (a) Rainfall data (b) Rainfall amount and rainfall patterns
[0025] (2) Weather radar Weather radar 21 is a device that observes rainfall amount, rainfall intensity, and rainfall duration, and is used to obtain wide-area rainfall information. This device has the function of measuring rainfall intensity and distribution using radar waves. The data collected by weather radar makes it possible to understand the location, intensity, and extent of rainfall in real time, and greatly contributes to flood risk prediction. Weather radar 21 is mainly installed in observation facilities of the Japan Meteorological Agency and local governments, and is responsible for collecting data over a wide area. The data that can be collected by weather radar 21 mainly consists of the following: (a) Rainfall data (b) Intensity, extent, and duration of rainfall
[0026] (3) Weather satellite Weather satellite 22 is positioned in Earth orbit to collect wide-area rainfall data. This satellite observes cloud movement and rainfall distribution, providing global weather data. Satellite data is a crucial source for understanding regional rainfall distribution and long-term weather changes. This provides broad and detailed information for assessing flood risk. The data that can be collected by weather satellite 22 mainly consists of the following: (a) Rainfall data (b) Widespread rainfall patterns and extreme weather events
[0027] (4) River observation equipment (water level gauges, flow meters, etc.) The river observation device 23 is a device that monitors river water levels and flow rates to assess flood risk. This device has the function of measuring water levels and flow rates in real time and understanding the condition of the river. The river data serves as basic data for assessing the likelihood of flooding and issuing warnings. These devices are installed along rivers and play an extremely important role in flood risk assessment. The data that can be collected by the river observation device 23 mainly consists of the following: (a) River data (b) River water level (c) River flow rate, flow rate changes
[0028] (5) Satellite imaging equipment Satellite imagery device 24 is used to observe land use and topographic changes over a wide area. This device is used to identify areas at risk of flooding and to assess flood risk. The data that can be collected by the satellite imagery device 24 mainly consists of the following: (a) Topographic data (b) Elevation differences and gradients of the topography over a wide area (c) Land use data (d) Types and distribution of land use such as urban areas, farmland, and forests (e) Vegetation data (f) Wide-area data on forest distribution and vegetation
[0029] (6) Monitoring device for drainage pumps The drainage pump monitoring device 25 is a device that monitors the location and performance of the drainage pump. This device is used to understand the operating status of the drainage pump and to ensure appropriate wastewater treatment. It is installed at the site where the drainage pump is installed or at a centralized management facility. The data obtained from the drainage pump monitoring device 25 is as follows: (a) Location and performance of the drainage pump
[0030] (7) Drones Drone 26 will be used for aerial observation of rivers and flood-affected areas. The drone is equipped with a high-resolution camera and various sensors, making it possible to grasp the condition of rivers and the extent of flood impact in detail from the air. The drone will be operated in affected areas and along rivers, and has the advantage of being able to acquire data even in places that are difficult to access by humans. This will allow for a rapid and accurate assessment of the impact of floods. The data that can be collected by drone 26 mainly consists of the following: (a) River data (b) Topographic data of the area around the river (c) Vegetation data (d) Distribution and density of vegetation
[0031] Furthermore, databases used to assess flood risk include land use databases such as GIS27, drainage facility databases28, and disaster history databases29. (1) Land use database (GIS, remote sensing equipment) The Land Use Database 27 is used to understand land use conditions when assessing flood risk. This database records the distribution of urban areas, agricultural land, and forests, and serves as an important source of information for predicting flood impacts. The data is collected through GIS (Geographic Information Systems) and remote sensing devices. Land use data is used to analyze which areas may be affected and to what extent. The data that can be collected by the land use database 27 mainly consists of the following: (a) Land use and distribution, such as urban areas, farmland, forests, grasslands, and water areas. (b) Changes in land use
[0032] (2) Drainage Facility Database The drainage facility database 28 provides information for understanding the status of flood prevention infrastructure. This database records the location and performance of dams, levees, drainage pumps, and other facilities, containing essential information for planning flood control measures. Drainage facility data is primarily obtained from municipal infrastructure databases and is used in regional disaster prevention plans. The data that can be collected by the wastewater treatment facility database 28 mainly consists of the following: (a) Location and performance of dams, embankments, drainage pumps, etc.
[0033] (3) Disaster history database The Disaster History Database 29 is a database for utilizing past disaster records in flood risk assessment. This database records past flood locations, magnitudes, and affected areas, providing important reference information for predicting future flood risks. The database is managed online or locally and is used as fundamental data for disaster risk assessment and disaster prevention measures. The data that can be collected by the disaster history database 29 mainly consists of the following: (a) Disaster history (b) Past disaster locations, scale, and frequency These sensors, devices, and databases provide the data necessary for flood risk assessment, each according to its respective role. By integrating multifaceted information such as rainfall data, river conditions, land use, infrastructure status, and past disaster history, a comprehensive flood risk assessment becomes possible.
[0034] B. Sensors and devices used to assess the risk of landslides, etc. Next, the sensors, devices, and databases used to assess the risk of landslides are, as shown in Figure 1, rain gauges, weather radar, weather satellites, geological survey equipment, drones, and vegetation data collection equipment. (1)Rain gauge Rain gauge 30 measures rainfall in real time and collects data on rainfall patterns and the frequency of extreme weather events. This data is acquired at ground-based weather stations. The data that can be collected by rain gauge 30 mainly consists of the following: (a) Rainfall data (b) Rainfall amount and rainfall patterns
[0035] (2) Weather radar Weather radar 31 is used to observe rainfall information over a wide area. It measures the intensity and distribution of rainfall using radar waves and is operated by the Japan Meteorological Agency and local government observation facilities. The data that can be collected by weather radar 31 mainly consists of the following: (a) Rainfall data (b) Intensity, extent, and duration of rainfall
[0036] (3) Weather satellite Weather satellite 32 is used to collect wide-area rainfall data. It observes cloud movements and rainfall distribution to provide global weather information. The data that can be collected by weather satellite 32 mainly consists of the following: (a) Rainfall data (b) Widespread rainfall patterns and extreme weather events
[0037] (4) Satellite imaging equipment The satellite imagery device 33 is a data collection tool for understanding slope gradient, topographic irregularities, and elevation differences. It is also used to observe vegetation and slope conditions. This device is used to identify and assess landslide risks. It is installed at a remote sensing center. The data that can be collected by the satellite imagery device 33 mainly consists of the following: (a) Topographic data (b) Elevation differences and gradients of the terrain (c) Land use data (d) Types and distribution of land use such as urban areas, farmland, and forests (e) Vegetation data (f) Wide-area data on forest distribution and vegetation
[0038] (5) Geological survey equipment (boring equipment, etc.) The geological survey device 34 is used to measure the physical properties of soil and geological strata. It is operated at geological survey sites to collect data on soil type and geological structure. The data that can be collected by the geological survey device 34 mainly consists of the following: (a) Geological data (b) soil type (c) Structure of the geological layers
[0039] (6) Drones The Drone 35 will be used for aerial observation of slopes and vegetation. Equipped with high-resolution cameras and sensors, it will acquire data on slope conditions and vegetation distribution from the air. The data that can be collected by Drone 35 mainly consists of the following: (a) River data (b) River flow (c) Vegetation data (d) Distribution and density of vegetation
[0040] (7) Vegetation data collection device The vegetation data collection device 36 is used to collect vegetation data. This device utilizes satellite imagery and remote sensing technology. The data that can be collected by the vegetation data collection device 36 mainly consists of the following data: (a) Types of vegetation (b) Distribution and density of vegetation
[0041] Furthermore, the Disaster History Database 37 is used as a database for evaluating the risk of landslides. Disaster History Database The disaster history database 37 stores past landslide disaster data and uses it for evaluation and prediction. This database is managed on an online or local system. The data that can be collected by the disaster history database 37 mainly consists of the following: (a) Disaster history (b) Past disaster locations, scale, and frequency These sensors and devices are used to monitor and assess disaster risks according to their respective purposes, playing a crucial role in forming the foundation of disaster prevention and mitigation measures. By integrating this data, it is possible to realize effective disaster prevention and mitigation measures.
[0042] Data collected by each sensor mounted on the mobile mapping vehicle M is sent in real time via the data acquisition transmission unit 10 and the network 11 to the data center's management server and cloud server. Data acquired from sensors, devices, and various databases installed outside the mobile mapping vehicle is also sent to the data center and cloud server via a transmission control unit (not shown) and the network 11. Hereafter, the server to which the data is sent will be referred to as the cloud server 40. Here, the various sensors, devices, and databases shown in Figure 1 that do not belong to the mobile mapping vehicle M correspond to the hazard data acquisition unit that acquires hazard data (reference data for evaluating flood risk, reference data for evaluating sediment-related disaster risk) in disaster risk areas.
[0043] The cloud server 40 receives the transmitted acquired data with the acquired data receiving unit 41, and integrates and stores the acquired data (first data group, second data group) received by the acquired data receiving unit 41 in the data integration unit 42, associating it with the positioning data (motion parameters and position information) of the moving body M identified by the positioning sensor 3. The cloud server 40 then analyzes the data stored in the data integration unit 42 in the data analysis unit 43, and based on the analysis results, processes risk information in the risk information processing unit 44, such as evaluating (classifying) disaster risks and creating risk maps. The risk information processing unit 44 also performs processing such as displaying analysis results and evaluation results on the display unit (dashboard) 101 as needed, or issuing warnings via the warning issuing device 102. It also integrates data related to flood risk and landslide risk onto the GIS 103 and visualizes it on a map.
[0044] 3. Methods for assessing disaster risk in disaster-prone areas. Next, we will explain how to assess disaster risk in disaster-prone areas (see Figure 2). A. Methods for assessing flood risk The flood risk assessment is carried out using the following procedure. (1) Collection of necessary data (Step S01) To assess flood risk, the following data is required. This data is collected using various sensors and devices. (a) Topographic data (DEM: Digital Elevation Model) Required information: Elevation differences, gradients, and topography. Data collection method: Data is collected using LiDAR mounted on mobile mapping vehicles, aerial photographs, and satellite images. (b) Rainfall data Required information: Past rainfall amounts, rainfall patterns, and frequency of extreme weather events. Data collection methods: Acquired using rain gauges at weather stations, weather radar, and weather satellites. This allows us to model rainfall intensity and frequency. (c) River data Required information: River flow rate, water level, drainage basin area, and topographic characteristics of the drainage basin. Collection method: Water level gauges and flow meters at river observation stations, as well as drones, are used for collection. This will help identify factors that increase the risk of flooding. (d) Land use data Required information: Types and distribution of land use, such as urban areas, farmland, and forests. Data collection method: Obtained using satellite imagery and GIS. This helps identify areas that are prone to flooding. (e) Drainage facility data Required information: Location, performance (drainage capacity), and durability of facilities such as dams, levees, and drainage pumps. Collection method: Obtained from municipal infrastructure databases and on-site surveys. This allows us to evaluate the limitations and challenges of drainage capacity. By collecting and analyzing this data, it is possible to accurately assess flood risk.
[0045] (2) Data integration and analysis (Step S02) Next, the collected data will be integrated to conduct a flood risk assessment. (a) Analysis of topographic data Based on data acquired from LiDAR, satellite imagery, and aerial photographs (terrain data: DEM), the elevation differences and gradients of the terrain are analyzed to identify areas prone to water flow and accumulation during rainfall. Furthermore, low-lying areas and water flow paths susceptible to flooding are identified. This process utilizes GIS (Geographic Information System) to visualize the data. (b) Analysis and simulation of rainfall data We analyze rainfall characteristics in specific regions based on past rainfall amounts, rainfall patterns, and the frequency of extreme weather events. We also use past rainfall data to simulate the likelihood of flooding during future rainfall events. (c) Analysis of river data (modeling of river flow rate and water level) The risk of river flooding is assessed based on river flow rate, water level, and drainage basin area. In other words, the risk of river overflow during floods is modeled based on river data. (d) Land use impact analysis Using land use data for urban areas, farmland, forests, etc., the extent of impact during a flood will be evaluated. In other words, areas that will be greatly affected by floods (such as urban areas and areas around critical infrastructure) will be identified. Furthermore, satellite imagery and GIS will be used to analyze the relationship between current land use and flood risk. These integrated assessments make it possible to clarify the overall picture of flood risk. (e) Performance evaluation of drainage facilities This involves evaluating the durability and drainage capacity of infrastructure facilities such as dams, levees, and drainage pumps. The analysis is based on information obtained from municipal infrastructure databases and on-site surveys.
[0046] (3) Risk assessment (risk classification) and mapping Based on the integrated data, flood risk is assessed (classified) (Step S03), and high-risk areas are visualized as a risk map (Step S04). (a) Risk assessment (risk classification) Flood risk is classified into "high risk," "medium risk," and "low risk." This classification uses a combination of topography, rainfall intensity, river flow rate, and land use data. (b) Creating a risk map We will use GIS to create risk maps and visualize the extent of impact. By providing the created risk maps to government agencies and local residents, they will be used for flood control measures and evacuation plans.
[0047] (4) Model verification and improvement The flood risk model created will be validated using records of actual flood events and disaster record databases. The model will be improved as needed to enhance its accuracy (Step S05).
[0048] (5) Planning of disaster prevention and mitigation measures Based on the flood risk assessment results, the following disaster prevention and mitigation measures will be planned (Step S06). (a) Strengthen levees and drainage facilities in areas at high risk of flooding. (b) Develop evacuation plans for residents in high-risk areas. (c) Establish evacuation shelters and develop flood warning systems. Flood risk assessment is performed by collecting and analyzing a wide range of data, including topographic data, rainfall data, and river data, and then integrating them. Through this process, it becomes possible to assess the likelihood and impact of flood occurrence and automatically formulate appropriate disaster prevention and mitigation measures.
[0049] B. Methods for assessing sediment-related disaster risk The assessment of landslide risk is carried out using the following procedure. (1) Collection of necessary data (Step S01) To assess the risk of landslides, the following data is necessary. This data is collected using various sensors and devices. (a) Topographic data (DEM: Digital Elevation Model) Required information: slope gradient, terrain irregularities, elevation difference. Data collection method: Data is collected using LiDAR mounted on mobile mapping vehicles, aerial photographs, and satellite images. (b) Geological data Required information: soil type, stratum structure, and geological characteristics. Data collection methods: Geological surveys, data collection using boring equipment, and utilization of geological maps. (c) Rainfall data Required information: Past rainfall amounts, rainfall patterns, and frequency of torrential downpours. Data collection methods: Acquired using rain gauges at weather stations, weather radar, and weather satellites. (d) Vegetation data Required information: Presence or absence of forests, type and distribution of vegetation. Data collection methods: Using cameras on mobile mapping vehicles, satellite imagery, and remote sensing. (e) Past disaster history Required information: Locations of past landslides, scale and frequency of disasters. Collection methods: Referencing disaster record databases and historical documents.
[0050] (2) Data integration and analysis (Step S02) Next, the collected data will be integrated to conduct a landslide risk assessment. (a) Evaluation of slope stability Using topographic data (DEM), the slope gradient and topographic characteristics of the slope are analyzed, and the stability of the slope is assessed. Steep slopes and convex terrain are identified as factors that increase the risk of sediment-related disasters. (b) Analysis of geological characteristics Geological data is analyzed to evaluate soil strength, permeability, and geological structure. Weak ground and easily inundated soil are factors that increase risk. (c) Assessment of the impact of rainfall Using historical rainfall data and prediction models, we simulate the impact of torrential downpours and prolonged rainfall on slope stability. (d) Assessment of the role of vegetation Based on vegetation data, we will evaluate the impact of the presence or absence of forests and grasslands on slope stability. While forests contribute to controlling soil erosion, we must also consider the possibility that excessive logging could increase the risk. (e) Comparison with past disaster history We analyze past disaster locations and conditions to identify their relevance to current risk areas.
[0051] (3) Risk assessment (risk classification) and mapping Based on the integrated data, the risk of landslides is assessed (classified) (Step S03), and high-risk areas are visualized as a risk map (Step S04). (a) Risk assessment (risk classification) Landslide risks are classified into "high risk," "medium risk," and "low risk." The classification uses a combination of topographic, geological, rainfall, and vegetation data. (b) Creating a risk map The created risk map will be visualized using GIS and provided to government agencies and local residents. This map will be used for evacuation plans and disaster prevention measures during emergencies.
[0052] (4) Model verification and improvement The created risk model is validated by comparing it with past disaster history and field surveys. Data necessary to improve accuracy is added, and the model is improved (Step S05).
[0053] (5) Planning of disaster prevention and mitigation measures Based on the results of the risk assessment, specific disaster prevention and mitigation measures are planned (Step S06). (a) Installation of sediment control dams and implementation of slope stabilization work in high-risk areas. (b) Creating evacuation plans for residents and conducting evacuation drills. (c) Management of deforestation and implementation of vegetation restoration projects. Landslide risk assessment is conducted by integrating and analyzing a wide range of data, including topography, geology, rainfall, vegetation, and past disaster history. Through this process, high-risk slopes and areas can be identified, and disaster prevention measures can be formulated. The assessment results are visualized as a risk map and provided to government agencies and residents.
[0054] C. Summary The assessment of flood risk and landslide risk involves collecting and integrating a wide range of data, including topographic data, rainfall data, geological data, and vegetation data. By analyzing this data, each risk can be classified into "high," "medium," and "low," and visualized as a risk map, which can then be used in the development of disaster prevention plans and evacuation plans.
[0055] 4. Regarding the introduction of the learning model Machine learning models may be used to assess the risk of floods and landslides and to generate risk maps, as described above. By building such learning models, areas with a high probability of disaster occurrence can be identified, which can be used to plan disaster prevention and mitigation measures. For example, a machine learning device 50 may be installed on the cloud server 40 that uses a learning model based on data integrated by the data integration unit 42 to estimate a disaster risk profile (such as the location of a disaster, the extent of its impact, and the frequency and scale of disasters) for predicting disaster risk (see Figure 1). As shown in Figure 3, such a machine learning device 50 includes an input data acquisition unit 51 that acquires data sets obtained from 3D point cloud terrain data acquired by LiDAR 1, image data acquired by high-resolution camera 2, and hazard data acquired by hazard data acquisition unit as input data; a label acquisition unit 52 that acquires disaster risk profiles (disaster occurrence locations and their impact areas, and the frequency and scale of disaster occurrences, etc.) as labels for predicting disaster risk; and a learning model construction unit 53 that constructs a learning model 55 by performing supervised learning using the input data and label pairs as training data. It is desirable to construct separate learning models for identifying areas at high risk of flooding and areas at high risk of landslides, as described below. (1) Learning models for identifying areas at high flood risk (a) Input data Terrain data (DEM): Elevation, slope, and terrain shape. Rainfall data: Rainfall amount, rainfall pattern, frequency of rainfall events River data: flow rate, water level, river shape Land use data: Distribution of urban areas, farmland, and forests. Drainage facility data: Location and performance of dams, levees, and drainage pumps. (b) Output data (training data) Past flood locations and their impact areas Flood frequency and scale
[0056] (2) Learning models for identifying areas at high risk of sediment-related disasters (a) Input data Terrain data (DEM): Elevation, slope, and terrain shape. Geological data: Soil type, geological structure Rainfall data: Rainfall amount, rainfall pattern Vegetation data: Presence or absence of forest, type of vegetation Past disaster history: Location and extent of landslides (b) Output data (training data) Past landslide locations and their impact areas Frequency and scale of landslides
[0057] Therefore, by establishing such a learning model, the assessment of disaster risk (classification of disaster risk) is performed based on a disaster risk profile obtained by inputting input data, including 3D point cloud terrain data acquired by LiDAR1, image data acquired by high-resolution camera2, and hazard data acquired by the hazard data acquisition unit, into a learning model 55 that has been pre-trained to recognize the correlation between the input data and a disaster risk profile (disaster occurrence location and its affected area, and the frequency and scale of disaster occurrence, etc.) for predicting disaster risk.
[0058] (3) Actual operation The trained learning models are integrated into platforms such as GIS (Geographic Information Systems), and real-time data is input to predict flood and landslide risks. This enables the activation of warning systems and allows for a rapid response to disasters. In other words, the output results of the generated learning model are used as follows: (i) Risk assessment Based on the results generated by the learning model, the risk of floods and landslides is classified as "high," "medium," or "low." (ii) Generation of a risk map The model's output is integrated into a GIS (Geographic Information System) to create a risk map. The maps visually indicate hazardous areas and are used in disaster prevention and evacuation plans. (iii) Real-time prediction Real-time weather and river data are input into a learning model to dynamically predict risks. (iv) Planning of disaster prevention measures Based on the risk map, specific disaster prevention measures will be planned, such as the construction of embankments and sediment control dams, and the development of evacuation plans.
[0059] (4) Examples of actual use For example, a flood risk assessment model acquires rainfall and river data in real time and generates a risk map on a GIS. Based on this risk map, high-risk areas can be identified, and warnings can be issued in advance, along with evacuation orders. Furthermore, the landslide risk assessment model analyzes rainfall and geological data on steep slopes to create a system that issues warnings when the risk of landslides increases. This will enable a swift and effective response before a disaster occurs, minimizing damage. Through the above process, it becomes possible to improve public safety by assessing disaster risks in advance and taking appropriate measures.
[0060] 5. Creating a Risk Map When evaluating flood risk and landslide risk based on the analysis results and creating a risk map, the following methods may be used. (1) Risk assessment based on analysis results A. Flood risk assessment (a) Hydrological analysis Based on rainfall data, the amount of rainfall during a specific period is predicted from past rainfall patterns. Flow analysis is used to simulate flow rates within a river basin and evaluate the possibility of river level rise and flooding. (b) Terrain analysis We analyze the topography around rivers using topographic data (DEM) to identify lowlands and natural dams. Furthermore, we perform drainage path analysis to identify the most likely water flow routes and estimate the extent of flood impact. (c) Infrastructure evaluation By evaluating the durability of drainage facilities and the capacity of drainage pumps, we will identify challenges in mitigating flood risk. Based on land use data, we will consider the impact of land use, such as urban areas and agricultural land, on flood risk. Based on these analysis results, the flood risk in each region is classified into risk levels such as "high," "medium," and "low."
[0061] B. Assessment of sediment-related disaster risk (a) Geological analysis We analyze soil types and stratum structure using geological data to assess the likelihood of landslides. We also analyze slope stability, taking into account slope gradient, presence or absence of vegetation, and geological characteristics. (b) Meteorological data analysis This system evaluates the soil saturation state caused by torrential rainfall and prolonged rainfall. Based on past landslide data, it sets thresholds for rainfall amounts considered dangerous. (c) Past disaster data Based on past disaster records, the location and scale of landslides are evaluated, and the risk level is classified. Based on these analysis results, the risk of landslides on slopes is classified into risk levels such as "high," "medium," and "low."
[0062] (2) Creation of a risk map Creating a risk map is a process of visually displaying analysis results and identifying high-risk areas. (a) Data integration All analysis results, including topographic data, rainfall data, geological data, river data, land use data, and historical disaster data, will be integrated into a GIS (Geographic Information System). (b) Setting the risk level The risk levels evaluated from each analysis result are classified as "high," "medium," and "low," and plotted on a map. Color coding (e.g., red = high risk, yellow = medium risk, green = low risk) is used to display the information visually for clarity. (c) Drawing of risk zones Flood risk zones are drawn on the map, indicating low-lying areas along rivers and areas where flooding is predicted. Landslide risk zones are identified and defined based on slope gradients and geological characteristics, indicating areas with a high probability of landslides. (d) Superposition and analysis Multiple data sets are combined to perform a comprehensive risk assessment. For example, areas where flood risk and landslide risk overlap are identified. (e) Generation of a risk map All risk zones will be integrated to create a risk map. Evacuation routes, evacuation sites, and the locations of critical infrastructure will also be displayed on the map to enhance its practicality. (f) Building a dynamic risk map We will incorporate real-time data (weather data and river data) to create a dynamically updated risk map. This will allow for immediate updates when risks change, enabling rapid response.
[0063] (3) Use of risk maps (a) Disaster prevention plans and infrastructure development Use risk maps to define evacuation routes and shelters for each region. In high-risk areas, consider installing levees and drainage pumps, and developing sediment control dams and disaster prevention nets. (b) Operation of the warning system The system monitors weather and river data in real time and updates risk maps. If the risk increases, it automatically issues evacuation advisories or orders. (c) Public education and awareness activities By publishing risk maps and providing disaster prevention drills and information, residents will become aware of the risks in their area and raise their awareness of disaster preparedness. Through these processes, risk maps become a tool that visualizes the risks of floods and landslides, enabling a rapid and effective response.
[0064] 6. Regarding the construction of an automatic alarm system To build an automated warning system, it is necessary to implement a mechanism that monitors data obtained from sensors and mobile mapping systems in real time and issues a warning when specific conditions are met. Below are specific examples of warning systems for flood risk and landslide risk.
[0065] A. Automatic flood risk warning system About the data to be used The following data is needed to address flood risk: (1) Data to be used (a) Rainfall data Sensors: Weather radar, rain gauge Data: Rainfall amount, rainfall intensity, rainfall duration We collect rainfall data, intensity, and duration using weather radar and rain gauges. (b) River data Sensors: water level gauge, flow meter Data: River water level, flow rate Water level gauges and flow meters are used to measure the water level and flow rate of rivers. (c) Topographic data (DEM) Sensors: LiDAR, drone Data: Analysis of terrain elevation differences and drainage routes. We will use LiDAR and drones to analyze terrain elevation differences and drainage routes. (2) Warning conditions Based on flood risk, the following conditions will be set. (a) Rainfall conditions This occurs when the cumulative rainfall within a certain period exceeds a set threshold (e.g., 50 mm or more of rainfall in one hour). When heavy rainfall is observed in a short period of time (e.g., more than 20 mm of rainfall in 30 minutes) (b) River conditions When the river water level reaches a dangerous level (e.g., exceeds the designated water level). When the flow rate increases rapidly, the risk of flooding rises. (c) Compound condition When heavy rainfall and rising river levels are observed simultaneously. This refers to situations where heavy rainfall is observed in specific geographical areas, such as lowlands.
[0066] B. Automatic warning system for landslide risk (1) Regarding the data to be used The following data is necessary to address the risk of landslides. Rainfall data We collect rainfall data, intensity, and duration using weather radar and rain gauges. Geological data Using geological sensors and boring data, we measure soil types and the structure of geological layers. Topographic data (DEM) We use LiDAR and drones to analyze the elevation differences and slope gradients of the terrain. Vegetation data Satellite imagery and drones are used to observe the types of vegetation and the presence or absence of forests. (2) Warning conditions Based on the risk of landslides, the following conditions will be set. (a) Rainfall conditions - When the cumulative rainfall within a certain period exceeds a set threshold (e.g., 50 mm or more of rainfall in one hour). • When heavy rainfall is observed in a short period of time (e.g., more than 20 mm of rainfall in 30 minutes). (b) Slope conditions • When rainfall is observed on a slope exceeding a certain gradient (e.g., a slope of 30 degrees or more). • When rainfall is observed in areas with loose soil or unstable geological formations. (c) Compound condition • When heavy rainfall and steep slopes are observed simultaneously. • When heavy rainfall and areas with unstable geological conditions are observed simultaneously.
[0067] C. Configuration of the automatic warning system (1) Data acquisition module Data is collected in real time from various sensors, transmitted to a central server, and stored in a database for analysis. (2) Data Analysis Module The collected data is analyzed in real time, and risk assessments are performed based on various thresholds and conditions. (3) Warning Emitting Module Based on the risk assessment results, an alert will be issued if the warning conditions are met. Notification methods include SMS, email, disaster prevention apps, and disaster prevention radio broadcasts. (4) Management Dashboard The management dashboard includes the following features: Real-time data graphs and map displays A heatmap function that displays risk levels on a map using different colors. Storage of data for managing and analyzing warning history Administrator manual intervention function
[0068] D. Assessing the situation in disaster-stricken areas and supporting rescue operations during disasters. (1) Assessing the situation in the disaster-stricken areas Using drones and mobile mapping vehicles, we will grasp the overall picture of the disaster area. We will collect real-time data, perform image analysis and generate 3D models to evaluate the extent and scope of the damage. (2) Rescue operation support Based on risk maps, rescue operations are prioritized. Safe evacuation routes are identified and provided to rescue teams. A resource management system is used to efficiently allocate rescue teams, medical teams, and supplies. Information is shared between rescue teams and the command center via real-time communication, and information from residents is also accepted.
[0069] E. Examples of specific warning systems (1) Flood risk warning system Rainfall data collection: Data is collected in real time using weather radar and rain gauges. Example: When the cumulative rainfall in one hour exceeds 50 mm. River water level monitoring: Monitoring is performed using water level gauges. Example: When the water level exceeds the designated danger level. Warning issued: If the above conditions are met, residents will be notified of a warning via SMS or a disaster prevention app. (2) Sediment-related disaster risk warning system Collection of rainfall data: This data is collected using weather radar and rain gauges. Example: When the cumulative rainfall in one hour exceeds 50 mm. Slope data monitoring: Evaluate slope stability based on topographic and geological data. Example: When the slope is 30 degrees or more and the soil is unstable. Warning issued: If the above conditions are met, residents will be notified of a warning via SMS or a disaster prevention app.
[0070] F. Implementation Considerations When implementing an automated warning system, the following points must be considered: Ensure data integrity. Data collection, analysis, and warning issuance will be performed in real time. Ensure redundancy so that the overall function is maintained even if a part of the system fails. We will conduct educational and awareness-raising activities to ensure that residents understand the warnings and can take swift action.
[0071] G. Conclusion The automated warning system performs real-time risk assessments based on data obtained from sensors and mobile mapping systems, and issues immediate warnings when specific conditions are met. This strengthens disaster prevention and mitigation measures. This system enables risk prediction and rapid response before a disaster occurs, and is expected to minimize damage.
[0072] Thus, this disaster prevention and mitigation support system conducts regular data collection and pre-assessment to predict and evaluate the risks of floods and landslides, providing static topographic data and risk assessments, and is primarily used for preventative purposes. Furthermore, the aforementioned disaster prevention and mitigation support system S can also be provided in the form of a program (disaster prevention and mitigation support program) that causes a computer to execute each step of the aforementioned disaster prevention and mitigation support method. [Explanation of symbols]
[0073] 1 LIDAR 2 high-resolution cameras 3. Positioning Sensor 42 Data Integration Department 43 Data Analysis Department 50 Machine Learning Devices 55 Learning Models S Disaster Prevention and Mitigation Support System M Mobile Unit (Mobile Mapping Vehicle)
Claims
1. A terrain data acquisition unit that acquires 3D point cloud terrain data obtained by 3D scanning terrain data of disaster risk areas, The image acquisition unit acquires image data obtained by imaging the aforementioned disaster risk area, A positioning data acquisition unit that acquires positioning data, comprising at least a GPS receiver and an inertial measuring instrument, A mobile body equipped with the terrain data acquisition unit, the image acquisition unit, and the positioning data acquisition unit, A hazard data acquisition unit is provided in a manner not belonging to the mobile body in order to evaluate the risk of natural disasters in the aforementioned disaster risk area and to acquire hazard data, A data integration storage unit that integrates and stores the 3D point cloud terrain data, image data, and positioning data acquired while the moving body is moving or while the moving body is stationary, and the hazard data acquired by the hazard data acquisition unit. A disaster risk assessment unit evaluates the disaster risk in the disaster risk area based on the data integrated in the aforementioned data integration storage unit, A disaster prevention and mitigation support system characterized by being equipped with the following features.
2. The system further comprises an alarm issuing unit that issues an alarm when the disaster risk assessed by the disaster risk assessment unit is determined to be equal to or greater than a predetermined risk. The disaster prevention and mitigation support system according to claim 1.
3. The system further comprises a risk map creation unit that visualizes areas where the disaster risk assessed by the disaster risk assessment unit exceeds a predetermined risk level as a risk map on a map. A disaster prevention and mitigation support system according to claim 1 or 2.
4. Based on the degree of risk assessed by the aforementioned disaster risk assessment department, the system further comprises a disaster prevention and mitigation planning department that formulates disaster prevention and mitigation measures. The disaster prevention and mitigation support system according to claim 1.
5. The disaster prevention and mitigation support system according to claim 1, wherein the mobile body includes at least a mobile mapping vehicle, and the disaster risk assessment unit includes the assessment of flood risk or landslide risk in the disaster risk area.
6. The disaster risk assessment conducted by the aforementioned disaster risk assessment unit is as follows: The disaster prevention and mitigation support system according to claim 1, characterized in that it is implemented based on a disaster risk profile obtained by inputting input data, which includes 3D point cloud terrain data acquired by the terrain data acquisition unit, image capture data acquired by the image capture unit, and hazard data acquired by the hazard data acquisition unit, into a learning model that has been pre-trained to recognize the correlation between a disaster risk profile for predicting disaster risk and the input data.
7. A terrain data acquisition unit that acquires 3D point cloud terrain data obtained by 3D scanning terrain data of disaster risk areas, The image acquisition unit acquires image data obtained by imaging the aforementioned disaster risk area, A positioning data acquisition unit that acquires positioning data, comprising at least a GPS receiver and an inertial measuring instrument, A mobile body equipped with the terrain data acquisition unit, the image acquisition unit, and the positioning data acquisition unit, A disaster prevention and mitigation support method using a hazard data acquisition unit, which is provided in a manner not belonging to the mobile body for the purpose of evaluating the risk of natural disasters in the aforementioned disaster risk area and for acquiring hazard data, A data integration and storage step involves integrating and storing the 3D point cloud terrain data, image data, and positioning data acquired while the moving body is moving or while the moving body is stationary, and the hazard data acquired by the hazard data acquisition unit. A disaster risk assessment step, which evaluates the disaster risk in the disaster risk area based on the data integrated in the data integration and storage step, A disaster prevention and mitigation support method characterized by comprising the following:
8. The system further comprises an alarm issuance step, which issues an alarm if the disaster risk assessed in the aforementioned disaster risk assessment step is determined to be at or above a predetermined risk level. The disaster prevention and mitigation support method described in item 7.
9. The system further includes a risk map creation step, which visualizes areas where the aforementioned disaster risk exceeds a predetermined risk level on a map as a risk map. The disaster prevention and mitigation support method described in item 7.
10. Based on the degree of risk assessed in the aforementioned disaster risk assessment step, the system further includes a countermeasure planning step for formulating disaster prevention and mitigation measures. The disaster prevention and mitigation support method described in item 7.
11. The assessment of disaster risk in the aforementioned disaster risk assessment step is: The disaster prevention and mitigation support method according to claim 7, characterized in that it is carried out based on a disaster risk profile for predicting disaster risk, which is obtained by inputting input data, including 3D point cloud terrain data acquired by the terrain data acquisition unit, image capture data acquired by the image capture unit, and hazard data acquired by the hazard data acquisition unit, into a learning model that has been pre-trained to recognize the correlation between a disaster risk profile for predicting disaster risk and the input data.
12. A disaster prevention and mitigation support program for causing a computer to perform each step of the disaster prevention and mitigation support method described in any one of claims 7 to 11.