Systems and methods for monitoring environmental events

Abstract

One or more environmental events on Earth are monitored by a method that includes receiving a notification of the occurrence of the environmental event, the notification being obtained from first environmental data. The region on Earth corresponding to the notification is identified. The event is monitored by collecting additional environmental data in response to a determination that the event meets one or more predetermined event criteria. Additional environmental data determined to be related to the event according to one or more relevance criteria is tagged to the event in a geographically indexed database and used to estimate the severity of the event at locations within the identified region.

Description

Technical Field

[0001] The present invention relates to the monitoring of environmental events.

Background Art

[0002] Environmental events include adverse conditions such as floods, firestorms, sandstorms, hurricanes, tornadoes, volcanic eruptions, earthquakes, tsunamis, and storms. These environmental events can cause significant loss of life or property damage. In such large-scale events of this type, it can be particularly difficult to grasp the scope and severity of damage caused by environmental events, especially during events where the situation is very dynamic and immediately after events where there is a possibility that infrastructure and communication systems have been damaged. The present invention is not limited to harmful events and can also be used for monitoring other types of environmental events.

[0003] There is a need for a solution that enables more rapid and accurate response and monitoring of environmental events. US Patent No. 10346446B2 discloses a system and method for aggregating multiple source data and identifying a geographic area for data acquisition. Here, asynchronous data packets, such as those obtained from social media posts, weather conditions at a weather location, news wire articles, and maps of OpenStreetMap (OSM), are identified as being worthy of consideration and are correlated with "first change" information that can be obtained using satellite imagery. Next, the results of the correlation are used to perform resource allocation, for example, predicting the geographical progression of a correlated event.

[0004] The present invention is not limited to solutions to any of the problems described herein and can also solve other problems.

Summary of the Invention

[0005] This summary is provided to introduce, in a simplified form, a selection of concepts that are further described in the following detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to determine the scope of the claimed subject matter.

[0006] Some of the systems and methods described below relate to, for example, estimating the local severity of environmental events in near real-time.

[0007] In some of the systems and methods described below, a notification of an environmental event derived from first environmental data is received. The region on the Earth corresponding to the notification is identified. It is determined that the event meets one or more predetermined event criteria, and in response to that determination, additional environmental data is collected and the event is monitored. Additional environmental data determined to be related to the event according to one or more relevance criteria is tagged to the event in a geographically indexed database and used to estimate the severity of the event at locations within the identified region. Embodiments of the present invention will be described below by way of example only with reference to the following drawings.

Brief Description of the Drawings

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DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, embodiments of the present invention will be described as an example only. These examples represent the best methods known to the applicant for practicing the invention, but are not the only ways to achieve this.

[0010] FIG. 1 is a flowchart showing an environmental event monitoring method applicable to various types of events. With reference to the subsequent figures, some specific examples of this method will be further described.

[0011] Using the systems and methods described in this specification, as is known to those skilled in the art, any type of environmental event can be monitored. Examples of events include, but are not limited to, earthquakes, forest fires, floods, and tornadoes. These are events that may be referred to as major disasters. Other environmental events that can be monitored according to the methods described in this specification include, for example, large changes in environmental data such as a significant increase or decrease in the water depth of a river or a significant increase or decrease in temperature, all of which can be precursors to catastrophic events.

[0012] For example, an environmental event monitoring system for implementing the method shown in FIG. 1 can include a computing system, and typically, it will be understood to include a distributed computing system that uses computing power at different locations. Some implementations can achieve effects by using the cloud-based system described later.

[0013] In FIG. 1, selectable operations or actions are shown within the dotted frame. Thus, the first operation in this method can be to receive a notification of an event and, at operation 105, determine whether the event meets a predetermined event criterion described later. The notification can be received, for example, from a remote source operated by a third party. The notification can identify the geographical location of the event.

[0014] In addition to, or as an alternative to, using notifications from third parties, this method can include monitoring the earth's environment at operation 101 by collecting first environmental data. The first environmental data is obtained from third-party sources. Additionally or alternatively, the systems described herein can include sensors and other devices for collecting the first environmental data.

[0015] At operation 102, an environmental event can be identified from the first environmental data and a notification of the event can be generated.

[0016] At operation 103, the area on the earth corresponding to the notification can be identified. The notification, whether from a third-party source or from within the systems described herein, may include information related to the area on the earth corresponding to the notification, and this can be used to identify that area at operation 103. Additionally or alternatively, it may be necessary to obtain additional data, such as supporting information, to identify the area at operation 103. The area identified at operation 103 may be referred to as the area of interest "AOI".

[0017] Identifying the area in operation 103 may include identifying the area that may be affected by the event. For example, the notification may identify a specific geographical location. Therefore, optionally, depending on the nature of the event, it may be preferable to obtain data related to an area larger than that location. As the monitoring process described in this specification progresses, it may be effective to redefine the area as appropriate.

[0018] Therefore, the determination in operation 105 is performed by a computing module such as a decision engine further described with reference to FIG. 2 and can receive input from a remote source and / or a source that is part of the system.

[0019] The monitoring performed in operation 101 may represent the normal or background operating mode when monitoring the entire environment for any specific environmental event.

[0020] The term "environmental data" is intended to be interpreted broadly and may include meteorological data such as temperature, humidity, rainfall, wind speed, and other data obtained from sensors and / or measuring instruments. For example, it may include images and radar data from one or more satellites, or other platforms on Earth such as airplanes and other aerial platforms. Environmental data may include data from sources other than sensors, such as text, images, and records of other forms of social media. From this, it can be understood that in some ways, one or more pre-filtering operations can be performed on the received communication to determine whether sufficient information is included for the determination in operation 105. Additionally or alternatively, similar filtering may be applied in operation 102 where the event is identified and notified.

[0021] The above is an example of data that is relatively frequently generated and / or monitored, for example, at least daily.

[0022] Environmental data may further include data that is less frequently generated and may not be monitored, but is useful as part of the methods described herein. Such data includes, but is not limited to, map data such as open source maps, Google Maps, maps or geographical information from other sources, for example, structural information related to buildings, roads, and other artificial structures, modeling information such as flood prediction models, soil conductivity, crop or other plant cover rates, elevation from the nearest drainage channel, and other recorded information. This type of data can be effective in identifying the area corresponding to the notification, i.e., the area corresponding to the event. Some of this data is referred to as "past" or non-real-time data.

[0023] The following is an example of specific event criteria applicable in operation 105. Others can be conceived by those skilled in the art and applied in any of the methods described herein.

[0024] The predetermined event criteria may include geographical criteria. Thus, the methods described herein may be limited to one or more geographical areas.

[0025] The predetermined event criteria may include a global severity threshold. This is different from the local severity of the event described with reference to operation 117. The global severity may be determined in different ways depending on the nature of the event. Examples of global severity include, but are not limited to, geographical extent, e.g., region, and the number of people affected or likely to be affected.

[0026] In some methods, the received notification can be processed on different channels depending on, for example, the nature of the event being notified, the form of notification reception, or other categories.

[0027] If, in operation 105, the event does not meet one or more predetermined criteria, the monitoring process in operation 101 may continue. If the event meets one or more predetermined criteria, the process proceeds to operation 107, where additional environmental data is added. The purpose of operation 107 is to collect additional data related to the event. The event may be "activated" in response to meeting one or more predetermined criteria, and a second operation mode may be initiated.

[0028] Note that in this specification, the collection of the first environmental data in operation 101 may continue after the activation of the first event, i.e., after the first event has satisfied operation 105. Thus, multiple events may be activated in parallel. The following operations in FIG. 1 are described in relation to a single event for simplicity.

[0029] The collection of additional data may be different from collecting the first data in one or more ways. In other words, the collection of the first data may be performed according to the first data collection criteria, and the collection of additional data may be performed according to the second data collection criteria. Although many examples are described in this specification, other examples may be envisioned by those skilled in the art.

[0030] The collection of additional data may include collecting the same data as in operation 101 at a higher frequency in order to obtain more data and monitor the progress of the event more closely. The collection of additional data may also include collecting a high-resolution version or high-quality version of the same data.

[0031] The collection of additional data may include collecting data from additional sources that do not provide the first data.

[0032] Broad criteria may be defined for the collection of additional data, and these may vary depending on the source.

[0033] Regardless of whether the additional data includes the same data or data from sources not included in the first data, as a result of collecting the additional data, data related to the event is collected more frequently than it was occurring before operation 105.

[0034] The criteria for collecting additional data may include, for example, one or both of one or more keywords and geographical criteria. Thus, the additional data may include data whose location is specified within a predetermined range of a specified area or within the range of the location of an event. Also, for example, it can be collected based on a search by keywords. In the case of a flood, the data may include news articles and other social media items found by keyword search (e.g., "flood" and "location"). The additional data may include data from sources outside the specified area (e.g., weather forecasts from the area where a weather system is occurring).

[0035] The additional data may not include relevant location information, in which case it is located at operation 109 or "tagged" to a relevant location. Examples of ways to do this are described below.

[0036] In operation 111, by verifying the additional data, it is determined whether it is relevant to the specified event according to one or more relevance criteria. The relevance criteria may include whether the data is related to a location within a predetermined range of the specified area, since the data may include data whose location has not already been specified in the collection criteria. Other relevance criteria may include the geographical location information of the located data, the presence of keywords, etc. The relevance criteria may depend on the nature of the additional data and / or the nature of the event. For example, in the case of image data, the criterion is whether the event appears in the image, which can be determined using, for example, image processing techniques. As a specific example, a traffic surveillance camera has accurate geographical location information, but may be too high to confirm the inundation at the scene. However, it can capture useful information related to a wildfire.

[0037] The general principle of applying the relevance determination in operation 111 is to enable the additional data filtered in operation 111 to be widely collected in operation 107. This may be implemented in a decision engine further described in relation to Figure 2.

[0038] Data that meets the relevance criteria is tagged to events in the geographically indexed database in operation 117. Data that does not meet the relevance criteria is either discarded or not tagged and retained in the database for future use in operation 113.

[0039] In operation 117, the tagged data in the database related to the event is used to estimate the severity of the event at locations within the specified area. By determining the severity, more information related to the event can be obtained compared to simply determining the extent of the event. This is because when determining the extent of the event, it can only indicate whether a building or area has been affected by the event. In particular, the severity can indicate not only whether a location has been affected but also the degree of damage.

[0040] As described elsewhere in this specification, in the case of floods, the severity can be determined, optionally with high frequency, for example, for individual buildings, by the estimated flood depth at a specific location. For inundation and other types of environmental events, the overall extent or local severity within the specified area can be estimated in various other ways. For example, lower-resolution severity indicators can include the number and proportion of remaining buildings, the number and proportion of collapsed buildings, or the number and proportion of damages by other methods. In the case of natural features, severity indicators can include, for example, the proportion of land or crops washed away, which can also be applied to floods, landslides, snowstorms, or other events.

[0041] In operation 117, the severity is determined for locations within the area specified in operation 103. The location can be a landmark, building, or other feature within the specified area. Additionally or alternatively, for the monitoring of some environmental events, the area identified in operation 103 may be subdivided in operation 117 to determine severity. Thus, the location may include areas within the identified area. Geometric patterns such as square or hexagonal grids may be used for subdivision. Alternatively, geographical features may be used to more simply subdivide the area, for example, using a river to separate one area from adjacent areas. It should be understood that the type of subdivision may vary depending on the event being monitored. To fully monitor the identified area, the sub-areas, i.e., the areas within the identified area, may be contiguous.

[0042] From the above, other possible indicators of severity include, particularly when suitable for wildfires and other events causing fires, the percentage or extent of the burned area, the degree of damage to buildings, infrastructure, vegetation, and other features (e.g., untouched, partially burned, burned to the ground), in the case of volcanic activity, the percentage of lava occupied and / or the depth of lava occupied, the percentage of existing buildings, in the case of earthquakes, the percentage of damaged buildings and other infrastructure, and the degree of damage (no change, slight displacement in the building, total destruction, etc.), but are not limited to these.

[0043] Figure 1 shows a method assuming two modes of environmental data collection. In operation 101, first data is collected in the "normal" or background mode, and additional data is collected in the "activation event" mode in response to the activation of an event. For example, additional collection modes may also be possible, such as those that are executed before an event is "activated". For example, the additional mode may be effective in determining the location of an event related to a notification received in operation 105. For example, if a social media report regarding a flood is received without indicating the location, an intermediate mode for "listening" to corroborating information that can be used to identify the location of the event may be implemented. These operation modes are not mutually exclusive and may be implemented in parallel, for example.

[0044] All monitoring described in this specification is performed in real time in proportion to the progress of the event. However, the information obtained through real-time monitoring may be augmented by historical data, which is also referred to in this specification as "non-real-time data".

[0045] By monitoring and determining the severity of events at locations within a wider area, the methods and systems described in this specification can be used to obtain an immediate assessment of the damage suffered during an environmental event, so that resources, such as emergency support or subsequent repair work, can be accurately delivered to a specified area, such as the area affected by the event.

[0046] Some systems and methods combine synthetic aperture radar "SAR" obtained from space or airborne platforms with geographical location data from one or more sources on Earth. Thus, for example, a notification can be generated from the SAR data at operation 102. For example, in the case of a flood or a forest fire, the SAR data enables the determination of the geographical extent of the environmental event. The area identified at operation 103 may be made larger than the extent identifiable from the SAR data by including areas that may be affected by the event.

[0047] The areas identified at operation 103 may be contiguous, but are not necessarily so. In other words, two or more areas on Earth may be identified at operation 103. For example, in the case of a flood as the environmental event, the areas identified at operation 103 may be interrupted by areas of high ground that are not likely to be affected by the flood.

[0048] Figure 2 is a schematic diagram showing a possible structure of an environmental event monitoring system. This is an example of a structure that enables and can implement operations 107 to 117, which may be continuously repeated during the process of an environmental event such as a natural disaster in the operation of Figure 1.

[0049] The system of FIG. 2 includes a web application map-based front end 201, which can be implemented on any computer or computing system configured as a server, for example, as shown by box 202. This can include a geographically indexed database 220, described with reference to FIG. 1, which includes tagged data related to notified events. The front-end server 202 can perform many functions, including an implementation of a decision engine that makes decisions in operations 105 and 111, as referred to in connection with FIG. 1. The decision engine implemented on server 202 can receive inputs from a back-end server (which may be a third-party server) shown by box 204, and a ground-based server 206 that can form part of the system as described herein.

[0050] Generally, inputs to the front end from weather services, ground sensors, and other sources can help in predicting environmental events and identifying the areas affected. Geospatial algorithms can be used to integrate, for example, geospatial data into a common format that can be stored in a geospatially indexed database. Machine learning services can be used to process the data to identify features of interest.

[0051] Server 202 is shown connected to a further series of servers shown as hexagons, although these other servers may also be implemented as servers on a computer or computing system. For the purposes of environmental event monitoring described herein, these further servers are back-end servers. These back-end servers can take the form of micro-servers known in the art. The back-end servers can provide environmental data from ground sources such as rainfall measurements, temperature measurements, wind speed, etc. Additionally or alternatively, the back-end servers can provide information from aerial and space platforms.

[0052] FIG. 2 shows, by way of example, several back-end servers. The first group of back-end servers is shown in box 204 and can communicate with the front-end server 202 directly or via one or more other servers. The weather service 204a can be of any known type that uses not only ground data but also data obtained from air or space platforms. The machine learning server 204b can take in environmental data from various other servers and operate as is known in the art to interpret, for example, what the data represents. This can include interpreting image data to identify water or fire, or interpreting temperature, rainfall, and other data from multiple sources to predict events, all based on learning from past interpretations.

[0053] The geospatial algorithm server 204c can be used to perform a number of functions, including identifying the location of additional data using geographical location information in operation 109. Thus, in some implementations of the methods and systems described herein, a third-party server can perform location identification using the geographical location information of the data. It will be apparent that the servers 204 and 206 described below can communicate with each other and may also communicate with the front-end server 202.

[0054] The ground sensor server 204d can provide various ground sensor data including, but not limited to, rainfall, soil moisture, temperature, and others.

[0055] The real-time distribution server 204e can provide data from any number of sources including, but not limited to, news feeds. Further, the real-time distribution server can be used to provide any type of environmental data described herein. For example, any of the other servers mentioned herein may also provide data such as weather data on an offline basis, and for example, a server can be queried to find out what the weather was like at a particular location and date, but this weather data does not necessarily have to be real-time.

[0056] Therefore, although the real-time distribution data from the real-time distribution server may display, for example, the current flow rate value of the water level gauge or the webcam image, it is not actually stored for a long time. Therefore, in the methods and systems described herein, real-time data can be periodically collected and stored in the database 220. An algorithm may be used for the collection. For example, a program (or "server") that collects water level gauge information may be specialized for collecting that type of data. Generally, in the methods and systems described herein, data can be acquired and / or translated in different types and / or formats and stored in the database 220 in a common format.

[0057] The SNS (Social Media Service) data server 204f can provide data from any number of sources, including but not limited to news feeds, social media posts, online images, chat groups, etc. The server 204f can perform functions related to SNS, including crawling, acquiring, processing data from subscription sources, and other functions known in the art, to acquire potentially relevant data.

[0058] One or more of the backend servers 204a-204f can be a source of the first data, as described with reference to FIG. 1. One or more of the backend servers 204a-204f can be a source of additional data, as described with reference to FIG. 1.

[0059] It will be appreciated that some of the functions of the servers 204 and 206 may be provided in other ways. For example, the servers are described as providing services that do not necessarily require a server. These servers can be functions, apps, macros, or programs provided by a server or other means.

[0060] An important aspect of some of the methods and systems described herein is that services such as AI algorithms can interact with automatically collected data, for example, by using a server. Further, a server such as server 202 can generate insights into environmental events and, ultimately, provide a user interface for analysts and other people to view, interact with, and interpret the data as needed to determine the scope and local severity of the event.

[0061] FIG. 2 further includes a satellite 210, which is one of the constellations that downlink images or image data, and can provide its content via one or more ground servers that are also back-end servers for monitoring environmental events, as described herein. The back-end servers that may be associated with the satellite constellation or aerial platform may include an image archive 206a and a geographic information system "GIS" map server 206. The images are processed by various exchangeable functions and generate and provide analysis-ready data (ARD) in server 206c, and may utilize auxiliary data from the data lake based on the needs of the solution. The data lake includes, but is not limited to, digital elevation maps, structural details, and weather information, and is provided from server 206d.

[0062] The database 208 (or a series of databases) can hold customer-specific data or event-specific data generated either a priori or as part of an analysis. The database 208 may take the form of a relational database in PostgreSQL or other suitable format, enabling the visualization of geospatial maps.

[0063] In a specific example of how various data sources shown in FIG. 2 are utilized, as an example of the first data collected before the activation of an event, worldwide water level gauge data that may be periodically imported into the system before activation can be cited. Typically, it continues to be collected even after activation and monitors events other than the activated event, for example, and this data helps to create a baseline and determine the timing of activation. The specific data collected before activation is Prediction data that is useful for forming the baseline of environmental events, while the data collected after activation is collected more frequently, tracks the progress of events, and is used for creating and updating flood models. Some data sources may be the same before and after activation (for example, water level gauges, weather data, and satellite data are also possible), but are collected more frequently before activation. The collection criteria for the first data and additional data may depend on the effectiveness of the data in predicting the occurrence of an event or sending reliable alerts. Therefore, other data sources (such as social media data) may require more analysis and may not be efficient to collect before the event is activated and the area of interest is identified.

[0064] FIG. 3 is a schematic diagram of a process that can be implemented in the front-end server 202 to enable the interoperable automatic import and use of data from the back-end server. The back-end server is represented by the hexagon 304 and may include, for example, any of the servers described with reference to FIG. 2 or any other back-end server. For example, a cloud-based web platform implemented in the front-end server 202 can perform operations on the data received from the back-end server and achieve the severity estimation described with reference to FIG. 1. Therefore, the operations performed on the received data can include any of the analysis for determining the local severity of the event, quality control for determining the reliability of the analysis, finalization and archiving in the database 220, and export of the severity estimation value to end users, etc. These operations can be executed automatically. Alternatively, some of these operations may be executed manually.

[0065] Based on the archived information, events may be re-considered based on the need for new data collection or re-analysis. This is continued to manage the versions of the content required for the analysis of these time-dependent events.

[0066] The systems and methods described herein may provide important information related to environmental events to various end-users. An example of an end-user of the systems and methods described herein is an operator of an emergency service who can use the system to determine where resources should be allocated. The end-user can use the front-end interface to obtain information related to a specific location within the scope of the event, or the area specified in operation 102.

[0067] As described above, the systems and methods described herein can be used to monitor various types of environmental events. A specific example of a flood will be described below.

[0068] Figure 4 is a schematic diagram showing a flood monitoring method. This exemplary method begins with collecting first environmental data, which in this example is SAR satellite image data indicated at 401. The data can be verified either on the satellite or on the ground and can identify environmental events. Generally, satellites can be used to monitor various environmental events. Here, assume that a flood has been identified in the satellite image data.

[0069] SAR is an active technology that transmits radar signals and receives their echoes to form images. This is in comparison to passive optical satellite technologies that rely on existing light (such as light from a camera). SAR has the advantage that it can take images both during the day and at night, and also in clouds and bad weather that are opaque to optical satellites. Optical satellites cannot take images at night and are often unable to image flooded areas during the most critical time periods due to the weather systems causing the floods. However, by using SAR images for flood monitoring, it becomes possible to monitor flooded areas more frequently.

[0070] It is also possible to perform an operation 102 to identify a region on the Earth from the SAR image data 401. Otherwise, the region may be identified using additional information such as a digital elevation model indicating lowlands prone to flooding.

[0071] In FIG. 4, assume that the identified event is determined to meet one or more criteria described in relation to operation 105. Next, for example, as described with reference to operation 107, by collecting additional data 403, the event is monitored in operation 107. This may include SAR satellite images, optical satellite images 403a, aerial images 403b, open source images 403c such as those obtainable from social media, and water level / tide gauge information 403d shown in FIG. 4 as points on a map.

[0072] The SAR image can be enhanced using the optical image 403a and the aerial image 403b, and the open source image 403c can be used together with data from other sensors such as the water level / tide gauge information 403d to enhance data related to floods.

[0073] Some of this additional data needs to identify a geographical location, as shown in operation 109, and its relevance can be determined in operation 111. The relevant additional data 403 is tagged as shown in operation 115.

[0074] The tagged data 403 may be used to estimate the severity of an event at a location within the identified region corresponding to the event or notification.

[0075] For determining the severity, as described above, non-real-time geographically indexed data can be used. Thus, although not shown, the selectable operations in FIG. 1 are to obtain non-real-time data and use this in operation 117. Non-real-time data may not be stored in database 220 and may not be tagged. Examples of non-real-time data include, but are not limited to, geographically indexed data such as watershed data 405a and digital elevation model (DEM) 405b. Next, these can be combined with flood data to create a model 407 of the extent and depth of the flood. In this example, the depth represents the severity, and from the model over the flood extent, the severity at a location within the specified area, or within the flood extent (which may be an area within the specified area) is estimated.

[0076] Note that a watershed is an area where all water flows to a common location. For example, the watershed of a river is the entire land where the rainfall on that land ultimately flows into a specific river. Thus, in some implementations of the methods described herein, the specified area may include a watershed. Usually, a DEM is simply a numerical elevation model of the land and may be used to determine the boundaries of a watershed, but may not contain sufficient information about the watershed of interest. Watershed information is particularly useful, for example, when determining whether rainfall data is related to flooding of a specific river. If the rainfall does not "fall" within the watershed of that river, it will not cause flooding and does not need to be tagged. Next, model 407 can be used to evaluate whether environmental events affect both artificial infrastructure such as buildings and natural features such as rivers and forests. By frequently combining data from multiple sources with satellite imagery, it becomes possible to monitor floods almost in real time.

[0077] The model can be used to predict the progress of the event, and the additional environmental information collected in operation 107 can be used to update the model in real time in proportion to the progress of the event.

[0078] The output of any of the methods described herein can be actionable data, such as data for checking the depth of flooding in a particular building. By using the determination of the severity of events in different locations to prioritize some areas over others, resources can be appropriately allocated to respond to the events. Further, this type of information can be valuable for victim relief and post-event reconstruction of environmental events for, for example, all levels of government, research and relief agencies, insurance companies, and the like.

[0079] The methods described herein can very quickly determine the severity of an event at a location within a specified area from data related to another location. For example, as will be described later, by using information regarding the depth of inundation in a particular building, such as can be obtained from a photograph posted on social media, together with a DEM, it becomes possible to determine the depth of inundation in other buildings. From a single social media photograph showing shallow inundation in one building, it can be determined that another building in a low-lying area is likely to suffer more severe damage from deeper inundation, thereby allowing resources to be allocated to buildings with more severe flood damage more quickly than currently.

[0080] In the example of FIG. 4, SAR data 401, and other geographic location data 403 including data from sources on the Earth, are combined with historical data, for example, non-real-time data related to the terrain of the specified area, to estimate the geographic extent and severity of an event at locations within the geographic scope. Since this is done almost in real-time, the extent and severity of the event can be monitored in real-time. As a result of determining not only the extent of the event but also the local severity, it is possible to determine not only whether a location has been affected by the event, but also the severity of that impact.

[0081] Determination of the severity of an event can include, for example, determining the height of damage caused by the event in relation to one or more structures on the Earth. This can be determined from images and / or sensor data included within the geographic location data.

[0082] Determining the scope and severity of an event may include comparing information obtained from additional data with non-real-time data. For example, additional data obtained in real time can be used to determine the current situation of an event, such as indicating an area where a tree has fallen, a flooded area, or other natural features, and this can be compared with past records to determine the severity of the damage caused by the event.

[0083] In the example of FIG. 4, first, an event such as a flood can be identified from SAR image data and “activated”. In any of the methods described herein, before and after activation at operation 107, additional environmental data, for example, the location and its attributes of a social networking service “SNS” where the geographical location is specified, can be used to confirm that the event has occurred. These can include, for example, an image of the event, or an image of the area or structure affected by the event.

[0084] Taking the example of a flood described further with reference to FIG. 9, the water depth can be estimated from SNS images along with the corresponding confidence levels. The water depth can be estimated by combining the street view when there is no flooding with the corresponding measurements during flooding.

[0085] Known tools can be used for data aggregation. For example, a suitable application such as Floodtags can be used to aggregate flood-related tweets based on geography. This can be combined with historical data such as the above-mentioned DEM or digital terrain map “DTM” to generate a flood depth map. Data aggregation may include ranking by the importance of SNS sources and other geographical location data sources.

[0086] The terms DEM and DTM are used interchangeably in the art. Here, DEM is used to refer to a map containing elevation information of the mapped terrain, e.g., altitude above sea level. The DEM can include additional information. The DTM may be a "bare-earth forced" model. In other words, it may ignore surface structures but can include elevation information. Further, the DTM may include information not included in the DEM, e.g., it may consider the direction of water flow. The methods described herein can use DEM or DTM information as appropriate.

[0087] Figure 5 is an image showing the extent of flooding affecting two small towns in the state of Washington, USA. In addition, this image provides an indicator of the severity of flooding for buildings within the flooded area, where the severity is determined by a method as described herein. This figure is a composite image combining SAR imagery and map information of an area affected by a major environmental event of widespread flooding due to a series of severe storms that occurred in November 2021. Images of the type shown in Figure 5 can be formed using historical data on the land coverage rate by buildings. For example, building footprints can be obtained from the Federal Emergency Management Agency (FEMA) of the United States or from Microsoft Canadian Building Footprints. Sources of building footprint data and other layers can also be used. In the image, river 501 is shown along with flooded areas such as 503, 505, and 507. Two urban areas are shown at 508 and 509. Monitoring of this environmental event started on November 11 日 and the first satellite image acquisition was carried out on November 13 when the flood was approaching its peak. 日 The final image acquisition was carried out on November 18 when the flood was receding, 日 and the final analysis was completed on November 19. 日 This shows an example of the speed at which such types of environmental events can occur and the speed at which the monitoring system needs to respond and collect relevant data accordingly.

[0088] The system can provide information regarding flood extent and impact on infrastructure. In one example, water levels are determined by a combination of processes described in more detail below with reference to FIG. 12, based on flood models developed using various data sources as described with reference to FIG. 2. In one example, social media information and news feeds can be used to calibrate and further improve flood models. For the environmental events causing inundation in FIGS. 5 and 6, a total of 164 SNS points were collected, and 96 of them were used for analysis. In one example, the disclosed environmental monitoring system can provide high-precision models of flood extent and water levels that were not obtainable by prior art methods. By knowing the exact water level, flood depth can be determined using pre-flood data (e.g., images or other data) that provides the digital terrain model and / or the exact height of the underlying terrain. The flood depth at a given point within the flood extent can then be calculated for all points within the flood extent by subtracting the height of the terrain from the height of the water.

[0089] From FIG. 5, it can be seen that some areas within the urban environment, such as 511 and 513, are inundated. Using the accurate inundation depth information determined above, the inundation depth of each building within the inundated area can be determined based on the height of the water at the location of the building. The severity of inundation for each building can be determined, and in this image, inundated buildings are represented by shading, with the darkness of the shading indicating the depth to which the building is inundated. In this image, a total of 2,998 buildings are inundated, of which 1,643 are inundated at relatively shallow water depths, 1,116 are inundated at moderate water depths, and 239 are inundated at deep water depths.

[0090] Figure 6 is an image of a flood in another urban area in British Columbia, Canada. The two areas shown in Figures 5 and 6 are hundreds of kilometers apart and are separated by a national border, yet they were caused by the same widespread environmental event. The environmental event consisted of a series of intense storms that brought a very large amount of precipitation to the area. In this embodiment, data indicating a large amount of rainfall and images indicating flooding generate a notification, which corresponds to step 102 in Figure 1. Areas of interest within the scope of the environmental event in both Washington State and British Columbia were identified according to step 103 using watershed information and administrative boundaries. Since there were many urban areas affected by the environmental event (step 105), monitoring of the environmental event was started and collection of additional environmental data (step 107) was started.

[0091] In Figure 6, the flood extent (area) and the buildings affected (by the flood) are identified. Similar to Figure 5, the severity of flooding of individual buildings is shown. The most affected buildings are shown in dark shade, and the less affected buildings are shown in light shade. For example, three buildings in 601 are shaded darker and are flooded with a higher severity (greater flood depth), while the building in 602 is flooded with a medium severity, and the building in 603 is flooded with a relatively low severity. It should be noted that this model has the ability to distinguish the difference between building 604 flooded with a high severity and its adjacent building 605 flooded with a relatively low severity with a very fine granularity. In this image, a total of 973 buildings are flooded, of which 539 are flooded with a relatively shallow water depth, 377 are flooded with a medium water depth, and 57 are flooded with a deep water depth. Buildings not affected by any flooding are not shown in this image. Due to the unprecedented ability of this monitoring system to accurately determine the severity of flooding with sub-building-level accuracy, the results are very useful for a number of purposes. For example, it can be used for search and rescue operations, the direction of recovery resources, damage estimation by the government, homeowners, insurance companies, financial service companies, banks, etc.

[0092] Data from various regions can be combined to provide an overall estimate of the severity due to a specific environmental event. For example, the environmental event that caused the flooding shown in FIGS. 5 and 6 was determined to have caused a flood that reached a level of 5,000 km 2 weak, causing a flood. On both sides of the Canada / USA border, a total of 10,012 buildings were affected, and the average flood height at the building level was 0.6 m.

[0093] FIG. 7 is a schematic diagram showing how additional data can be processed. More specifically, the flow in FIG. 7 can be used, for example, to create models of the extent and severity of an event, to process additional data collected in response to the activation of an event for the purposes of estimation in operation 117. Further, the same flow can be used to collect additional data and, as the event progresses, to reinforce and calibrate the model using the additional data, or to improve the model in other ways.

[0094] Accordingly, external information is processed into a database, geographically indexed if necessary, analyzed, and added to the event database in order to create a model or to reinforce and calibrate a model in a system or method.

[0095] FIG. 7 shows an example of a system used for flood monitoring. First, for example, a notification 703 (operation 101 in FIG. 1) derived from the first environmental data is received and brought into the work management system 705. In one example, a known work management system Jira, which is a commercially available system originally used for tracking bugs and issues, can be used. The work management system 705 may be used to determine whether an event meets a predetermined criterion (operation 105). When an environmental event is identified and activated, a process 707 is started and additional environmental data is collected (operation 107). The process 707 may include tasking a satellite to acquire high-resolution images at frequent intervals, initializing a web-based system for inputting, analyzing, and browsing a geospatially indexed database, and initializing an SNS (Social Networking Service) deck for this event to collect social media and other online information.

[0096] Browsing of the geospatially indexed database can be performed using a known GIS system such as ArcGIS. Additionally, this can be done online from anywhere using the ArcGIS WebApp. Other data used includes data from Google Maps, news articles, and the analyzed information is put into the measurement deck. An external service 711 provided by a team of analysts or artificial intelligence "Al" can analyze and process the data according to the flow indicated by box 701. It is shown that a series of flows of taking in data from various sources such as the servers described in this specification, automatically or manually analyzing that data, and updating the database 715 and the model 407 are constantly performed. The flow shown in 701 may include some or all of the operations 107 - 117 in FIG. 1 and some or all of the stages indicated by box 301 in FIG. 3.

[0097] The geographically indexed database 715 is updated using an API (Application Programming Interface) 713. An example of a commercially available API is FastAPI, a modern high-speed (high-performance) web framework for building APIs using Python. The results can be visualized with a GIS visualization program 717 such as QGIS to provide feedback 719 to the system 701. Quality control, either automatic or manual or a combination of both, can be part of the feedback 719.

[0098] The workflow can continue almost in real time and provide information with a high degree of temporal and special accuracy and resolution.

[0099] As already mentioned, some of the methods described herein can be used to model events and / or predict their progression almost in real time. This is particularly useful for transient events such as floods.

[0100] Figure 8 shows, for an environmental event (in this case a flood) in Port Macquarie, Australia, monitored using the methods and systems described herein, the time series of precipitation from March 15, 2021 to March 22, 2021. 日 An example of the time series of precipitation is shown.

[0101] Using meteorological data, 24-hour integrated precipitation data (mm) is plotted and represented by line 805. The 3-hour integrated precipitation data (mm) is represented by line 807. Histogram 813 represents precipitation data in mm / hour units. After the heavy rain shown in the histogram in the first half of March 19, the first peak 809 of line 805 occurs with an accumulation of more than 400 mm of precipitation in the past 24 hours. This provides a good indicator of where peak flooding can occur on the timeline. The second, smaller peak 811 occurs on March 21 with a water accumulation of 150 mm in the previous 24 hours. 日 After the heavy rain shown in the histogram in the first half of March 19, the first peak 809 of line 805 occurs with an accumulation of more than 400 mm of precipitation in the past 24 hours. This provides a good indicator of where peak flooding can occur on the timeline. The second, smaller peak 811 occurs on March 21 with a water accumulation of 150 mm in the previous 24 hours. 日The points along the x-axis indicate times at which SAR satellite images of an area during an environmental event are taken. Six points are shown. As can be seen, the point indicated by 814 is close to the first peak 809, and a SAR satellite image taken at that time could likely capture the flood near its peak level. FIG. 8 illustrates the advantage of being able to collect additional data, such as image data, at a relatively high frequency during the course of an environmental event, such as a flood.

[0102] Traditionally, large SAR satellites are single assets or part of a small constellation of, say, three or fewer, and are often only able to revisit a particular location on Earth to capture additional imagery once every few weeks.

[0103] A constellation of small SAR satellites allows for much more frequent repetitions. In this example, points are acquired with a constellation of five or more small SAR satellites. From the six points shown, it is clear that the rainfall was only observed on May 17, before the rainfall began to increase in earnest. 日 It can be seen that the first SAR images of the area were taken on May 17th, 2013, providing a baseline data point before the flooding occurred. 日 Since then, one image has been taken approximately every 24 hours, with the latest image recorded on March 19th. 日 Additional images were taken around midnight on 13th March 2013, which would have provided good data around the peak of the flood. In this example, images are taken at least once per day. In alternative examples, with a sufficiently large constellation of satellites, images could be taken even more frequently; for example, once every 12 hours, once every 6 hours, or once every 3 hours or less. The more frequent the repetition, the better the temporal resolution. Larger constellations of 10 or more, 20 or more, or 50 or more satellites would enable these high revisit rates and could provide unprecedented and previously impossible temporal resolution for Earth monitoring data.

[0104] Data such as social networking service (SNS) points can be used to calibrate the model and improve the dataset. In the example of flood monitoring, SNS points can be used to confirm not only the presence of floods at precise locations but also the severity of inundation. Figures 9a and 9b show examples of two flood scenes that can be used to assist in calibrating the flood model in this area. First of all, from these photos, it is clear that environmental events such as floods are occurring at these locations in this case, and this information itself can be said to be useful in the geospatially indexed database used for monitoring floods. However, since SNS data is often not geographically specific, an important step is to geographically identify the image. This can be done using clues such as the store sign 901 in Figure 9a and the flooded road sign 902 in Figure 9b. Then, using clues like these, it is possible to search for the location in an appropriate reference source such as on Google Maps. By being able to geographically locate SNS data, the possibility of using a wider range of SNS images is opened up, and more data about the event being monitored can be provided compared to using only SNS data that already has location information attached.

[0105] Photos such as those shown in Figure 9 can be the source of a notification that a flood has occurred. Additional confirmation information may be collected before the event is triggered and operation 107 is started. Information from the photos (e.g., information collected from social media sources) can form part of the additional information used to determine the severity of the event at each location within the area of the event in operation 117.

[0106] As described above, the estimation of the severity of an event may include creating a model of the scope and severity of the event by combining non-real-time data with additional environmental data collected in real time. Taking the example of a flood, the real-time data may include photographs such as those shown in FIG. 9. FIG. 9b shows a street sign flooded up to near the sign itself and a garage door flooded up to nearly the top of its height. From this information, the depth of the water at that location can be estimated. This information can be combined with non-real-time data such as a DTM to determine the depth of the flood at other locations. Buildings in low-lying areas are more likely to be severely flooded and require priority attention by emergency services, such as evacuating flooded occupants. From this example, it can be seen that an initial model can be created using limited real-time information.

[0107] The following table is an example of information obtained by monitoring floods by the method according to FIG. 1. Impact Table: Prioritization Based on Buildings Affected and Observed Depths JPEG2025520145000002.jpg119153

[0108] The collection of additional environmental information in operation 107 continues after the initial creation of the model. In the example of FIG. 9, the flood model can be checked using geographical location data such as that obtained from photographs to ensure that the flood depth is accurately estimated. Similar to the initial creation of the model, the depth of the water can be estimated based on the image and assigned to that location. This enables further calibration of the flood model.

[0109] As can be understood from the foregoing description, the determination of the severity of an event may include determining the height of the damage caused by the event in relation to one or more structures on the earth. In the case of other types of events, the height of the damage may be estimated from other indicators that may depend on the height of the fire, blackening, and the nature of the event being monitored.

[0110] In the foregoing description, the main example of non-real-time data (i.e., data acquired before an event occurs) is DEM, but for flooding and other types of environmental events, other types of non-real-time data can also be used.

[0111] Taking the example of a forest fire, an event can be modeled using non-real-time information from before the event, such as records of the coverage of trees and buildings. A single photograph of a fire at a specific location can be combined with other environmental data such as wind speed to model the event, its severity at a specific location, and its possible progression. The model can be used as a first indication of where resources should be directed, and this can be updated using additional environmental data as the event progresses.

[0112] FIG. 10a shows an example of a photograph of a building within a flood area that can be obtained, for example, from social media and geographically identified. For example, the building is geographically identified from a satellite photograph of the building before the flood (e.g., available from Google Maps or some other suitable source). The water height can be estimated from the image of FIG. 10a and assigned to the geographically identified point. The water height can be estimated, for example, by looking at the position of the water relative to the visible window 1010 of the building. The water height can be determined in various ways from the image. In another example, the water height can be estimated from social media and news images by looking at the water height relative to a vehicle. In FIG. 10b, the wheels 1020 of the pickup truck visible in the image indicate that they are flooded up to approximately the midpoint of the wheels. By knowing the average height of the wheels of the pickup truck, a relatively accurate water depth can be estimated, and the water depth estimate in the position location information of the truck can be used to further calibrate the water depth model.

[0113] FIG. 11 is a two-dimensional image of a flood showing how the extent of an environmental event such as a flood can be determined from a model. The dotted line is drawn around the water area, and any building within that range having geographical location information lower than the water surface can be considered affected, for example, can be considered flooded. A building with an elevation of a geographically located place lower than the water surface can be considered to have a medium severity, and a building lower than the flood water surface can be considered to have a high severity. In FIG. 11, buildings with darker shading represent higher severity, medium severity with medium shading, and buildings with lighter shading represent low severity. Buildings not within the flood range are considered unaffected and are not shaded.

[0114] FIG. 12 shows some processes that have been successfully tested for monitoring flooding and modeling its extent and progression. These processes can be modified for use in monitoring other environmental events.

[0115] The processes shown in FIG. 12 include obtaining non-real-time data related to the event and using the non-real-time data in estimating the severity of the event at each location within a specified area. In this example, determining the extent and severity of the event includes combining the non-real-time data with additional environmental data collected in real time to create a model of the extent and severity of the event.

[0116] These different processes are also referred to below as "approaches". This is because they approach the task of event modeling in different ways, but can be combined to achieve a result with a higher level of confidence than the results obtained by individual approaches.

[0117] Box 1210 in FIG. 12 shows the hydrodynamic model approach. Box 1220 shows the image thresholding approach. Box 1230 shows the contour approach. In the image thresholding and contour approaches, the real-time data includes image data, and the non-real-time data includes elevation data regarding the identified area.

[0118] Any one or more of these can be used alone or in combination in the monitoring of environmental events.

[0119] In the hydrodynamic model approach 1210, a hydrodynamic DTM 1211 known in the art, or other elevation data (e.g., DEM), can be used together with additional data such as real-time environmental data to model floods and the severity at each location within the flood area. In the example of FIG. 12, additional data in the form of precipitation 1215 and water level gauge 1216 data is shown together with land use / land cover "LULC" information 1214. The water level gauge data can provide information, for example, on how much water is flowing through the river at that time. LULC and the hydrodynamic DTM may be non-real-time data. The AOI 1212 can be identified from the watershed data as described above. This data can be used to model the extent of the flood and the severity at each location within the flood area by inputting the data into a known software tool for flood modeling such as HEC-RAS 2D. As described above, by using one or preferably multiple reliable estimates of flood depth and past elevation data, it is possible to estimate the water depth across the entire area of interest (AOI) at a resolution corresponding to, for example, the resolution of the DEM.

[0120] Both the image thresholding and contour approaches use live observations of the event to model its local severity and optionally also its extent. Visual observations from images obtained on or above the ground have been found to be particularly useful.

[0121] In the image threshold processing approach 1220, for example, using only image data such as SAR or other image data, and elevation information such as that obtained from DTM or DEM, the water level or water depth can be determined, optionally over the entire area of interest (AOI). After optionally generating a flood mask to define the AOI, threshold processing can be applied to an image such as an SAR image. In this threshold processing, known image analysis techniques are used to classify pixels based on whether water exists in the ground area corresponding to the pixel. Then, for example, by comparing the current image with a previous image, it can be determined whether the pixel indicates inundation. The area represented by the pixels indicating inundation corresponds to the flood extent. In other words, the area can be used to estimate the flood extent. The area can further be used with the DEM 1250 to determine the water depth, or flood height, or flood extent at locations within the identified area. For example, at the boundary of the flood extent, the water depth is zero, and the height of the flood water surface can be determined from the height of the ground in the flood extent. The flood depth at any location within the flood extent can be determined by subtracting the surface elevation (obtained from the DEM) of that location from the water level at that location. In another example, if an area of high ground appears to be inundated, the DEM can be used to infer that areas of low ground are inundated more deeply. Only this information has been found to provide a useful estimate of the local severity of the water within its extent.

[0122] In the contour approach, any information related to the height or depth of water may be used, along with elevation information, to determine the extent of flooding and its local severity. Under the condition of being reliable, a single height or depth information may be sufficient to provide an initial estimate. The height or depth information can be obtained from any suitable source, many of which are described above. FIG. 12 shows several sources, specifically, for example, a water level gauge 1231 that measures the water level of rivers and water areas, SNS data 1232 such as the above-mentioned photos, and image data 1233 such as SAR. Any of these may be used to estimate the water height, and in combination as known in the art to increase the reliability in the estimation.

[0123] The use of the term "contour" is derived from mapping a geographical area where contour lines are used to join geographical structures at the same height, for example, a height above sea level. Knowing the water height at a location, it can be assumed that the extent of the water area is bounded by the geographical structure at the water height. In other words, it can be assumed that the area bounded by the contour line at that height is flooded. Within that area, the local depth or height of the water may be estimated using an altitude model such as DEM 1250 as described above.

[0124] From the above, it can be inferred that it is assumed that the water is at the same level within the AOI. This may not be the case if the water is flowing relatively fast. Therefore, any of the methods described here may take into account the flow rate estimated from the additional data collected in operation 107, rather than assuming that the water surface is horizontal.

[0125] Each of approaches 1210, 1220, and 1230 can be used individually or in combination to model and monitor the progression of a flood. However, any of these may also be used in combination to provide a more accurate model, for example, a model with a higher level of confidence. A model obtained from one approach can be used to validate another approach. Tests have shown that the hydromodeling approach 1210 may be wrong in that the image thresholding or contour approach is more accurate.

[0126] Therefore, FIG. 12 shows that the results of all three approaches are subject to quality control and calibration 1270, from which a flood extent 1272 can be generated and a raster model 1271 of the water level (WT) can be generated. One or more of a confidence map 1281, statistics and estimation of building inundation depth 1282, and visualization (e.g., web-based) can also be generated, examples of which are shown in FIGS. 5, 6, and 11.

[0127] FIG. 13 shows how the principle of monitoring environmental events can be applied to other events, in this case, fires. In a monitoring mode or phase such as operation 101, first environmental data can be collected. FIG. 13 shows an example of the Wildland Fire Interagency Geospatial Services (WFIGS) group that provides geospatial data related to fires. This data may be buffered and analyzed, for example, to identify fires that meet the criteria in operation 105 in order to pose a threat to the population.

[0128] Events that pose a threat to the group or meet other criteria may then trigger a target stage or mode corresponding to operation 107 where additional data is collected. FIG. 13 shows initial data regarding a fire that can be used to determine criteria for collecting additional data. Similar to the example of flooding, this can be collected from many sources, for example as described with reference to FIG. 2. The information thus obtained can be used to determine hotspots in the area affected by the fire and predict its possible paths. In the example of a fire, the local severity within the scope of the fire can be determined at different resolutions up to the level of a single building, as described in relation to flooding.

[0129] Referring now to FIG. 14, which shows a block diagram of an exemplary computing system 1400 that can be used to implement any of the systems and methods described above. Computing system 1400 may comprise a single computing device or component, and the functions of system 1400 may be distributed across multiple computing devices. As described above, the systems described herein are likely to be distributed across multiple locations. Thus, the systems described above can include multiple systems as shown in FIG. 14. Each of the servers described above can be implemented, for example, in a computing system as shown in FIG. 14. Computing system 1400 may include, for example, one or more controllers (such as controller 1405) that can be a central processing unit processor (CPU), chip, or any suitable processor or arithmetic or computing device, an operating system 1415, a memory 1420, a storage 1401 (which may include, for example, database 220), an input device 1435, and an output device 1440.

[0130] One or more processors in one or more controllers, such as controller 1405, may be configured to execute any of the methods described herein. For example, one or more processors in controller 1405 may be connected to a memory 1420 that stores software or instructions that, when executed by the one or more processors, cause the one or more processors to perform operations. Controller 1405 or the central processing unit within controller 1405 may be configured to perform the operations shown in FIG. 1, for example, using instructions stored in memory 1420.

[0131] Operating system 1415 may be any code segment designed and / or configured to perform tasks that coordinate, schedule, arbitrate, monitor, control, or manage the operation of computing system 1400, for example, scheduling the execution of programs. Operating system 1415 may be a commercial operating system. Memory 1420 may be, for example, random access memory (RAM), read only memory (ROM), dynamic RAM (DRAM), synchronous DRAM (SD-RAM), double data rate (DDR) memory chips, flash memory, volatile memory, non-volatile memory, cache memory, buffers, short-term storage units, long-term storage units, or other suitable memory units or storage units, and may include these. In one embodiment, memory 1420 is a non-transitory processor-readable storage medium that stores instructions that are executed by controller 1405. Memory 1420 may be, in multiple cases, different memory units or may include them.

[0132] The executable code 1425 can be any executable code, such as an application, program, process, task, or script. The executable code 1425 can, in some cases, be executed by the controller 1405 under the control of the operating system 1415. The executable code 1425 can include code for selecting an offer provided according to some embodiments of the present invention and calculating a reward prediction.

[0133] The storage 1401 can be one or more storage components, such as, for example, a hard disk drive, a solid state drive, a compact disc (CD) drive, a CD-Recordable (CD-R) drive, a universal serial bus (USB) device, or other suitable removable and / or fixed storage units, and can include them. The memory 1420 can be a non-volatile memory having the storage capacity of the storage 1430. Thus, although shown as a separate component, the storage 1430 can be embedded in or included in the memory 1420.

[0134] Inputs to and outputs from a computing system according to some embodiments of the present invention can be via an API such as the API 1412 shown in FIG. 14. The API 1412 shown in FIG. 14 operates under the control of the controller 1205 that executes instructions stored in the memory 1420.

[0135] The input device 1435 can be or include a mouse, a keyboard, a touch screen or pad, or any suitable input device. As indicated by block 1435, it will be recognized that any suitable number of input devices can be operably connected to the computing system 1400.

[0136] The output device 1440 can include one or more displays, speakers, and / or any other suitable output device.

[0137] Input device 1435 and output device 1440 are shown as providing input to system 11400 via API 1412 for the purposes of the embodiments of the present invention. Output device 1440 may provide input to other parts of system 1400 or receive output from other parts.

[0138] Some embodiments of the present invention may include a computer-readable medium or article. For example, a non-transitory readable medium for a computer or processor, or a non-transitory storage medium for a computer or processor, such as a memory, a disk drive, or a USB flash memory. These encode, contain, or store instructions (e.g., computer-executable instructions) that, when executed by a processor or controller, perform the methods disclosed herein. For example, some embodiments of the present invention may include a storage medium such as memory 1420, computer-executable instructions such as executable code 1425, and a controller such as controller 1405.

[0139] Systems according to some embodiments of the present invention may include, but are not limited to, components such as a plurality of central processing units (CPUs) (e.g., similar to controller 1405), or other suitable general-purpose or application-specific processors or controllers, a plurality of input units, a plurality of output units, a plurality of memory units, and a plurality of storage units. Embodiments of the system may further include other suitable hardware components and / or software components. In some embodiments, the system may be, for example, a personal computer, a desktop computer, a mobile computer, a laptop computer, a notebook computer, a terminal, a workstation, a server computer, a personal digital assistant (PDA) device, a tablet computer, a network device, or other suitable computing device, and may include these. Unless explicitly stated otherwise, embodiments of the methods described herein are not restricted to a particular order or sequence. Additionally, some of the method embodiments or elements described may occur or be executed at the same time.

[0140] Some of the operations of the methods described herein may be performed, for example, by software in the form of a computer program, including in a machine-readable form, such as computer program code. Thus, some aspects of the present invention, when implemented in a computing system, provide a computer-readable medium for causing the computing system to execute some or all of the operations of any of the methods of the present invention. The computer-readable medium may be in a temporary or tangible (or non-transitory) form, such as a storage medium, such as a disk, a thumb drive, a memory card, etc. The software may be adapted to execute on a parallel processor or a serial processor so that the method steps can be executed in any suitable order or simultaneously.

[0141] In this use, it is recognized that firmware and software are goods with individually tradable value. This is designed to include software that is computed or controlled on "dams" or standard hardware to perform desired functions. It is also aimed at including software that "describes" or defines hardware configurations, such as HDL (Hardware Description Language) software for designing silicon chips or configuring general-purpose programmable chips to perform desired functions.

[0142] The above embodiments are almost automated. In some examples, the user or operator of the system can manually instruct some of the operations of the method to be performed.

[0143] In embodiments of the present invention, in the present invention, as described elsewhere in the present invention, the system can be implemented as any form of computing and / or electronic system. Such a device may include one or more processors that are microprocessors, controllers, or other suitable types of processors that process computer-executable instructions that control the operation of the device to collect and record routing information. In some examples, such as when a system-on-chip architecture is used, the processor may include one or more fixed-function blocks (also called accelerators) that implement part of the method in hardware (not software or firmware). Platform software including an operating system or other suitable platform software may be provided to a computing-based device so that application software can be executed on the device.

[0144] Here, the "computing system" is used to refer to any device having the processing ability to execute instructions. Those skilled in the art will understand that such processing ability can be incorporated into many different devices, and thus the term "computing system" includes personal computers, servers, smart mobile phones, personal digital assistants, and many other devices.

[0145] It is understood that the above advantages and benefits may pertain to one embodiment or several embodiments. The plurality of embodiments are not limited to those that solve any or all of the problems mentioned or have the advantages and benefits mentioned.

[0146] Any reference to an "item" or "piece" refers to one or more of these items, unless otherwise specified. As used herein, the term "comprising" means including the operations, actions, or elements of a specified method, but such operations or elements do not constitute an exclusive list, and the method or apparatus may include additional operations or elements.

[0147] Furthermore, to the extent that the term "including" is used within the detailed description or claims, since the term "including" is interpreted as a transitional term within the claims, this term is intended to have the same inclusiveness as the term "comprising".

[0148] The accompanying drawings illustrate an exemplary method. The method is shown and described as a series of operations to be performed in a particular order, but it should be understood and appreciated that the method is not limited by the order. For example, some operations can occur in an order different from that described herein. Further, one action may occur simultaneously with another action. Additionally, in some cases, not all actions may be necessary to perform the method described herein.

[0149] The order of operations of the methods described in this specification is illustrative, but the operations can be performed in any suitable order or, where appropriate, simultaneously. Further, operations can be added or replaced, or individual operations can be deleted, in any of the methods without departing from the scope of the subject matter described herein. Aspects of any of the above embodiments can be combined with aspects of any of the other above embodiments to further form embodiments.

[0150] The description of the above preferred embodiments is shown by way of example only, and it should be understood that those skilled in the art can make various modifications. The above content includes an example of one or more embodiments. Of course, it is impossible to describe all possible modifications and changes of the above apparatus or method for the purpose of explaining the foregoing aspects, but those skilled in the art can recognize that many more modifications and combinations of various aspects are possible. Therefore, the described aspects are intended to include all such changes, modifications, and variations that fall within the scope of the appended claims.

Claims

**Claim 1** A computer-implemented method for monitoring one or more environmental events on Earth, comprising: - receiving a notification of the occurrence of an environmental event derived from first environmental data; - identifying a region on Earth corresponding to the notification; - determining that the event meets one or more predetermined event criteria; - monitoring the event by collecting additional environmental data in response to determining that the event meets one or more predetermined event criteria; - determining that the additional environmental data is relevant to the event according to one or more relevance criteria; - tagging the additional data to the event in a geographically indexed database in response to determining that the additional environmental data is relevant to the event; - estimating the severity of the event at each location within the identified region using the tagged data in the database. **Claim 2** The method of claim 1, wherein identifying the region includes obtaining additional information to identify the region. **Claim 3** The method of claim 1 or 2, wherein identifying the region includes identifying regions on Earth that are likely to be affected by the event. **Claim 4** The method according to any one of the preceding claims, wherein collecting the additional environmental data includes collecting the first data at a higher frequency. **Claim 5** The method according to any one of the preceding claims, wherein collecting the additional environmental data includes collecting data from additional sources not included in the first data. **Claim 6** The method of claim 5, wherein the additional source not included in the first data includes a social media source. **Claim 7** The method according to any one of claims 1 to 5, wherein one or both of the first environmental data and the additional environmental data includes social media data. **Claim 8** The method according to any one of the preceding claims, further comprising obtaining non-real-time data related to the event and using the non-real-time data in estimating the severity of the event at each location within the identified region. **Claim 9** The method of claim 8, wherein determining the scope and severity of the event includes comparing information derived from the additional data with the non-real-time data. **Claim 10** The estimation according to claim 8 or 9 includes creating a model of the scope and severity of the event by combining the non-real-time data with additional environmental data collected in real time.

11. The method according to claim 10, wherein the real-time data includes image data and the non-real-time data includes elevation data regarding the specified area.

12. The event includes a flood, and the method includes analyzing pixels in the image data to determine whether water exists in the area on the earth corresponding to the pixels, and using the pixel analysis in combination with the elevation data of the area to determine the water depth at each location within the specified area, as claimed in claim 11.

13. The event includes a flood, the non-real-time data includes elevation data regarding the specified area, the real-time data includes water level data, and the method includes determining the scope of the flood by identifying structures at the water level using the elevation data, and determining the water depth at each location within the scope using the elevation data, as claimed in claim 10.

14. The method according to any one of claims 8 to 13 includes continuing to monitor the event by collecting additional environmental data and updating the model using the additional environmental data.

15. The method according to any one of the preceding claims, wherein the additional environmental data includes one or more images of the event.

16. The method according to claim 15 includes analyzing the one or more images to determine the severity of the event.

17. The determination of the severity of the event according to any one of the preceding claims includes determining the height of the damage caused by the event in relation to one or more structures on the earth.

18. The method according to any one of the preceding claims, wherein one or both of the first environmental data and the additional environmental data includes data obtained from satellites in space.

19. The data obtained from satellites in space according to claim 18 includes synthetic aperture radar data.

20. The satellite in space is part of a constellation of 5 or more satellites, 10 or more satellites, 20 or more satellites, or 50 or more satellites, as claimed in claim 18.

21. The method according to claim 18, 19, or 20, wherein the data obtained from the satellite of the universe includes data obtained at a frequency of at least once every 12 hours, at least once every 6 hours, or at least once every 3 hours.

22. The method according to any one of the preceding claims, wherein the location includes landmarks, buildings, or other features within the specified area.

23. The method according to any one of the preceding claims, wherein the location includes areas within the specified area.

24. The method according to claim 23, wherein the areas within the specified area are continuous.

25. A computing system comprising one or more processors and a memory, wherein the one or more processors are configured to implement the method according to any one of claims 1 to 24.

26. A computer-readable medium comprising instructions that, when implemented on one or more processors in a computing system, cause the system to implement the method according to any one of claims 1 to 24.

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