SYSTEM AND METHOD FOR DETECTING INFRASTRUCTURE HAZARD AREAS - Patent application

Abstract

A system and method for determining infrastructure hazardous areas is disclosed. The system and method may include receiving a first scanned image of an area from a radio frequency (RF) radiation sensor, the area at least partially including infrastructure, receiving additional data, filtering electromagnetic noise from the first scanned image using the additional data, receiving a location of the infrastructure within the area, determining an inspection area around the infrastructure, estimating from the filtered scanned image an amount of clay in soil contained in the inspection area, calculating from the filtered scanned image a soil moisture content of locations within the inspection area, and determining hazardous locations having a soil moisture content above a predetermined threshold, the threshold being determined based on the estimated clay amount.

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of priority to UK Patent Application No. 2014152.9, filed September 9, 2020, the contents of which are hereby incorporated by reference in their entirety.

[0002] The present invention relates generally to remote detection of infrastructure danger areas, and more particularly to a system and method for remote detection of infrastructure danger areas by soil moisture using microwave radiation. [Background technology]

[0003] Water, more specifically hidden water, is the enemy of infrastructure. Water plays a key role in the mechanical performance and lifespan of any infrastructure. For example, roads perform well if there is no excess water in the road structure and subgrade soil. Increased water content reduces the bearing capacity of the soil or aggregates, which increases the rate of deterioration under traffic loads, increasing the risk of road collapse and shortening the road's lifespan. In such cases, roads require more frequent maintenance measures and reconstructions than well-drained road structures. For this reason, road drainage systems need to function effectively over the lifespan of the pavement. Current predictions of the possible effects of climate change only magnify this problem, and modern asset management principles rightfully compel road owners to consider ways to maximize the use of available budgets and improve the lifespan of roads.

[0004] However, monitoring the moisture content is a major challenge in pavement structures and subgrade soils. Another challenge is to monitor the effectiveness of the drainage system in draining the structure sufficiently to ensure adequate bearing capacity of the road infrastructure.

[0005] In mountainous regions, further factors are added to the risk of land collapse or landslides. All studies and investigations on landslide initiation mechanisms point to the initial water content as the main factor. In mountainous terrain, rainfall-induced landslides impose significant risks to people and infrastructure. In rainfall-induced landslides, the availability of unconsolidated material, topography, vegetation cover, and hydrological prerequisites (pre-wetting of mountain slopes) determine the risk of sliding down the slope and are called "initiation factors".

[0006] Although rainfall measurements are available at increasing spatial resolution and realization and are used in different models, the use of rainfall thresholds and rainfall-based indices for early warning of landslides and the resulting damage to infrastructure is very limited and in most cases has no correlation with infrastructure hazard zones.

[0007] There are no good current solutions for comprehensive detection of potential collapses in infrastructure and land. Most solutions focus on the micro-level, using sensors, core samples, and other geomorphological means. Summary of the Invention

[0008] Embodiments of the present invention are directed to systems and methods for determining infrastructure hazardous areas. The embodiments may include receiving a first scanned image of an area at a first polarization from a radio frequency (RF) radiation sensor, the first scanned image including a first high frequency reflection from the area at a first resolution, the sensor being located at least 50 meters above the area, the area at least partially comprising infrastructure, receiving additional data, filtering electromagnetic noise from the first scanned image using the additional data, receiving a location of the infrastructure in the area, determining an inspection area around the infrastructure, estimating from the filtered scanned image an amount of clay in soil contained in the inspection area, calculating from the filtered scanned image a soil moisture content of locations within the inspection area, and determining hazardous locations having a soil moisture content above a predetermined threshold. In some embodiments, the threshold is determined based on the estimated clay amount.

[0009] In some embodiments, topographical data for the area may be received and a watershed surrounding at least a portion of the infrastructure may be identified. In some embodiments, the inspection area may be a watershed.

[0010] Embodiments of the present invention are directed to systems and methods for determining subsurface liquid (e.g., water) content. The embodiments may include receiving a first scanned image of an area at a first polarization, the first scanned image including a first RF reflection (e.g., L-band microwave reflection) from the area, receiving a second scanned image of the area at a second polarization, the second scanned image including a second RF reflection (e.g., L-band microwave reflection) from the area, the first and second scanned images being from a first sensor for RF radiation reflections attached to an object located at least 50 meters ("m"), 70 m, 100 m or more above the area, and filtering electromagnetic noise from the first scanned image using the second scanned image. An embodiment of the method may include creating a water composition map (e.g., a water roughness map based on typical roughness values) based on typical salinity values ​​of various types of water sources and the filtered first scanned image, identifying a first type of water source using the water composition map and the filtered first scanned image, and calculating water content at locations within the region based on the identified first type of water source.

[0011] An embodiment of the invention includes a method of determining subsurface liquid (e.g., water) content. An embodiment of the method may include receiving a first scanned image of an area at a first polarization, the first scanned image including a first RF reflection (e.g., L-band microwave reflection from the area), the first scanned image being from a first sensor for detecting RF radiation reflections, the first sensor being attached to an object located at least 50 meters ("m"), 70 m, 100 m or more from the area. An embodiment of the method may further include receiving optical data of or representative of at least a portion of the scanned area. According to some embodiments, the optical data may be captured at wavelengths ranging from 1 millimeter to 10 nanometers (e.g., infrared to ultraviolet). According to some embodiments of the method, electromagnetic noise from the first scanned image may be filtered using the optical data. An embodiment of the method may include creating a water composition map based on typical salinity values ​​of various types of water sources and the filtered first scanned image, identifying a first type of water source using the water composition map and the filtered first scanned image, and calculating water content at locations within the region based on the identified first type of water source.

[0012] The subject matter which is regarded as the invention is particularly pointed out and distinctly claimed in the concluding portion of this specification. The invention, however, both as to organization and method of operation, together with its objects, features, and advantages, may best be understood by reference to the following detailed description when read in connection with the accompanying drawings, in which: [Brief description of the drawings]

[0013] [Figure 1] FIG. 1 is a high-level block diagram of a system for detecting groundwater in accordance with some embodiments of the present invention. [Diagram 2] 1 is a flowchart of a method for detecting groundwater according to some embodiments of the present invention. [Figure 3A]1 is a scanned image of L-band microwave reflections from an area in horizontal-vertical (HV) polarization according to some embodiments of the present invention. [Figure 3B] 1 is a scanned image of L-band microwave reflections from an area in horizontal-horizontal (HH) polarization according to some embodiments of the present invention. [Figure 4] 11 is a HH polarized scanned image after filtering electromagnetic noise according to some embodiments of the present invention. [Diagram 5] 1 is a water composition map according to some embodiments of the present invention. [Figure 6] 1 is a map with identified drinking water sources according to some embodiments of the present invention. [Figure 7] 1 is a map with identified drinking water leaks according to some embodiments of the present invention. [Figure 8] 1 is a graphical map showing the amount and location of a leak, according to some embodiments of the present invention. [Figure 9] 1 is a flowchart of a method for detecting groundwater according to some embodiments of the present invention. [Figure 10] 1 is a flowchart of a method for detecting groundwater according to some embodiments of the present invention. [Figure 11] 1 is an example of a graphical representation of geographic data according to some embodiments of the present invention. [Figure 12] 1 is an example of a graphical representation of geographic data according to some embodiments of the present invention. [Figure 13] 1 is a flowchart of a method for determining infrastructure hazard zones of infrastructure and / or land according to some embodiments of the present invention. [Figure 14] 1 is an example of a graphical representation of a watershed of a terrain, according to some embodiments of the present invention. [Figure 15] 1 is an example of slope mapping around an infrastructure according to some embodiments of the present invention. [Figure 16]1 is an example of a graphical representation of an infrastructure hazard area due to high soil moisture content, according to some embodiments of the present invention. [Figure 17] 1 is an example of a graphical representation of infrastructure danger zones due to land collapse (landslide), according to some embodiments of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] It will be appreciated that for simplicity and clarity of illustration, elements shown in the figures have not necessarily been drawn to scale. For example, dimensions of some elements may be exaggerated relative to other elements for clarity. Further, where considered appropriate, reference numerals may be repeated among the figures to indicate corresponding or analogous elements.

[0015] In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will be understood by those skilled in the art that the present invention may be practiced without these specific details. In other instances, well-known methods, procedures, and components have not been described in detail so as not to obscure the present invention.

[0016] Although embodiments of the invention are not limited in this respect, for example, discussions utilizing terms such as "processing," "computing," "calculating," "determining," "establishing," "analyzing," "ascertaining," and the like, may refer to operations and / or processes of a computer, computing platform, computing system, or other electronic computing device that manipulate and / or transform data represented as physical (e.g., electronic) quantities in the computer's registers and / or memory into other data similarly represented as physical quantities in the computer's registers and / or memory or other information non-transitory processor-readable storage medium that stores instructions that, when executed by a processor, cause the processor to perform operations and / or processes as discussed herein. Although embodiments of the invention are not limited in this respect, as used herein, the terms "plurality" and "a plurality" may include, for example, "multiple" and "two or more." The terms "plurality" or "a plurality" may be used throughout the specification to describe two or more components, devices, elements, units, parameters, and the like. As used herein, the term set may include one or more items. Unless expressly stated, the method embodiments described herein are not limited to a particular order or sequence. In addition, some of the method embodiments described, or elements thereof, may occur or be performed at the same time, contemporaneously, or concurrently.

[0017]

[0003] Embodiments of the present invention relate to methods and systems for remote detection of groundwater, e.g., potable water leaks from municipal water systems. Water sources, such as water pipes, lakes, swimming pools, etc., reflect electromagnetic (EM) waves at both underground and above ground levels.

[0018] Water can reflect EM waves at frequencies in the RF range, e.g., microwaves such as L-band frequencies, P-band frequencies, C-band frequencies, etc. As used herein, the RF range can include any EM wave frequency within the range of 20 KHz to 300 GHz.

[0019] Every water source has a typical reflection and a typical EM behavior, and the type of water source can be identified using these typical reflections. An EM sensor placed on an elevated platform, e.g., a satellite, an aircraft, a balloon, can transmit EM waves of a known frequency (e.g., 1.3 GHz) toward an area and read the EM waves reflected from the area. The sensor can transmit a scanned image including all the reflections detected from a particular area for further processing by a system according to some embodiments of the present invention. The sensor can include a synthetic aperture radar (SAR) SAR, which uses the movement of a SAR antenna over a target area to provide finer spatial resolution than is possible with conventional beam scanning radar. The scanned image can include all the EM reflections received from the area. These reflections can include both reflections from the water source and undesired reflections from other objects in the area, such as buildings, vegetation, and other topographical features of the area. To identify water-related reflections, undesired reflections (e.g., EM noise reflections) can be filtered or removed from the scanned image. To reduce (e.g., remove or filter) EM noise, two or more scan images may be taken from an area at two different polarizations, for example, a horizontal-vertical (HV) scan image and a horizontal-horizontal (HH) scan image. The HH reflection may be received from a transmission with horizontal polarization received with horizontal modulation. The HV reflection may be received from a transmission with horizontal polarization received with vertical modulation.

[0020] Some embodiments of the present invention are capable of transmitting and receiving reflections with two different resolutions. For example, the second sensor may receive reflections with a resolution less than that of the first sensor (e.g., 12m). 3 ) with a resolution higher than 6m 3HH and HV scan images may be received from a first sensor having a first resolution, and additional HH (and / or HV) scan images may be received from a second sensor, such that the HH and HV scan images have a first resolution. The scan images from the first sensor may be used to identify EM noise reflections and filter (e.g., remove) them from the scan images received from the second sensor. In some embodiments, all scan images are at high resolution (e.g., 6m or higher). 3 , 3m 3 ) from a single sensor having a polarization of 100 MHz. Two HH and HV scan images may be received from a single sensor and may contain all the information needed to filter (e.g., reduce) EM noise and receive a scan image with sufficient resolution. In some embodiments, additional scan images with additional polarizations may be received from a single sensor, all at the same resolution. Such additional scan images may allow for further reduction of EM noise.

[0021] After filtering of the EM noise, at least some of the scanned reflections may be identified as water reflections. Different water sources (e.g., drinking water, sewage, oceans, lakes, swimming pools, etc.) may be able to be distinguished from one another since they have different typical EM roughness (typical EM reflections) due to different salinity levels. In some embodiments, EM reflections from sewage pipes, oceans, lakes, and swimming pools may be filtered or removed from the filtered noise scanned image, thus leaving only reflections received from water leaks in the scanned image. The resolution of the scanned image (e.g., at least 3m) may be adjusted to reduce the noise of the scanned image. 3 ) is larger than the diameter of the tube, so only leaks larger than this resolution can be detected, not the tube itself.

[0022] In some embodiments, drinking water content or volume may be calculated from the drinking water related reflectance and converted to water retention (e.g., cubic meters / hour, gallons / hour, etc.) This information may be displayed on a map (e.g., a city road map) to show, for example, the volume and location of suspected leaks within the city, respectively.

[0023] In some embodiments, the received scanned images may be used to determine infrastructure hazard areas, including locations at high risk of collapse due to above ground and / or groundwater, and / or areas more susceptible to landslides or soil movement due to one or more of soil moisture content, topography of the area, soil parameters, etc. As used herein, the term infrastructure refers to man-made structures and may include facilities and systems that serve a country, city, or other area. For example, infrastructure may include roads, railways, bridges, tunnels, water systems, sewer systems, power grids, buildings, power plants, parking lots, industrial areas, etc. Thus, in embodiments, the present invention is directed to identifying areas where such infrastructure facilities may be at risk of undersupport.

[0024] In some embodiments, an inspection area may be determined in which infrastructure danger areas may be identified. The inspection area may be determined to encompass the infrastructure included in the scanning area, or a part thereof. The inspection area may be predefined at a fixed distance surrounding the infrastructure (e.g., 20 meters on either side of a road, railway, bridge, etc.) or may be determined based on other data such as topography (e.g., watershed) or the amount of clay in the soil.

[0025] The received RF reflectance scanned image(s) may be used to determine the amount of clay in the soil located within the inspection area. The amount of clay may affect the soil's ability to absorb water. In some embodiments, the soil moisture content of various locations within the inspection area may be further calculated. In some embodiments, infrastructure hazard areas may be determined as locations having soil moisture above a threshold. The threshold may be set based on the calculated amount of clay.

[0026] Reference is now made to FIG. 1, which is a high-level block diagram of an exemplary system for remotely detecting groundwater, according to some embodiments of the present invention. The system 100 may include a computer processing device 110, a storage unit 120, and a user interface 130. The system 100 may receive L-band microwave scanning images from a region including at least one groundwater source 160 from a sensor 150. The processing unit 110 may include a processor 112, which may be, for example, a central processing unit (CPU), a chip, or any suitable computing or calculation device, an operating system 114, and a memory 116. The system 100 may be included in a desktop computer, a laptop commuter, a tablet, a mainframe computer, and the like. The processor 112 or other processor may be configured to perform methods according to embodiments of the present invention by executing instructions stored in a memory, such as, for example, the memory 116. In some embodiments, the system 100 may further receive L-band microwave scanning images from a region including at least one groundwater source 160 from a second sensor 152.

[0027] The operating system 114 may be or include any code segment designed and / or configured to perform tasks that involve coordinating, scheduling, arbitrating, supervising, controlling, or otherwise managing the operation of the processing device 110, such as scheduling the execution of programs. The operating system 114 may be a commercially available operating system. The memory 116 may be or include, 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 memory units, long-term memory units, or other suitable memory or storage units. The memory 116 may be or include multiple, possibly different, memory units.

[0028] The memory 116 may store any executable code, such as an application, a program, a process, an operation, a task, or a script. The executable code, when executed by the processor, may cause the processor to detect groundwater and perform methods according to embodiments of the present invention. The executable code may be executed by the processor 112, possibly under the control of the operating system 114. The memory 116 may store data, such as images, grayscale or intensity levels, scanned images, reflectance, and the like.

[0029] Storage device 120 may be or include, for example, a hard disk drive, a floppy disk drive, a compact disk (CD) drive, a CD recordable (CD-R) drive, a universal serial bus (USB) device, or other suitable removable and / or fixed storage unit. Content may be stored in storage device 120 or loaded from storage device 120 to memory 116 where it may be processed by processor 112. For example, storage device 120 may include L-band microwave scanned images of an area at various polarizations received from sensor 150, geographic data about the scanned area (e.g., soil type, moisture content in solids, road maps, etc.), and salinity values ​​of various types of water bodies, or any other necessary data according to embodiments of the present invention.

[0030] The user interface 130 may be, be displayed on, or include a screen 132 (e.g., a monitor, display, CRT, etc.), an input device 134, and an audio device 136. The input device 134 may be a keyboard, a mouse, a touch screen or pad, or any other suitable device that allows a user to communicate with the processor 112. The screen 132 may be any screen suitable for displaying maps and / or scanned images according to embodiments of the present invention. In some embodiments, the screen 132 and the input device 134 may be included in a single device, for example a touch screen. It may be appreciated that any suitable number of input devices may be included in the user interface 130. The user interface 130 may include an audio device 136, such as one or more speakers, earphones, and / or any other suitable audio device. It may be appreciated that any suitable number of output devices may be included in the user interface 130. Any applicable input / output (I / O) devices may be connected to the processing unit 110. For example, a wired or wireless network interface card (NIC), a modem, a printer or facsimile machine, a universal serial bus (USB) device, or an external hard drive may be included in the user interface 130 .

[0031] Embodiments of the invention may include articles such as computer or processor non-transitory readable media or computer or processor non-transitory storage media, e.g., memory, disk drive, or USB flash memory, that encode, contain, or store instructions, e.g., computer-executable instructions, that when executed by a processor or controller, perform the methods disclosed herein.

[0032] The storage medium may include any type of medium suitable for storing electronic instructions, including, but not limited to, floppy disks, optical disks, any type of disk including compact disk-read only memory (CD-ROM), compact disk-rewritable (CD-RW), and magneto-optical disks, dynamic RAM (DRAM), erasable programmable read-only memory (EPROM), flash memory, electrically erasable programmable read-only memory (EEPROM), read-only memory (ROM) such as magnetic or optical cards, semiconductor devices such as random access memory (RAM), or programmable storage units.

[0033] System 100 may include or 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 tablet computer, a network device, or any other suitable computing device. Unless expressly stated, the method embodiments described herein are not limited to a particular order or sequence. In addition, some of the described method embodiments or elements thereof may occur or be performed at the same time.

[0034] Sensor 150 and / or sensor 152 may be any sensor configured to scan and detect groundwater, such as groundwater source 160, using electromagnetic radiation. For example, sensor 150 may include a receiver for radar or synthetic aperture radar (SAR). Sensor 150 and / or 152 may be located, for example, on an elevated platform or structure 155. Elevated platform or structure 155 may be, for example, a satellite, an aircraft, or a balloon, and may be located at least 50 meters above the ground (e.g., 50 m height), for example, 70 meters, 100 meters, 150 meters, 500 meters, 1000 meters, or more. Sensor 152 may have a different detection resolution (e.g., higher resolution) than sensor 150.

[0035] See FIG. 2, a flow chart of an exemplary method for remotely detecting groundwater, according to some embodiments of the present invention. The method embodiment of FIG. 2 may be performed, for example, by the system 100 or by other systems. In operation 210, the method embodiment may include receiving a first scanned image of an area at a first polarization. The first scanned image may be a two-dimensional scanned image of the area. The first scanned image may include a first L-band microwave reflection from the area. The first scanned image may include reflections received from a predefined area on the ground that have been converted into data, for example data including pixel data. The size of each pixel may depend on the resolution of the sensor (e.g., sensors 150, 152) located at least 50 meters above the ground. The sensor may receive reflections from both above-ground and below-ground objects. A processor associated with the sensor may convert these reflections into data including pixels having different gray levels. This data may be received and analyzed by the system 100. The size of the scanned area may be determined by the sensor (e.g., a SAR sensor) and received as raw data. The grayscale level of each pixel converted from the microwave reflection of the scanned image is then converted to a single area unit (e.g., 3 m) at each depth (e.g., 3 m). 2 ) may relate to the reflected intensity level received from the 3 , 3m 3 , 6m 3 , 12m 3 etc.

[0036] The L-band microwave reflections or other radio frequency (RF) wave reflections may be received from a sensor (e.g., sensor 150 or 152) for detecting the L-band microwave or RF radiation reflections. The sensor may be mounted on an object (e.g., platform 155) located at least 50, 100 meters, 1000 meters above the area. Such a sensor may be mounted on an elevated platform, such as a satellite, an aircraft, or a balloon. The L-band microwaves (e.g., radio frequency in the 1-2 GHz frequency range) or other RF waves may be transmitted from a transmitter toward the scanning area and reflected from the scanning area after interacting with both above-ground and below-ground objects. The penetration depth of the L-band microwaves into the earth may vary depending on the type of soil, the amount of moisture in the soil, the structure of the land cover, etc. An exemplary penetration depth may be between the soil surface to a depth of 3 meters from a remote object located at least 50 meters above the soil surface. The L-band microwaves reflected back from the scanning area may be received and detected by the sensor. The sensor may discriminate between reflections having different polarizations. Sensors 150 and 152 may each be configured to detect reflections with different resolutions, for example, sensors may be configured to detect reflections with resolutions of 6m 3 and 12m 3 The image sensor 100 may be used to receive scanned images with a resolution of 100 MHz.

[0037] The L-band microwave or other RF waves may be transmitted with a first polarization, e.g., horizontal or vertical polarization, and the sensor may detect reflections having various modulations. For example, reflections from waves transmitted with horizontal polarization may be detected with vertical modulation (HV polarization) or horizontal modulation (HH polarization). Other polarizations may include vertical-vertical (VV) polarization, and vertical-horizontal (VH) polarization.

[0038] At operation 220, method embodiments may include receiving a second scanned image of the area at a second polarization. The second scanned image may include a second L-band microwave reflection from the same area. In some embodiments, if the first polarization is HV polarization, the second polarization may be HH polarization. In some embodiments, the second polarization may be VH polarization or VV polarization. Method embodiments may include receiving a third scanned image of the area at a second polarization (e.g., HH polarization), the third scanned image including a third L-band microwave reflection from the area at a higher resolution than that of the first and second scanned images. For example, if the first and second scanned images are at a resolution of 12 m, the third scanned image may include a third L-band microwave reflection from the area at a higher resolution than that of the first and second scanned images. 3 When the third scanned image is received from the first sensor at a resolution of 6 m, the third scanned image is received from the second sensor for detecting L-band microwave radiation reflections at a resolution of 6 m. 3 The second sensor may be mounted on an object (e.g., a satellite, aircraft, or balloon) located at least 50 meters, 100 meters, 1000 meters, or more above the region and calibrated similarly to the first sensor such that a pixel's gray level converted from the intensity levels of microwave reflections in the first and second scanned images received from a particular location within the region has a corresponding pixel (or pixels) gray level converted from the intensity levels of microwave reflections in the third scanned image received from that particular location. For example, the first and second scanned images may be received at a resolution of 12 m for all pixels in the first and second scanned images. 3 (or 13×6m 2 ), four corresponding pixels (or two corresponding pixels) may be received in the third scan image. Other numbers of scan images may be used.

[0039] The first, second, and optionally third scanned images may be received as a grayscale image with the microwave intensity levels converted to grayscale levels (e.g., each pixel in the map has a different gray level). 3Exemplary scanned images received at a resolution of 100 MHz are shown in FIG. 3A and FIG. 3B. FIG. 3A and FIG. 3B are exemplary scanned images taken over the city of Oakland, California, received from an L-band microwave sensor (e.g., SAR) located on a satellite. FIG. 3A is a scanned image with HV polarization, and FIG. 3B is a scanned image with HH polarization. In some embodiments, the method may include converting the first and second L-band microwave reflections from grayscale to intensity levels. As used herein, grayscale levels may be defined according to the ratio of black pigment or level to white pigment or level at each pixel. Gray levels may be correlated to microwave reflection intensity. The higher the black level or content, the higher the intensity of the microwave reflection from a particular area (e.g., pixel). For example, grayscale level data received from the sensor may be expressed, for example, as follows: (1) I dB =10 log(DN 2 )-83 can be converted to decibel (dB) intensity level using, where I dB is the transformed intensity level within each pixel, and DN is the grayscale level within each pixel. It should be understood that Equation 1 is provided by way of example only, and that it is within the scope of the present invention to use different equations to convert the gray levels to other intensity levels. Method embodiments may also include converting the third scanned image from grayscale to intensity levels.

[0040] An embodiment of the method may include receiving a fourth scanned image of the area at a third polarization, the fourth scanned image including a fourth L-band microwave reflection from the area. For example, the fourth scanned image may include a reflection having a VH polarization. An embodiment of the method may include receiving a fifth scanned image of the area at a fourth polarization, the fifth scanned image including a fifth L-band microwave reflection from the area. For example, the fourth scanned image may include a reflection having a VV polarization. The fourth and fifth scanned images are captured by a first sensor (e.g., a 6m 3 The received signal may be received from a sensor having a resolution of 0.1 μm to 100 μm.

[0041] In some embodiments, all received scanned images (eg, 1st through 5th) may be converted from grayscale to intensity levels, for example, using equation (1).

[0042] In operation 230, method embodiments may include filtering electromagnetic (EM) noise from the first scanned image using the second scanned image. Electromagnetic noise may include reflections reflected or bouncing off buildings, vegetation, or other topographical features located in the scanned area. There are methods known in the art for filtering EM noise from EM and RF signals, and the present invention is not limited to any particular method or algorithm. Some exemplary methods of filtering EM noise from each pixel according to embodiments of the present invention may include reducing noise from buildings, for example, using the following equation (other or different equations may be used, as well as other equations discussed herein): (2) Fd = 1 / 2(HH dB 2 -2·HV dB 2 ) where Fd is the electromagnetic noise from the bounce reflections from solid objects located within the scanning region, and HH dB is the intensity level of the HH polarized reflection at that pixel, and HV dB is the intensity level of the HV polarization at that pixel. In some embodiments, filtering electromagnetic noise may include filtering reflections received from solid objects located within the scan region. (3) C=(HH dB 2 ) / (2Fd) (4) Fv = 2 (1 / 2HH dB 2 -Fd·C 2 ) where Fv is the calculated electromagnetic reflected noise received from solid objects located within the scan region.

[0043] In some embodiments, reflections from additional polarizations (e.g., VV and VH polarizations) may be used to filter EM noise. For example, such reflections may be included in the expanded equation (2). Various parameters such as Fv and C calculated in equations (2)-(4) may be used to calculate a filtered first scanned image according to equation (5). (5) Bs=HH dB -(EM noise), where Bs is the filtered EM noise reflection.

[0044] An exemplary HH polarization scan image (e.g., a Bs scan image) after filtering electromagnetic noise according to some embodiments of the invention is shown in FIG. 4. As can be seen by comparing with the scan images of FIG. 3A and FIG. 3B, the filtered scan image is relatively homogenous without large noisy areas or portions. FIG. 4-FIG. 7 are grayscale representations of the intensity levels at each pixel in the scan image. FIG. 4-FIG. 7 were created by using the inverse formula of Equation (1) to reconvert the intensity levels used to calculate the various steps of the method from dB to grayscale.

[0045] In operation 240, the method embodiment may include creating a water composition map based on the typical salinity values ​​of various (e.g., a set of) types of water sources and the filtered first scanned image. In some embodiments, the typical salinity values ​​of various types of water sources may be stored in a database associated with the processor 112, for example in the storage unit 120. Different water sources, such as ocean water, lakes, rivers, swimming pools, sewer pipes, and drinking water pipes, have different typical reflections known and recorded in the art. This data can be used to create a water composition map that includes all undesirable water sources, for example, the map may include mapping all reflections associated with water sources other than drinking water (e.g., in urban water sources such as rivers, swimming pools, and sewer pipes). An exemplary process for creating a water composition map is shown in Equation (6). (6) Ks=aBs 2+bBs+c

[0046] where a is the average salinity of drinking water, b is the average salinity of outdoor freshwater sources (e.g., swimming pools, fountains, and lakes), and c is the average salinity of sewage. An exemplary water composition map is shown in Figure 5. Figure 5 is mostly dark, with the dark areas being places where no water is detected.

[0047] In some embodiments, the salinity of the water may be calculated based on the chemical composition of the water. The amount of chemicals, which may be solutes, in the water may affect the dielectric properties of the water. It is well known in the art that the amount of salinity may change the dielectric constant of the water, and at a given frequency, the higher the salinity, the higher the dielectric constant. Groundwater with different dielectric constants may have different waters with different microwave reflections at the same conditions. Some exemplary solutes, such as chlorine, calcium, and bicarbonate, may contribute to the salinity of the water. Drinking water in different regions of the globe has different salinity levels, for example, the amount of calcium in drinking water in Israel is much higher than the amount of calcium in drinking water in Germany. In Israel, rocks and soils contain large amounts of limestone, which contributes to the amount of calcium in the water. In some regions, there may be differences in the chemical composition of the water even between two neighboring cities due to fluoridation of the water or other manipulation of the drinking water performed, for example, by local authorities.

[0048] In some embodiments, when the composition of the water is calculated, for example using equation (6) above, the embodiment may include selecting the "a" parameter, the "b" parameter, and / or the "c" parameter of equation (6) based on the chemical composition of the water in the region. In some embodiments, selecting the "a" parameter may include selecting the parameters from a look-up table stored in a memory associated with the processor 112, for example in the storage unit 120. The look-up table may include a list of various "a", "b", and / or "c" parameters for waters having various chemical compositions. Additionally or alternatively, selecting the "a", "b" and / or "c" parameters may include modifying the "a", "b" and / or "c" parameters (e.g., multiplying them by a "salinity parameter"). The salinity parameter may be stored in a memory associated with the processor 112, for example in the storage unit 120.

[0049] In operation 250, an embodiment of the method may include identifying a first type of water source using the water composition map and the filtered first scanned image. Exemplary equations (7) and (8) may be used to calculate the value of the first water source. (7) Wc' = Bs Ks Ks (8) Wc=-d Wc' 2 -e·Wc'-f

[0050] where Wc is the calculated value of the first water source (e.g., drinking water) at each pixel in the scan region, d is a constant for urban areas, e is a constant for semi-urban areas, and f is a constant for non-urban areas. These constants may vary depending on the type of water source, the type of soil, precipitation (e.g., rain) in the region for a given time interval (e.g., a week) prior to the calculation, etc.

[0051] In some embodiments, Wc may be calculated using additional correction parameters based on at least one of the type of soil in the region, the density of the soil in the region, and the topography of the scanned region. In some embodiments, calculating Wc may include recommending a moisture level from the identified water source received from a database. The moisture level may be calculated based on at least one of the moisture characteristics of the soil in the region and the amount of precipitation (e.g., rain) in the region in a predetermined time interval (e.g., one week) prior to the calculation.

[0052] 6 is an exemplary map with identified water sources, showing water content in a geographical representation, according to some embodiments of the present invention. The detection resolution of drinking water is equal to the resolution of the first, second and optionally third scanned images, so that the scanned resolution (e.g., 3 m 2 , 6m 2 , 12m 2 The NI PXIe-8110 cannot detect drinking water or other water sources smaller than 100 mm (e.g., 100 mm diameter).

[0053] 7 is an example map (e.g., Wc map) with identified beverage leaks according to some embodiments of the present invention. Each small dot on the map has a different grayscale (e.g., different water content) and corresponds to a leak. Some leaks may be larger than the area covered by a single pixel and may include several pixels. Method embodiments may include summing or combining adjacent pixels identified as beverage leaks to define a single leak. The intensity level may be calculated, for example, in dB values ​​and converted to water retention.

[0054] In operation 260, method embodiments may include calculating water content at different locations in the region based on the identified first type of water source. In some embodiments, since every identified water source (e.g., leak) has its own intensity value, these values ​​may be used to calculate the water content associated with each water source. The higher the intensity level (e.g., the higher the Wc at that pixel or the sum of Wc in neighboring pixels), the higher the water content. Method embodiments may include converting the calculated water content for different region locations from reflected intensity level to water retention, e.g., in units of gallons per hour, cubic per hour, etc. Water retention may be proportional to intensity. Depending on the units used (e.g., gallons per hour, cubic per hour, etc.), different constants may be used to convert the intensity level to water retention. The calculated intensity level for each pixel may be multiplied by a known constant (e.g., different constants may be used for different water retention units) that converts the intensity level to water retention. Some embodiments may include summing the calculated water retention for neighboring pixels. The water retention calculated for several adjacent pixels, each corresponding to a location in the scanned area, may indicate that a large underground leak may be found at the corresponding location.

[0055] An embodiment of the method may include displaying the converted water retention on a graphical map of one or more scanned areas. The converted volume may be displayed on a city road map, a county road map, a satellite map, and the like. The converted water retention may be displayed on a screen 132 included in the user interface 130. An exemplary road map of downtown Oakland, California with locations of drinking water leaks is shown in FIG. 8. Because the received scanned image may include information (e.g., pixels) from a relatively large area, the map presenting the data to a user (e.g., city staff) may include only a portion of the scanned area. The user may move the map on the screen (e.g., using a mouse or keyboard) to cover all areas of interest (e.g., urban areas) within the scanned area. Some of the detected leaks, shown as small gray dots in FIG. 7, were given a water retention value and a corresponding location in the map (e.g., using coordinates). For example, as shown in FIG. 8, marks located at specific locations on the map represent different amounts of water leakage (e.g., in units of gallons per hour). Those skilled in the art should appreciate that the displayed information may be displayed on a Geographic Information System (GIS). It should be further appreciated that additional information may be displayed along with the water retention value and location information, such as water mains, water valves, etc. Such a representation may enable a better understanding of the cause of the leak and may facilitate real-time decision making.

[0056] See Figure 9, a flow chart of an exemplary method for remote detection of groundwater, according to some embodiments of the present invention. Method embodiments may be performed, for example, by system 100 or by other systems. In operation 910, method embodiments may include receiving a first scanned image of an area at a first polarization. Operation 910 may be substantially the same as operation 210 of the method shown in Figure 2, and may include the operations, steps, and formulas described above with respect to operation 210.

[0057] In operation 920, an embodiment of the method may include receiving optical data of or representative of at least a portion of the scanned area. The optical data may be captured at wavelengths from 1 millimeter to 10 nanometers (e.g., infrared to ultraviolet spectrum). The optical data may be received from at least one capture device or sensor (such as sensors 150 or 152) located either on the platform 155 or elsewhere. The capture device may include an infrared (IR) camera, a visible light camera, and / or an ultraviolet (UV) camera. The optical data may include satellite optical imagery, aerial photography, and the like. Exemplary optical data may include an IR image of the area captured by an IR camera, a visible light photograph of the area (e.g., an aerial photograph), or a UV scanned image of the area.

[0058] In operation 930, an embodiment of the method may include filtering electromagnetic noise from the first scanned image using the optical data. In some embodiments, the method may include comparing colors (e.g., wavelengths) or intensities of adjacent pixels in the optical data to detect differences or unexpected colors in the optical data. For example, IR radiation may vary due to temperature differences at various locations in the scanned area. Groundwater may cool the temperature of soil and land that is wetted by underground leakage compared to nearby soil and land. In some embodiments, detection of an area that is cooler than nearby areas may indicate the presence of groundwater. In yet another example, the presence of groundwater may affect the presence of vegetation and / or the color of the vegetation or soil in a particular area. For example, the presence of groundwater may result in significant lichen growth between the pavement and stones of a flagstone pavement, may result in the regrowth of some of the vegetation in substantially dry vegetation (e.g., in summer), may result in a change in color of the soil (e.g., darkening), and the like. These color changes, if detected, may indicate the presence of unmilled water. In some embodiments, the detected indication for the presence of groundwater may be used to filter EM noise from the first scanned image.

[0059] Some embodiments may include receiving a second scanned image of the area at a second polarization, the second scanned image including second L-band microwave reflections from the area, the second scanned image being from the first sensor as discussed with reference to operation 220 of the embodiment shown in Figure 2. In some embodiments, filtering EM noise from the first scanned image may further include using the second scanned image as discussed with respect to operation 230 of the embodiment of Figure 2.

[0060] 2 embodiment, and may include steps, operations, and formulas of operations 240-260. The embodiment of FIG. 9 may include any operations or steps that may be included in and disclosed with respect to the embodiment of FIG.

[0061] See Figure 10, a flow chart of an exemplary method for remotely detecting groundwater, according to some embodiments of the present invention. The method embodiment of Figure 10 may be performed, for example, by system 100 or by other systems. In operation 1010, the embodiment may include receiving a first scanned image of an area at a first polarization. Operation 1010 may be substantially the same as operation 210 of the embodiment of Figure 2, and may include the operations, steps, and formulas disclosed above with respect to operation 210.

[0062] In operation 1020, an embodiment may include receiving geographic data for the region from a database. In some embodiments, the geographic data may include land cover data for the region. Exemplary land covers may include types such as densely built-up areas, built-up areas, parks, agricultural areas, industrial areas, villages, and / or paved areas. In some embodiments, the land cover data may include classification of various portions within the scanned region into various land cover types, such as the land cover types listed above. A graphical representation of a scanned region classified into various land cover types is shown in FIG. 11. FIG. 11 is a map of a portion of a region presenting four land cover types at different locations on the map, according to one embodiment. The land cover may be classified as industrial areas at location A, as built-up areas at location B, as parks at location C, and as paved areas at location D. Other classifications may be used.

[0063] In some embodiments, the geographic data may include the location, length, width, and height of objects (e.g., buildings) within the scan area. For at least some of the buildings within the area, the geographic data may include the location and dimensions of the buildings.

[0064] In operation 1030, embodiments may include filtering electromagnetic noise from the first scanned image using the geographic data. In some embodiments, filtering the electromagnetic noise may include assigning filtering parameters to each portion of the region based on a land cover type of a classification of the portion of the region. The filtering parameters may relate to an amount of microwave scattering typical of each land cover type.

[0065] In some embodiments, filtering the electromagnetic noise may include calculating the size and location of blind spots near objects in the area that block microwave reflections from the blind spots from reaching the sensor. An exemplary calculation of blind spots near a building may be performed using exemplary Equation (9). (9) S = tan α × Hbl where S is the size of the blind spot area (m 2 where α is the off-nadir angle from the satellite to the ground and Hbl is the height of the building. A calculation done for a three-storey building results in 4m 2 12 is an illustration of calculated blind spot areas created by nearby buildings according to one embodiment. The areas in a square pattern around the dark object are the blind spot areas. These blind spot areas may be used to filter false readings, for example, if indications are given that there is a water leak under an area located in a blind spot area (shown as a circle), an embodiment may include concluding that these indications are false readings and should be ignored.

[0066] Some embodiments may include receiving a second scanned image of the area at a second polarization, the second scanned image including second L-band microwave reflections from the area, the second scanned image being from the first sensor as discussed with reference to operation 220 of Figure 2. In some embodiments, filtering EM noise from the first scanned image may further include using the second scanned image as discussed with respect to operation 230 of Figure 2.

[0067] 2 and may include the steps, operations, and formulas of operations 240-260. The embodiment of FIG. 10 may include any operations or steps that may be included in and disclosed with respect to the embodiments of FIG. 2 and / or FIG.

[0068] Reference is now made to FIG. 13, which is a flowchart of a method for determining infrastructure hazard zones, according to some embodiments of the present invention. The method of FIG. 13 may be performed, for example, by system 100 or by other systems. In operation 1310, a first scanned image of an area at a first polarization may be received from an RF emission sensor. In some embodiments, the first scanned image may include a first RF reflection from the area at a first resolution, the sensor being located at least 50 meters above the area. In some embodiments, the area may at least partially include an infrastructure, for example, a road 1450 shown in FIG. 14. In some embodiments, the scanned area may be selected to include at least a portion of the infrastructure of interest, for example, a portion of a highway, a portion of an industrial area, an entire shopping center, etc.

[0069] In some embodiments, operation 1310 may be substantially the same as operations 210, 910, and 1010 of Figures 2, 9, and 10, respectively. In operation 1320, additional data may be received. For example, the additional data may include receiving a second scanned image of the area at a second polarization, as discussed in detail with reference to step 220 of the method of Figure 2. In some embodiments, the additional data may further include receiving third and fourth scanned images from the area, as discussed in detail with reference to step 220 of the method of Figure 2.

[0070] In another example, the additional data may include optical data describing at least a portion of the scanned area, as discussed in detail with reference to step 920 of the method of Figure 9. In yet another example, the additional data may include geographic data regarding the area received from a database, as discussed in detail with reference to step 1020 of the method of Figure 10.

[0071] In operation 1330, electromagnetic noise may be filtered from the first scanned image using the additional data. For example, electromagnetic noise may be filtered according to operations 230, 930, and / or 1030 of the corresponding methods of FIGS.

[0072] In operation 1340, the location of the infrastructure in the area may be received from an external database, such as via the Internet, for example from a database associated with the system 100 (e.g., storage unit 120), or may be acquired by a sensor located at least 50 meters above the inspection area, such as an aerial photograph of the area. For example, the location may be included in a map depicting roads, rails, gas stations, etc. In some embodiments, the first scanned image may be selected to cover an area in which at least a portion of the infrastructure is located (e.g., a portion of a road, a portion of a rail, etc.). Examples of such areas are shown in Figures 14-17, including infrastructure facilities or systems (e.g., roads) 1450, 1550, 1555, 160, and 170.

[0073] In operation 1350, an inspection zone may be determined around the infrastructure. In some embodiments, the inspection zone may be determined as an area between two or more boundaries located at a certain distance from the infrastructure. For example, as shown in FIG. 16, a certain distance "d" (e.g., 20 meters) may be determined from either side of the road 160 to define the inspection zone 165.

[0074] In some embodiments, the method may further include receiving terrain data (e.g., a topographical map) of the region from an external database, digital elevation map, or the like, via the Internet, for example, from a database associated with the system 100 (e.g., storage unit 120). For example, a topographical map having a portion of infrastructure 1450 (in this case, a road) is shown in FIG. 14. In some embodiments, a watershed surrounding at least a portion of the infrastructure may be identified (e.g., a boundary of a basin surrounding the infrastructure). As used herein, a "watershed" (also known in the art as a water divide, division, ridge, watershed, ridge, highland) is a raised topographical feature that separates adjacent drainage basins. Alternatively, in rugged terrain, the division may lie along a topographical rise and be in the form of a single chain of hills or mountains, known as a watershed mountain range.

[0075] For example, a watershed 1400 surrounding road 1450 may be identified within the region of Figure 14. To identify the watershed, the slope within the region may be mapped. Figure 15 is an illustration of roads 1500 and 1510 mapped near roads 1550 and 1555. In some embodiments, the inspection area may be a watershed.

[0076] In some embodiments, the inspection area may be dynamically determined based on real-time information regarding changes in the determined basin size around the infrastructure, as discussed below with reference to Equation 11. The slope (e.g., location and / or grade) may be a dynamic value that may change depending on soil parameters. For example, the strength of a rock mass may depend on the type of rock, as well as the presence and discontinuities of joints and other cracks, etc. The more discontinuities present in the bedrock, the higher the chance of rock instability. Rock type may affect landslides due to differences in the strength of surface material between two different types of rock. For example, soils derived from schist or shale may contain a high percentage of clay. These soils may have lower strength properties than coarse-grained soils, such as those derived from granite bedrock. Thus, soils containing more clay may require a larger inspection area to be determined than is necessary for sandy soils.

[0077] In operation 1360, the amount of clay in the soil contained in the inspection area may be estimated from the filtered first scanned image. For example, the filtered RF reflections (Bs) associated with locations (coordinates) inside the inspection area may be used to estimate the amount of clay in the soil, for example, using equation (10). (10)

number

number

[0078] Equation 11 may allow the test area (e.g., basin size) to be determined according to the required soil / land stability defined by the required slope. In some embodiments, the size of the test area (e.g., Km 2 Units) can be determined such that for a given clay volume and a given average slope, the calculated slope can be higher than the predetermined slope.

[0079] In operation 1370, soil moisture content at locations within the inspection area may be calculated from the filtered first scanned image. The soil moisture content may be calculated using any method known in the art. In some embodiments, the soil moisture content may be calculated using the methods disclosed in operations 240-260 of FIG.

[0080] In operation 1380, a soil moisture threshold may be determined. For example, the threshold may include a soil moisture content of greater than 30% (determined by gravimetric analysis). In some embodiments, the threshold may be determined based on an estimated clay volume. For example, for each estimated volume of clay, a corresponding pre-defined threshold of soil moisture content may be determined (e.g., using a look-up table based on experimental and / or simulation data). In some embodiments, the correlation between the volume of clay (which may be expressed as a percentage) and the distance "d" of the boundary of the inspection area from the infrastructure may be a linear correlation or any other polynomial correlation. In some embodiments, the calculated linear correlation may be a starting point for forming a look-up table, which may be updated and refined using newly collected experimental and / or actual data.

[0081] In some embodiments, the predetermined soil moisture content threshold may depend on both the slope and chemical composition of the soil, as shown in Equation 12. (12)

number

[0082] where SMT is the soil moisture threshold, %NonClay is the average amount in % of other material in the soil, and percent slope.

[0083] In operation 1390, at least one hazardous location may be determined to have a soil moisture content above a predetermined threshold.

[0084] In some embodiments, at least one hazard location may be displayed to the user (e.g., graphically). Examples of such presentations are shown in Figures 16 and 17. Figure 16 is a map showing hazard locations 168A and 168B within an inspection area 165 near a road 160. As shown in Figure 16, the landscape through which the road 160 runs is generally flat, and therefore the inspection area 165 may be determined using a fixed distance "d" from either side of the road 160.

[0085] 17 is a map showing hazardous locations 175A and 175B near a road 170 that runs along a basin (and thus may be subject to slope). Thus, the watershed may be used as a test area to identify hazardous locations 175A and 175B.

[0086] Other characteristics of the water, such as the chemistry of the water, may further affect buildings and infrastructure. For example, the amount of salt in the water (e.g., salinity level) may affect the harmful properties of the water with respect to materials / buildings contained in the infrastructure. For example, high salinity levels may cause rapid corrosion of steel rebars contained in reinforced concrete. Thus, determining the type of water in the soil, for example, using the method disclosed in operations 240 and 250 of the method of FIG. 2, may allow for further estimation of the risk of corrosion of the infrastructure.

[0087] While certain features of the invention have been illustrated and described herein, many modifications, substitutions, changes, and equivalents will occur to those skilled in the art. It is therefore to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.

Claims

1. 1. A method for determining infrastructure hazardous areas, comprising: receiving a first scanned image of an area at a first polarization from a radio frequency radiation sensor, the first scanned image including first radio frequency reflections from the area at a first resolution, the sensor being located at least 50 meters above the area, the area at least partially comprising infrastructure; receiving additional data; filtering electromagnetic noise from the first scanned image using the additional data; receiving a location of infrastructure within the area; determining an inspection area around said infrastructure; estimating the amount of clay in soil contained in the inspection area from the filtered first scanned image; calculating soil moisture content at locations within the inspection area from the filtered first scanned image; determining at least one hazardous location having a soil moisture content above a predetermined threshold, the threshold being determined based on the estimated clay content; The method includes:

2. receiving terrain data for the area; identifying a watershed surrounding at least a portion of the infrastructure, the inspection area being the watershed; The method of claim 1 further comprising:

3. 3. The method of claim 1 or 2, wherein the additional data includes at least a second scanned image of the area at a second polarization, the second scanned image including second high frequency reflections from the area at a second resolution, the second scanned image being from the high frequency radiation sensor used to acquire the first scanned image.

4. The method of claim 1 , further comprising converting the first and second high frequency reflections from a greyscale level to an intensity level.

5. The method of claim 3 , wherein the first polarization is a horizontal polarization and the second polarization is a vertical polarization.

6. receiving a third scanned image of the area in the second polarization, the third scanned image including a third reflection from the area at a higher resolution than the first and second scanned images; receiving a fourth scanned image of the area in a third polarization, the fourth scanned image including a fourth reflection from the area; Further comprising: the fourth and third scanned images are from the radio frequency radiation sensor used to acquire the first scanned image; said filtering electromagnetic noise from the first scanned image includes using the third and fourth scanned images to filter said noise; The method according to claim 3.

7. The step of filtering electromagnetic noise further comprises: filtering electromagnetic reflection noise received from solid objects located within the scanning area; and filtering electromagnetic bounce noise from solid objects located within the scan area. The method according to claim 1 , comprising at least one of the following:

8. 8. The method of claim 1, wherein the additional data includes optical data of at least a portion of the scanned area, and wherein the step of identifying a first type of water includes analyzing the optical data to detect a change in color of the portion of the area.

9. The method of claim 1 , wherein the additional data comprises geographical data for the region received from a database, the geographical data comprising land cover data for the region.

10. 10. The method of claim 9, wherein the land cover data for the area includes a classification of portions of the area into different land cover types, and wherein filtering electromagnetic noise includes assigning filtering parameters to each portion of the area based on the classification of the portions of the area into the land cover types.

11. 10. The method of claim 9, wherein the geographic data includes locations and dimensions of buildings within the region, and wherein the step of filtering electromagnetic noise includes calculating the size and location of blind spot regions near objects within the region, the objects blocking high frequency reflections from the blind spot regions from reaching the sensor.

12. 1. A system for determining subsurface water content, comprising: A processor; A non-transitory computer-readable medium, comprising: When executed by the processor, the processor: receiving a first scanned image of an area at a first polarization from a high frequency radiation sensor, the first scanned image including a first high frequency reflection from the area, the sensor being attached to an object located at least 50 meters above the area; Receive additional data, filtering electromagnetic noise from the first scanned image using the at least one additional data; receiving a location of infrastructure within said region; determining an inspection area around said infrastructure; estimating the amount of clay in the soil contained in the inspection area from the filtered first scanned image; calculating soil moisture content at locations within the inspection area from the filtered first scanned image; determining hazardous locations having soil moisture content above a predetermined threshold, said threshold being determined based on said estimated clay content; A computer-executable instruction storage device, A non-transitory computer-readable medium; A system comprising:

13. The processor, receiving topographical data for the area; identifying a watershed surrounding at least a portion of the infrastructure, the inspection area being the watershed; The system of claim 12 further configured to:

Images