System and method for analysing and visualizing geospatial data

Abstract

A system (106) and method (900) for processing and visualizing classified spatial data is disclosed. The system (106) is configured to receive classified spatial data from various input data sources (114). Further, visual identifiers are assigned to classes within the classified spatial data and image tiles are generated at multiple levels of details from the classified spatial data using the assigned visual identifiers. The system (106) is further configured to store the generated image tiles in a database (124) and render a set of image tiles from the database (124) on a user interface (200) based on a current viewport of a map.

Description

SYSTEM AND METHOD FOR ANALYSING AND VISUALIZING GEOSPATIAL DATARESERVATION OF RIGHTS

[0001] A portion of the disclosure of this patent document contains material which is subject to intellectual property rights such as, but are not limited to, copyright, design, trademark, integrated circuit (IC) layout design, and / or trade dress protection, belonging to Jio Platforms Limited ( JPL) or its affiliates (herein after referred as owner). The owner has no objection to the facsimile reproduction by anyone of the patent document or the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all rights whatsoever. All rights to such intellectual property are fully reserved by the owner.TECHNICAL FIELD

[0002] The present disclosure relates to geospatial analytics and data management, and specifically to a system and a method for processing and visualizing classified spatial data.DEFINITION

[0003] As used in the present disclosure, the following terms are generally intended to have the meaning as set forth below, except to the extent that the context in which they are used to indicate otherwise.

[0004] The term “Classified Spatial Data” as used herein, refers to geospatial data that are classified into different classes based on certain attributes. The classes in the classified spatial data represent distinct categories that help in analysis, visualization and interpretation of geospatial data.

[0005] The term “Classified Raster Data” as used herein, refers to geographic information in a grid format where each cell (or pixel) has a value representing a specific class. The class represents different categorical data such as, but not limited to, different coverage areas, different land uses, and so forth. In otherwords, the classified raster data is arranged in a grid of rows and columns with each cell representing a specific area on a ground.

[0006] The term “Polygon Based Classified Data” as used herein, refers to a type of geospatial data that represents spatial information using polygons. Each polygon represents specific area with attributes that classify and describe the spatial data.

[0007] The term “Color Index Table” as used herein, refers to a data structure used in geospatial applications to map different classes (class identifies) to Red Green Blue (RGB) color values.

[0008] The term “Image Tiles” as used herein, refers to small and rectangular images that represent a portion of a larger map or raster data at various zoom levels.

[0009] The term “Current View Port” as used herein, refers to a specific area of a map that is visible to a user on a screen at any given time. A viewport is defined by a current zoom level and geographic coordinates of map’s center and comers.

[0010] The term “Flat Files” as used herein, refers to simple text files that contain data in a tabular format with each line representing a record and each field separated by a delimiter.

[0011] The term “structured row key format” as used herein, refers to a structure and organization of a primary key used to uniquely identify rows in a database.BACKGROUND

[0012] The following description of related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of thepresent disclosure. However, it should be appreciated that this section be used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of prior art.

[0013] Wireless communication technology has rapidly evolved over past few decades. First generation of the wireless communication technology was analog technology that offered only voice services. Further, when a second generation (2G) technology was introduced, text messaging and data services became possible. Third Generation (3G) technology marked an introduction of high-speed internet access, mobile video calling, and location-based services. Fourth generation (4G) technology revolutionized wireless communication with faster data speeds, improved network coverage, and security. Currently, fifth generation (5G) technology is being deployed, with even faster data speeds, low latency, and an ability to connect multiple devices simultaneously.

[0014] As wireless technologies are advancing, there is a need to cope up with 5G requirements and deliver a high level of service to customers. Further, with an advancement of the wireless technologies, a size of geospatial dataset has increased substantially over the years, and this growth has outstripped a capacity of a computing power to manage geospatial data. The geospatial data can be various types of data associated with both time and location. The geospatial data includes a variety of datasets, for example, satellite data, weather reports and forecasts, social media posts that are geotagged, normalized difference vegetation indices, maps, drone data, Internet of Things (loT) data, survey data, census data, and other datasets. There are a wide variety of types and sources of the geospatial data; this data can be diverse in terms of spatial and temporal resolution, type (e.g., raster or vector), and dimensions.

[0015] In any web-based or desktop-based tool with geospatial analytics capability, a variety of geospatial datasets is used for analytics. The geospatial data could be point data, line data, polygon (or vectors) data, and raster data. The raster data could be of two types: either numeric raster data or classified grid raster data.In the numeric raster data, every coordinate has some different numeric value. In the classified grid raster data, there are a fixed number of classes, and every coordinate is associated with one of these classes.

[0016] In a telecom domain, one of the most popular examples of the classified raster grid data is a Best Server Plot (BSP). The BSP provides information on best serving cells for every location on a map depending on raster resolution. Generally, such kind of data is stored in various flat file formats.

[0017] One common source of a BSP file is a simulation tool. However, such file storage is not optimal if it is of big data scale (say nationwide), and further, such files are flat which can’t be rendered directly over a web-based platform. Further, using a spatial database for storing every coordinate of polygon along with classified grid value does not provide an optimal solution for web rendering.

[0018] Thus, there is a need to design storage formats that help computers access, visualize, and process the data in an efficient and timely fashion.OBJECTS OF THE PRESENT DISCLOSURE

[0019] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are as listed herein below.

[0020] An object of the present disclosure is to provide a system and a method for storing classified grid data for web rendering and spatial analytics.

[0021] Another object of the present disclosure is to provide a visualization of big data scale classified map data.

[0022] Another object of the present disclosure is to provide a storage for all kinds of classified spatial data (i.e. classified raster data and polygon based classified data) for web rendering.

[0023] Another object of the present disclosure is to provide a system and a method for storing processed classified spatial data in tiles format for every zoom level for web visualization.

[0024] Another object of the present disclosure is to provide a system and a method for maintaining a definition of pixels or mapping of color to an actual class (in tiles) in a color index table.

[0025] Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.SUMMARY

[0026] In an exemplary embodiment, the present invention discloses a method for processing and visualizing classified spatial data. The method includes a step of receiving, by a receiving unit, the classified spatial data from a plurality of input data sources. The method includes a step of assigning, by a processing unit, visual identifiers to one or more classes within the classified spatial data. The method includes a step of generating, by the processing unit, one or more image tiles at multiple levels of details from the classified spatial data using the assigned visual identifiers. The method includes a step of storing, by the processing unit, the one or more generated image tiles in a database. The method includes a step of rendering, by the processing unit, a set of image tiles from the one or more image tiles stored in the database on a user interface based on a current viewport of a map.

[0027] In some embodiments, the classified spatial data is selected from one of, classified raster data or polygon based classified data.

[0028] In some embodiments, the method includes retrieving, by the processing unit, a color index table from the database. The color index table includes a predefined mapping of the one or more classes to the corresponding visual identifiers. The method further includes determining, by the processing unit,whether each class in the received classified spatial data is an existing class or a new class based on the color index table. The method further includes retrieving, by the processing unit, previously mapped visual identifiers from the color index table when the classified spatial data includes the existing class. The method further includes generating and assigning, by the processing unit, new visual identifiers when the classified spatial data includes the new class.

[0029] In some embodiments, the method includes identifying, by the processing unit, the current viewport of the map displayed on the user interface based on a user interaction with the map. The method further includes calculating, by the processing unit, a geographic extent of the current viewport of the map. The method further includes identifying, by the processing unit, the one or more image tiles corresponding to the geographic extent at a zoom level of the current viewport from the database. The method further includes retrieving, by the processing unit, the identified one or more image tiles from the database for rendering the one or more image tiles on the user interface.

[0030] In some embodiments, the method includes a step of storing a mapping of the one or more classes to the corresponding visual identifiers in a color index table.

[0031] In some embodiments, the method includes a step of fetching class information corresponding to at least one of the visual identifiers on the user interface from a color index table in response to a user interaction with the map.

[0032] In some embodiments, fetching the class information includes decoding the class information from reverse mappings of the visual identifiers to the one or more classes stored in the color index table.

[0033] In another exemplary embodiment, the present invention discloses a system for processing and visualizing classified spatial data. The system includes a receiving unit configured to receive the classified spatial data from a plurality ofinput data sources. The system further includes a processing unit communicatively coupled to the receiving unit. The processing unit is configured to: assign visual identifiers to one or more classes within the classified spatial data. The processing unit is configured to: generate one or more image tiles at multiple levels of details from the classified spatial data using the assigned visual identifiers. The processing unit is further configured to store the one or more generated image tiles in a database. The processing unit is further configured to render a set of image tiles from the one or more image tiles stored in the database on a user interface based on a current viewport of a map.

[0034] In some embodiments, the classified spatial data is selected from one of, classified raster data or polygon based classified data.

[0035] In some embodiments, the processing unit is configured to: retrieve a color index table from the database. The color index table includes a predefined mapping of the one or more classes to the corresponding visual identifiers. The processing unit is further configured to determine whether each class in the received classified spatial data is an existing class or a new class based on the color index table . The processing unit is further configured to retrieve previously mapped visual identifiers from the color index table when the classified spatial data comprises the existing class. The processing unit is further configured to generate and assign new visual identifiers when the classified spatial data comprises the new class for assigning the visual identifiers to the one or more classes within the classified spatial data.

[0036] In some embodiments, the processing unit is configured to identify the current viewport of the map displayed on the user interface based on a user interaction with the map. The processing unit is further configured to calculate a geographic extent of the current viewport of the map. The processing unit is further configured to identify the one or more image tiles corresponding to the geographic extent at a zoom level of the current viewport from the database. The processingunit is further configured to retrieve the identified one or more image tiles from the database for rendering the one or more image tiles on the user interface.

[0037] In some embodiments, the processing unit is configured to store a mapping of the one or more classes to the corresponding visual identifiers in a color index table.

[0038] In some embodiments, the processing unit is configured to fetch class information corresponding to at least one of the visual identifiers on the user interface from a color index table in response to a user interaction with the map.

[0039] In some embodiments, the processing unit is configured to fetch the class information by decoding the class information from reverse mappings of the visual identifiers to the one or more classes stored in the color index table.

[0040] In an exemplary embodiment, the present invention discloses a User Equipment (UE) configured for rendering one or more image tiles on a user interface. The UE includes: a main processor. The UE further includes a computer readable storage medium storing one or more instructions for execution by the main processor to receive a request for map data corresponding to a current viewport of a map displayed on the user interface; determine a set of image tiles from the one or more image tiles corresponding to the current viewport based on a zoom level of the map; retrieve the determined set of image tiles from a database; and render the retrieved set of image tiles on the user interface based on the current viewport of the map.

[0041] In an exemplary embodiment, the present invention discloses a computer program product including a memory having instructions that, when executed by a processing unit, cause the processing unit to perform a method for analyzing and visualizing geospatial data. The method includes a step of receiving, by a receiving unit, the classified spatial data from a plurality of input data sources. The method includes a step of assigning, by a processing unit, visual identifiers toone or more classes within the classified spatial data. The method includes a step of generating, by the processing unit, one or more image tiles at multiple levels of details from the classified spatial data using the assigned visual identifiers. The method includes a step of storing, by the processing unit, the one or more generated image tiles in a database. The method includes a step of rendering, by the processing unit, a set of image tiles from the one or more image tiles stored in the database on a user interface based on a current viewport of a map.BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In the figures, similar components and / or features may have the same reference label. Further, various components of the same type may be distinguished by following the reference label with a second label that distinguishes among the similar components. If only the first reference label is used in the specification, the description is applicable to any one of the similar components having the same first reference label irrespective of the second reference label.

[0043] The diagrams are for illustration only, which thus is not a limitation of the present disclosure, and wherein:

[0044] FIG. 1A illustrates an exemplary network architecture in which or with which embodiments of the present disclosure may be implemented.

[0045] FIG. IB illustrates an exemplary block diagram of a system depicting an interaction between components, in accordance with an embodiment of the disclosure.

[0046] FIG. 1C illustrates an exemplary block diagram depicting modules of the system, in accordance with an embodiment of the present disclosure.

[0047] FIG. 2 illustrates an exemplary user interface for visualization of classified raster data, in accordance with an embodiment of the present disclosure.

[0048] FIG. 3 illustrates an exemplary visualization of the classified raster data, in accordance with an embodiment of the present disclosure.

[0049] FIG. 4 illustrates an exemplary visualization of classified spatial data with polygon details for every class, in accordance with an embodiment of the present disclosure.

[0050] FIG. 5 illustrates an exemplary visualization of the classified spatial data with the polygon details stored in a spatial object of a spatial database, in accordance with an embodiment of the present disclosure.

[0051] FIG. 6 illustrates an exemplary color index table, in accordance with an embodiment of the present disclosure.

[0052] FIG. 7 illustrates an exemplary visualization of a Best Plot Server (BPS), in accordance with an embodiment of the present disclosure.

[0053] FIG. 8 illustrates an exemplary computer system in which or with which embodiments of the present disclosure may be implemented.

[0054] FIG. 9 illustrates a flowchart of a method for analyzing and visualizing geospatial data, in accordance with an embodiment of present disclosure.LIST OF REFERENCE NUMERALS100 - Network architecture102-1, 102-2... 102-N - User Equipment104-1, 104-2... 104-N - Users106 - System108 -Network110-1, 110-2... 110-N - Main Processors112-1, 112-2. . . 112-N - Computer Readable Storage Mediums114 - Input Data Source116 - Preprocessing Unit118 - Visual Identifier Generator120 - Color Index Table122 - Image Generator124 - Database126 - Web Portal128a - Flat Files Type A128b - Flat Files Type B130 - Spatial Database132 - Receiving Unit134 - Memory136 - Interfacing Unit138 - Processing Unit140 - Assignment Module142 - Tile Generation Module144 - Display Module200 - User Interface202 - Prediction Uayers204 - On Air300 - Visualization of Classified Raster Data302 - Metadata304 - Encoded Class Information400 - Visualization of Classified Spatial Data with Polygon Details402 - Class Name404 - Polygon Vertices500 - Visualization of Classified Spatial Data with Polygon Details Stored in a Spatial Object502 - Class Identification (ID)504 - Shape600 - SitelD Column602 - Class Number (CNUM) Column604 - Red Column606 - Blue Column608 - Green Column610 - Band Column612 - Creation Date Column700 - Visualization of a Best Server Plot (BPS)702 - Cell Identifier A704 - Cell Identifier B706 - Cell Identifier C708 - Cell Identifier D800 - Computer system810 - External storage device820 - Bus830 - Main memory840 - Read only memory850 - Mass storage device860 - Communication port(s)870 - Processor900 - MethodDETAILED DESCRIPTION

[0055] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used individually of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.

[0056] The ensuing description provides exemplary embodiments only, and is not intended to limit the scope, applicability, or configuration of the disclosure. Rather, the ensuing description of the exemplary embodiments will provide those skilled in the art with an enabling description for implementing an exemplary embodiment. It should be understood that various changes may be made in the function and arrangement of elements without departing from the spirit and scope of the disclosure as set forth.

[0057] Specific details are given in the following description to provide a thorough understanding of the embodiments. However, it will be understood by one of ordinary skill in the art that the embodiments may be practiced without these specific details. For example, circuits, systems, networks, processes, and other components may be shown as components in block diagram form in order not to obscure the embodiments in unnecessary detail. In other instances, well-known circuits, processes, algorithms, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.

[0058] Also, it is noted that individual embodiments may be described as a process which is depicted as a flowchart, a flow diagram, a data flow diagram, a structure diagram, or a block diagram. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel or concurrently. In addition, the order of the operations may be re-arranged. A process is terminated when its operations are completed but could have additional steps not included in a figure. A process may correspond to a method, a function, a procedure, a subroutine, a subprogram, etc. When a process corresponds to a function, its termination can correspond to a return of the function to the calling function or the main function.

[0059] The word “exemplary” and / or “demonstrative” is used herein to mean serving as an example, instance, or illustration. For the avoidance of doubt, the subject matter disclosed herein is not limited by such examples. In addition, anyaspect or design described herein as “exemplary” and / or “demonstrative” is not necessarily to be construed as preferred or advantageous over other aspects or designs, nor is it meant to preclude equivalent exemplary structures and techniques known to those of ordinary skill in the art. Furthermore, to the extent that the terms “includes,” “has,” “contains,” and other similar words are used in either the detailed description or the claims, such terms are intended to be inclusive — in a manner similar to the term “comprising” as an open transition word — without precluding any additional or other elements.

[0060] Reference throughout this specification to “one embodiment” or “an embodiment” or “an instance” or “one instance” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” or “in some embodiments” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0061] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. As used herein, the term “and / or” includes any and all combinations of one or more of the associated listed items.

[0062] In a telecom domain, one of a most popular example of classified raster grid data is a Best Server Portal (BSP). The BSP provides information of bestserving cells for every location on a map depending on a raster resolution. Generally, such kind of data is stored in a flat file format. Generally such kind of data is stored in flat file format such as .shp, .tab, .grc, .grd etc. One common source of BSP file is from simulation tool.

[0063] However, such file storage is not optimal if it is of big data scale (say nationwide), and further, such flat files cannot be rendered directly over a webbased platform. Further, using a spatial database for storing every coordinate along with classified grid value does not provide an optimal solution for web rendering.

[0064] The present disclosure aims to overcome challenges in visualization of big data scale classified map data due to a large number of polygons. For example, a payload as shown below cannot be transferred over the web:

[0065] Also, the present disclosure aims to overcome the above-mentioned and other existing problems in this field of technology by providing an improved system and method for storing classified spatial data for efficient web rendering and spatial analytics.

[0066] Various embodiments of the present disclosure will be explained in detail with reference to FIGs. 1 to 9.

[0067] FIG. 1A illustrates an exemplary network architecture (100) in which or with which embodiments of the present disclosure may be implemented.

[0068] Referring to the FIG. 1 A, the network architecture ( 100) may include one or more computing devices or one or more User Equipment (UE) (102-1, 102-2... 102-N) that may be associated with one or more users (104-1, 104-2... 104-N) and a system (106) in an environment. In an embodiment, the one or more UE (102- 1, 102-2... 102-N) may be communicated to the system (106) through a network (108). A person of ordinary skill in the art will understand that the one or more UE (102-1, 102-2... 102-N) may be individually referred to as the UE (102) and collectively referred to as the UE (102). A person of ordinary skill in the art will appreciate that the terms “computing device(s)” and “UE” may be used interchangeably throughout the disclosure. Although three UE (102) are depicted in the FIG. 1A, however any number of the UE (102) may be included without departing from the scope of the ongoing description. Similarly, a person of ordinary skill in the art will understand that the one or more users (104-1, 104-2... 104-N) may be individually referred to as the user (104) and collectively referred to as the users (104).

[0069] In an embodiment, the UE (102) may include smart devices operating in a smart environment, for example, an Internet of Things (loT) system. In such embodiment, the UE (102) may include, but not limited to, smart phones, smart watches, smart sensors (e.g., mechanical, thermal, electrical, magnetic, etc.), networked appliances, networked peripheral devices, networked lighting system, communication devices, networked vehicle accessories, networked vehicular devices, smart accessories, tablets, Smart Television (TV), computers, a smart security system, a smart home system, other devices for monitoring or interacting with or for the users (104) and / or entities, or any combination thereof. A person of ordinary skill in the art will appreciate that the UE (102) may include, but not limited to, an intelligent multi-sensing, network-connected devices, that can integrate seamlessly with each other and / or with a central server or a cloudcomputing system or any other device that is network-connected.

[0070] In an embodiment, the UE (102) may include, but not limited to, a handheld wireless communication device (e.g., a mobile phone, a smart phone, a phablet device, and so on), a wearable computer device (e.g., a head-mounteddisplay computer device, a head-mounted camera device, a wristwatch computer device, and so on), a Global Positioning System (GPS) device, a laptop, a tablet computer, or another type of portable computer, a media playing device, a portable gaming system, and / or any other type of computer device with wireless communication capabilities, and the like.

[0071] In an embodiment, the UE (102) may include, but is not limited to, any electrical, electronic, electro-mechanical, or an equipment, or a combination of one or more of the above devices such as Virtual Reality (VR) devices, Augmented Reality (AR) devices, a general-purpose computer, a desktop, a personal digital assistant, a mainframe computer, or any other computing device. In another embodiment, the UE (102) may include one or more in-built or externally coupled accessories including, but not limited to, a visual aid device such as a camera, an audio aid, a microphone, a keyboard, and input devices for receiving input from the user (104) or the entity such as a touch pad, a touch enabled screen, an electronic pen, and the like. A person of ordinary skill in the art will appreciate that the UE (102) may not be restricted to the mentioned devices and various other devices may be used.

[0072] Further, each ofthe UEs (102) may include main processors (110-1, 110-2... 110-N) (hereinafter collectively referred to as the main processors (110) and individually referred to as the main processor (110)) that refers to any logic circuitry for processing instructions. The main processor (110) may be, but not limited to, a conventional processor, a Digital Signal Processor (DSP), a plurality of microprocessors, one or more microprocessors in association with the DSP, a controller, a microcontroller, Application Specific Integrated Circuits (ASIC), Field Gate Programmable Array (FGPA) circuits, any other type of integrated circuits, and so forth. More specifically, the main processor (110) is a hardware processor. The main processor (110) may perform a signal coding data processing, an input / output processing, and / or any other functionality that enables a working of the system (106), according to the present disclosure.

[0073] Further, each of the UEs (102) may include computer readable storage mediums (112-1, 112-2. . . 112-N) (hereinafter collectively referred to as the computer readable storage mediums (112) and individually referred to as the computer readable storage medium (112) to store the instructions. As used herein, the term “instructions” may refer to a sequence of commands that are written in a programming language and may be executed by the main processor (110) to perform tasks associated with the UE (102). In an exemplary embodiment, the computer readable storage mediums (112) may be, but not limited to, Hard Disk Drives (HDDs), Solid State Drives (SSD), a flash memory, a Random Access Memory (RAM), and so forth.

[0074] As portable electronic devices and wireless technologies continue to improve and grow in popularity, advancing wireless technologies for data transfer are also expected to evolve and replace older generations of technologies. In a field of wireless data communications, a dynamic advancement of various generations of a cellular technology are also seen. Development, in this respect, has been incremental in an order of a Second Generation (2G), a Third Generation (3G), a Fourth Generation (4G), and now Fifth Generation (5G), and more such generations are expected to continue in the forthcoming time.

[0075] In an embodiment, the UE (102) may utilize a Radio Access Technology (RAT) to connect to a cellular network. It refers to specific protocols and standards that govern the way devices communicate with base stations, which are responsible for providing a wireless connection. Further, each RAT has its own set of protocols and standards for communication, which define frequency bands, modulation techniques, and other parameters used for transmitting and receiving data. Examples of the RATs include a Global System for Mobile Communications (GSM), a Code Division Multiple Access (CDMA), a Universal Mobile Telecommunications System (UMTS), a Long-Term Evolution (LTE), and 5G. A choice of the RAT depends on a variety of factors, including a network infrastructure, available spectrum, and capabilities of the UE (102). The UEs (102)often support multiple RATs, allowing them to connect to different types of networks and provide optimal performance based on the available network resources.

[0076] Referring to the FIG. 1A, the UE (102) may communicate with the system (106) via a set of executable instructions residing on any operating system. The system (106) may be for example, the system (106) for processing and visualizing classified spatial data. As used herein, the term “classified spatial data” may refer to geospatial data that may be classified into different classes based on certain attributes. In an exemplary embodiment, the classified spatial data may be, but not limited to, classified raster data, polygon based classified data, and so forth. In an embodiment, the classified raster data may represent geographic information in a grid format where each cell has a value corresponding to a specific class. In such embodiment, the class may represent different categorical data such as, but not limited to, different coverage areas, different land uses, and so forth. In an exemplary embodiment, the classified raster data may be initially stored in a classified flat file format (128a) (as shown in FIG. IB) such as, but not limited to, Map Raster (.MRR) files, Grid Class (.GRC) files, Grid (.GRD) files, and so forth.

[0077] As used herein, the term “.MRR files” may refer to file formats used by a Geographic Information System (GIS) software to store raster data. Further, as used herein, the term “.GRC files” may refer to file formats used to store the raster data where each grid cell is assigned a class value. Also, as used herein, the term “.GRD files” may refer to grid formats that store various types of the raster data including the classified raster data.

[0078] In an exemplary embodiment, the polygon based classified data may represent geographic areas as polygons with each polygon corresponding to the specific class. In an aspect, the polygon based classified data may be stored in a classified flat file format (128b) (as shown in the FIG. IB) such as, but not limited to, Shapefile (.SHP), TAB files, and so forth. As used herein, the term “.SHP” refers to a format for storing geometric data such as, point data, line data, polygon data,and so forth. Similarly, as used herein, the term “TAB files” refers to a file format for storing the geometric data along with attribute information.

[0079] In another embodiment, the classified spatial data may initially be stored in a spatial database (130) (as shown in the FIG. IB) that may allow for querying, updating and managing large datasets. In an exemplary embodiment, the spatial database (130) may be, but not limited to, a relational database, a nonrelational database, and so forth. As used herein, the term “relational database” may be, for example, PostgreSQL where the geometric data is stored in spatial tables. Each table row may represent the polygon with class details. Further, as used herein, the term “non-relational database” may be, for example, MongoDB where spatial objects may be stored as documents with embedded geometric data and the attributes.

[0080] In an embodiment, the system (106) may be configured to receive the classified spatial data from various input data sources (114) (As shown in FIG. IB). In an exemplary embodiment, the input data sources (114) may be, but not limited to, simulation tools (e.g. radio network planning software which simulate network coverage and performance for telecom operators), telecommunication equipment vendors (e.g., data from vendors includes ), GIS platforms (e.g., Google Earth Engine that provides geospatial data layers like population density, terrain and so forth), regulatory agencies (e.g., data from Federal Communication Commission (FCC) or Telecom Regulatory Authority of India (TRAI) on spectrum allocations or coverage compliance), measurement campaigns (e.g., drive test tools that provide data on signal strength, latency or drop rates), remote sensors (e.g., satellite imagery from Landsat for environmental monitoring), and so forth. In an embodiment, the classified spatial data may initially be stored in one of, the classified flat files (128a-128b) or the spatial database (130) before receiving from the input data sources (114). Further, the system (106) may be configured to process the classified spatial data to assign visual identifiers to the different classes within the classified spatial data. Further, the system (106) may be configured to store a mapping of the classes to the visual identifiers in a color index table (120) (as shownin the FIG. IB). In an exemplary embodiment, the visual identifiers may be Red Green Blue (RGB) color values showing numerical representation of colors based on a RGB model. As used herein, the term “color index table (120)” refers to a data structure used in geospatial applications to map the different classes (class identifies) to the RGB color values.

[0081] Further, in an embodiment, the system (106) may be configured to generate image tiles based on the processed data and store the generated image tiles in a database (124) (as shown in the FIG. IB) for efficient retrieval. As used herein, the term “image tiles” may refer to small and rectangular images that represent a portion of a larger map or the raster data at various zoom levels. Further, in an embodiment, the system (106) may be configured to retrieve and display the relevant image tiles for a current viewport of a map when the user (104) accesses the map. As used herein, the term “current viewport of the map” may refer to a specific area of the map that is visible to the user (104) on a screen at any given time. A viewport may be defined by a current zoom level and geographic coordinates of map’s center and comers. Thus, the system (106) enables smooth and dynamic visualization of geospatial information. In an embodiment, components and modules of the system (106) may be explained in conjunction with FIG. IB and FIG. 1C respectively.

[0082] In an embodiment, the network (108) may include, at least one of a 4G network, a 5G network, or the like. The network (108) may enable the UE (102) to communicate with other devices in the network architecture (100) and / or with the system (106). The network (108) may include a wireless card or some other transceiver connection to facilitate this communication. In another embodiment, the network (108) may be implemented as, or include any of a variety of different communication technologies such as a Wide Area Network (WAN), a Local Area Network (LAN), a wireless network, a mobile network, a Virtual Private Network (VPN), the Internet, a Public Switched Telephone Network (PSTN), or the like.

[0083] Although the FIG. 1 A shows exemplary components of the network architecture (100); however, in other embodiments, the network architecture (100) may include fewer components, different components, differently arranged components, or additional functional components than depicted in the FIG. 1A. Additionally, or alternatively, one or more components of the network architecture (100) may perform functions described as being performed by one or more other components of the network architecture (100).

[0084] FIG. IB illustrates an exemplary block diagram of a system (106) depicting an interaction between components, in accordance with an embodiment of the disclosure.

[0085] As shown in the FIG. IB, the system (106) may include the components such as, the input data sources (114), a preprocessing unit (116), a visual identifier generator (118), the color index table (120), an image generator (122), the database (124) and a web portal (126).

[0086] In an exemplary embodiment, the input data sources (114) may be capable to store the classified spatial data. The input data sources (114) may be, but not limited to, the classified flat files type A (128a), the classified flat files type B (128b), the spatial database (130), and so forth. The classified spatial data may be available in different formats such as, a raster format and a polygon format. In an embodiment, the classified spatial data may be the classified raster data stored in the classified flat files type A (128a). The classified flat files type A (128a) may represent a specific type of data file containing the classified spatial data in the raster format. In another embodiment, the classified spatial data may be the polygon based classified data stored in the classified flat files type B (128b). The classified flat files type B (128b) may represent a specific type of data file containing the classified spatial data in the polygon format. As used herein, the term “flat files” are simple text files that contain data in a tabular format with each line representing a record and each field separated by a delimiter. In yet another embodiment, the classified spatial data may be a classified map data stored in the spatial database(130). In such embodiment, the classified map data may be, the classified raster data or the polygon based classified data.

[0087] Further, the classified spatial data may be transmitted from the input data sources (114) to the preprocessing unit (116) that may be configured to read and process the classified spatial data. In an embodiment, processing of the classified spatial data may include various operations such as, data formatting, data cleaning, data transformation, class assignment, data aggregation, date filtering, and so forth. The preprocessing unit (116) may be configured to transmit the processed classified spatial data to the visual identifier generator (118).

[0088] The visual identifier generator (118) may be configured to assign the visual identifier to each class within the classified spatial data. For example, in a scenario of a Best Server Plot (BPS), where every cell represents a new class, all the visual identifiers may be assigned to every new class. As used herein, the term “BPS” may refer to a geospatial representation that shows a best serving cell for every location on the map within a specific area. Further, the visual identifier generator (118) may be connected to the color index table (120) for storing the mapping of the classes to the visual identifiers in the color index table (120). In an aspect, the BSPs are updated frequently in best server files with new sites (classes) along with existing sites. As used herein, the best server files” may refer to geospatial data files used to indicate the best serving cell for each location within the specific area. For the existing sites, the visual identifiers need not to be generated again, and the same visual identifier may be reused for the color index table (120). In an embodiment, a process of assignment of the visual identifier may be explained in detail in conjunction with the FIG. 1C.

[0089] Further, in an embodiment, once the visual identifiers for every class and the mapping is available in the color index table (120), the image generator (122) starts preparing tile-wise data. The tile-wise data may be the image tiles that cover all geographical locations as covered by an actual input data such as, the classified spatial data. In an exemplary embodiment, there may be multiple rastertiles to cover a same area, which is covered by the input data. In an embodiment, the image generator ( 122) may start generating the image tiles at higher zoom levels first (e.g., zoom level 18), and then the image tiles are generated at all lower zoom levels (e.g., zoom level 4). In an aspect, a plurality of tiles may be calculated for various zoom levels. In an embodiment, a process of an image tile generation may be explained in detail in conjunction with the FIG. 1C.

[0090] Further, the image generator ( 122) may be configured to transmit the generated image tiles of the classified spatial data to the database (124) that may store the image tiles of the classified spatial data for every zoom level. In an exemplary embodiment, the database (124) may be configured to store the classified raster data in a map tile format. In another embodiment, the database (124) may be configured to store the polygon based classified data in the map tile format.

[0091] Further, in an embodiment, the web portal (126) may be a website. In another embodiment, the web portal (126) may be web application installed within the UE (102) (as shown in the FIG. 1A). The web portal (126) may be a user interface (200) (as shown in FIG. 2) through which the users (104) (as shown in the FIG. 1A) may interact with the classified spatial data. The web portal (126) may allow the users (104) to view the map, zoom in, zoom out, pan across different areas, and access additional information about map data.

[0092] FIG. 1C illustrates an exemplary block diagram depicting modules of the system (106), in accordance with an embodiment of the present disclosure. In an embodiment, the system (106) may include a receiving unit (132), a memory (134), an interfacing unit (136), a processing unit (138) and the database (124). In an embodiment, the processing unit (138) may include an assignment module (140), a tile generation module (142) and a display module (144).

[0093] In an embodiment, the receiving unit (132) may be configured to receive the classified spatial data from the UE (102) (as shown in the FIG. 1A). Insuch embodiment, the UE (102) may first receive the classified spatial data from the input data sources (114) and then transmit the classified spatial data to the receiving unit (132). In another embodiment, the main processor (110) (as shown in the FIG. 1A) of the UE (102) may process the classified spatial data for performing the specific tasks. In an exemplary embodiment, the UE (102) may initiate a request to the input data sources (114) for retrieving the classified spatial data. In an embodiment, the user (104) may directly download files of the classified spatial data from the input data sources (114) using the UE (102). In an exemplary embodiment, the files may be the classified flat files (128a-128b) (as shown in the FIG. IB). In another embodiment, the UE (102) may fetch the classified spatial data from the input data sources (114) using Application Programming Interface (API) calls. Further, the UE (102) may parse the files of the classified spatial data and may store the files in a temporary or local storage.

[0094] Further, in an embodiment, the receiving unit (132) may be configured to transmit the classified spatial data received from the UE (102) to the assignment module (140) through one of, a transmission method such as, but not limited to, secure protocols (e.g. Hyper Text Transfer Protocol (HTTP), Secure File Transfer Protocol (SFTP)), the Application Programming Interface (API) calls, direct upload, and so forth.

[0095] The memory (134) may be a non-transitory computer readable storage medium configured to store the instructions or routines. As used herein, the term “instructions” may refer to the sequence of commands that are written in the programming language and may be executed by the processing unit (138) to perform the tasks associated with the system (106). The memory (134) may include any non-transitory storage device including, for example, but not limited to, a volatile memory such as the Random- Access Memory (RAM), or a non-volatile memory such as an Erasable Programmable Read Only Memory (EPROM), the flash memory, and the like. Embodiments of the present invention are intended to include or otherwise type of the memory (134) including known related art and / or later developed technologies.

[0096] In an embodiment, the interfacing unit (136) may comprise a variety of interfaces, for example, the interfaces for data input and output devices (I / O), storage devices, and the like. The interfacing unit (136) may facilitate communication through the system (106). The interfacing unit (136) may also provide a communication pathway for various other units / modules of the system (106).

[0097] In an embodiment, the database (124) may offer functionality to manage, capture, storage, and retrieval of the classified spatial data. In an embodiment, the database (124) may be configured for serving as a centralized repository for storing the image tiles, the color index table (120) (as shown in the FIG. IB), and so forth. The database (124) is designed to interact seamlessly with other modules of the system (106), such as the assignment module (140), the tile generation module (142) and the display module (144), to support a functionality of the system (106) effectively. The database (124) may store the data that may be generated as a result of functionalities implemented by any of the modules of the processing unit (138). In an embodiment, the database (124) may be separate from the system (106). In an embodiment, the database (124) may be the relational database. In another embodiment, the database (124) may be the non-relational database. In such embodiment, the non-relational database may be a NoSQL database (e.g., HBase).

[0098] The modules are controlled by the processing unit (138) which execute the instructions retrieved from the memory (134). The processing unit (138) further interacts with the interfacing unit (136) to facilitate a user interaction and to provide options for managing and configuring the system (106). The processing unit (138) may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and / or any devices that process data based on operational instructions.

[0099] In an embodiment, the assignment module (140) may be communicatively coupled to the receiving unit (132). The assignment module (140) may be configured to receive the classified spatial data from the receiving unit (132). In an embodiment, the assignment module (140) may be configured to assign the visual identifier to the corresponding classes within the classified spatial data. The visual identifiers may be used to display cells belonging to the corresponding classes on the map.

[0100] In an exemplary embodiment, the assignment module (140) may be configured to retrieve the existing color index table (120) from the database (124), that may contain the mapping of the classes to the visual identifiers. In an embodiment, the mapping of the classes to the visual identifiers may be stored in the color index table (120) by first defining a table structure including columns for storing a class identifier and the visual identifier. Further, the color index table (120) may be created in the database (124) to store mapping information. Further, rows may be inserted into the color index table (120) for storing the mapping of the classes to the visual identifiers.

[0101] The assignment module (140) may also be configured to parse the classified spatial data for identifying unique classes. The assignment module (140) may be configured to utilize the visual identifier generator (118) (as shown in the FIG. IB) to initialize a color palette, ensuring a wide range of distinguishable colors. In an embodiment, the assignment module (140) may be configured to determine whether the classes of the classified spatial data are existing classes or new classes based on the parsed data. In other words, for each class in the parsed data, the assignment module (140) may be configured to check if the corresponding class exists in the color index table (120). If the class is found in the color index table (120), then the class is considered as an existing class. If the class is not found in the color index table (120), then the class is considered as a new class.

[0102] The assignment module (140) may be configured to retrieve previously assigned visual identifiers for the corresponding existing classes of theclassified spatial data from the color index table (120) when it is determined that the classes are the existing classes. In another embodiment, if the classes are determined to be the new classes, then the assignment module (140) may be configured to generate and assign new visual identifiers to the corresponding new classes of the classified spatial data. The assignment module (140) may be configured to store the mapping of the classes to the corresponding visual identifiers in the color index table (120). In an exemplary embodiment, the assignment module (140) may be configured to store the mapping of the new classes to the new assigned visual identifiers in the color index table (120).

[0103] Considering an example where the classified spatial data contains the classes A, B, C and D and the initial color index table (120) may already have some classes such as, A, B and C with the assigned visual identifiers. Therefore, while assignment of the visual identifiers for the classes A, B and C, their visual identifiers are retrieved from the color index table (120) and for a new class D, a new visual identifier is generated and assigned. Accordingly, the color index table (120) is updated with the mapping of the new class D to the new visual identifier.

[0104] Further, in an embodiment, the tile generation module (142) may be communicatively coupled to the assignment module (140). The tile generation module (142) may be configured to generate the image tiles at multiple levels of details from the classified spatial data using the assigned visual identifiers. In an exemplary embodiment, the multiple levels of details may be multiple zoom levels ranging from a higher zoom level to a lower zoom level. In an exemplary embodiment, the higher zoom level may be 18 and the lower zoom level may be 4. In an embodiment, at the higher zoom level, each image tile covers a small geographic area, providing detailed views. In another embodiment, at the lower zoom level, each tile covers a large geographic area, providing broader views.

[0105] In an exemplary embodiment, the tile generation module (142) may be configured to generate the image tiles by following a below process.

[0106] In an embodiment, the tile generation module (142) may be configured to first identify a geographic area covered by a tile at each zoom level. Then, the tile generation module (142) may be configured to extract the classified spatial data along with the assigned visual identifiers for the corresponding geographic area.

[0107] Further, the tile generation module (142) may be configured to convert the extracted classified spatial data into an image format using the assigned visual identifiers. For converting the extracted classified spatial data into the image format, an image may be created where each pixel corresponds to the cell in a spatial data grid is colored according to the visual identifiers assigned to the class of the corresponding cell.

[0108] In an embodiment, the tile generation module (142) may be configured to store the generated image tiles in the database (124) for web rendering. In an embodiment, the generated image tiles may be stored in the database (124) in a structured row key format. As used herein, the term “structured row key format” refers to a structure and organization of a primary key used to uniquely identify rows in the database (124). In an exemplary embodiment, the row key format may be [Data Name]-[Date]-[Zoomlevel]. For example, the structured row key format may be LMN_20240619 18, where LMN represents the data name, 20240619 represents the date and 18 represents the zoom level. In an embodiment, the tile generation module (142) may be configured to assign a unique identifier to each image tile based on location / coordinates and the zoom level. In an embodiment, the unique identifier may be used as a key for storing and retrieving the corresponding image tile.

[0109] In an exemplary embodiment, the display module (144) may be configured to render a set of image tiles from the image tiles stored in the database (124) on the map displayed on the user interface (200) of the web portal (126) (as shown in the FIG. IB) based on the current viewport of the map. In an exemplaryembodiment, the display module (144) may be configured to identify the current viewport when the user ( 104) interacts with the web portal ( 126) . In an embodiment, an interaction may involve actions such as, but not limited to, panning, zooming, initial loading, and so forth. The display module (144) maybe configured to identify the current viewport by calculating geographic boundaries of the current viewport based on factors such as, but not limited to, latitude and longitude, the zoom level, map dimensions, and so forth. Further, the display module (144) may be configured to calculate a geographic extent (e.g. north, south, east, west) of the current viewport using center coordinates, the zoom level, the map dimensions and so forth. Based on the calculated geographic extent, the display module (144) may be configured to identify the image tiles covering the geographic extent at the zoom level identified in the current viewport from the database (124). The display module (144) may further be configured to retrieve the identified image tiles from the database (124) and transmits the image tiles to the web portal (126). The web portal (126) may render the image tiles in the current viewport of the map on the user interface (200), allowing the user (104) to see the map for the selected zoom level. In other words, the rendered image tiles may show colored representation of the classified spatial data on the map.

[0110] In an embodiment, the display module (144) may be configured to receive a user interaction with the specific area on the map from the web portal (126). The user interaction may be, but not limited to, click, hover, touch, and so forth. Upon receiving the user interaction, the display module (144) may be configured to identify the visual identifier of the specific area based on the image tiles. Further, in an embodiment, the display module (144) may be configured to transmit a query to the color index table (120) for fetching class information corresponding to the identified visual identifier from the color index table (120). The color index table (120) may include reverse mappings of the visual identifiers to the classes for decoding the class information. The display module (144) may be configured to display the fetched class information on the web portal (126) in response to the user interaction.

[0111] In an exemplary embodiment, if the user (104) clicks on any point on the map, then the user ( 104) may view the actual class using the reverse mapping which is available in the color index table (120).

[0112] Although the FIG. 1C shows an exemplary block diagram of the system (106); however, in other embodiments, the system (106) may include fewer components, different components, differently arranged components, or additional functional components than depicted in the FIG. 1C. Additionally, or alternatively, one or more components of the system (106) may perform functions described as being performed by one or more other components of the system (106).

[0113] FIG. 2 illustrates an exemplary user interface (200) for visualization of the classified raster data, in accordance with an embodiment of the present disclosure.

[0114] In an aspect, the classified raster data may be processed, transformed, and finally stored in the database (124) (as shown in the FIG. IB) for efficient rendering.

[0115] In an aspect, whenever the user (104) (as shown in the FIG. 1A) opens the web portal (126) (as shown in the FIG. IB) and accesses an “onair” (204) option under prediction layers (202), the classified raster data is requested only for the current viewport of the map. Thus, from a backend as explained in the FIG. 1C, the database (124) is queried only for the current viewport, and only those raster image tiles are returned back to the user interface (200), which covers the entire current viewport. In an aspect, to decode the class information of every color, the reverse mapping from the visual identifier to the actual class is fetched from the color index table (120) (as shown in the FIG. IB). In an aspect, every color may represent the class or the best serving cell.

[0116] FIG. 3 illustrates an exemplary visualization (300) of the classified raster data, in accordance with an embodiment of the present disclosure.

[0117] The classified raster data may include metadata (302) that represents information about the classified raster data. In an exemplary embodiment, the metadata (302) may be, but not limited to, coordinates of a comer reference point, cell size, total number of classes, number of columns (ncols), number of rows (nrows). mapping of encoded values to actual classes, and so forth. In an embodiment, the coordinates of the comer reference point may be geographical coordinates of a reference point, usually on a top-left comer of the classified raster data. For example, xllcomer represents a geographic location (X-coordinate) of a lower left comer of a raster grid. Also, yllcomer represents the geographic location (Y -coordinate) of the lower left comer of the raster grid.

[0118] In an aspect, the ncols may represent the number of columns in the raster grid and nrows may represent the number of rows in the raster grid. Further, in an aspect, the total number of classes may represent a total number of unique classes within the classified raster data. As shown in the FIG. 3, each class is represented by a unique cell site identifier such as X, Y, Z, P, Q, R, and so forth.

[0119] Further, in an embodiment, the visualization (300) of the classified raster data includes encoded class information (304) for every coordinate as shown in the FIG. 3, where each coordinate is equally spaced, and defined by the cell size in the metadata (302). As used herein, the term “encoded class information (304)” refers to a representation of the classified spatial data in a numerical format that can be processed.

[0120] FIG. 4 illustrates an exemplary visualization (400) of the classified spatial data with polygon details for every class, in accordance with an embodiment of the present disclosure.

[0121] In an aspect, the visualization (400) includes the polygon details for every class, where each class is represented by a polygon shape (504) (as shown in FIG. 5) with specific vertices. In an aspect, the .SHP file format may allow for storage of the polygon based classified data, including a class name (402) associatedwith each polygon. This enables the visualization (400) of the classified spatial data with detailed polygon boundaries for each class. In an aspect, polygon vertices (404) may define the shape (504) of each class, and inclusion of the class name (402) allows for identification and labelling of the different classes.

[0122] FIG. 5 illustrates an exemplary visualization (500) of the classified spatial data with the polygon details stored in a spatial object of the spatial database (130) (as shown in the FIG. IB), in accordance with an embodiment of the present disclosure.

[0123] In an aspect, each spatial object may represent the specific class and associated with a unique class Identification (ID) (502) and the shape (504). In an aspect, the spatial object in the spatial database (130) may provide a structured way to store and manage the classified spatial data. In an exemplary embodiment, for the class ID (502) such as, ID1, the corresponding shape (504) may be, Shape 1. In an exemplary embodiment, the spatial database (130) may be the relational database or the non-relational database.

[0124] FIG. 6 illustrates an exemplary color index table (120), in accordance with an embodiment of the present disclosure.

[0125] In an aspect, the color index table (120) may be managed in any database (124) (as shown in the FIG. IB) with the structure as shown in the FIG. 6. The structure of the color index table (120) includes columns such as, sitelD column (600), Class Number (CNUM) column (602), red column (604), blue column (606), green column (608), band column (610) and a creation date column (612).

[0126] In an exemplary embodiment, the site ID column (600) may be a unique identifier for a specific location. The site ID column (600) may be utilized to associate the visual identifiers such as, the RGB color values with the geographical area.

[0127] Further, the CNUM column (602) may represent the class identifier. In an embodiment, each class of the classified spatial data may be assigned with a unique CNUM value, that may be used to link the class to the corresponding RGB color value. Further, the red column (604), the blue column (606) and the green column (608) may store the RGB color values associated with each class. Each color value ranges from 0 to 255.

[0128] The band column (610) may represent a specific channel or a band of color information. In the RGB color model, each pixel is represented by three color channels such as, red, blue and green. The creation date column (612) may store date when the RGB color mapping was created or last updated.

[0129] FIG. 7 illustrates an exemplary visualization (700) of the BPS, in accordance with an embodiment of the present disclosure.

[0130] In an aspect, the BSP provides the information of the best serving cells for every location on the map depending on the raster resolution. As used herein, the term “raster resolution” refers to a size of each cell in the raster grid used to represent the map. In an aspect, the cell site that provides best quality for each cell is identified and such information is then used to create the BSP, which usually represents the best serving cell for every location on the map. In an aspect, the BSP with varying raster resolutions may be beneficial in understanding coverage and quality of a service provided by the different cell sites across the map. In an exemplary embodiment, a cell identifier A (702), a cell identifier B (704), a cell identifier C (706) and a cell identifier D (708) are the unique identifiers that may be assigned to the corresponding cell sites.

[0131] FIG. 8 illustrates an exemplary computer system (800) in which or with which embodiments of the present disclosure may be implemented. As shown in the FIG. 8, the computer system (800) may include an external storage device (810), a bus (820), amain memory (830), a read only memory (840), amass storage device (850), a communication port (860), and a processor (870). A person skilledin the art will appreciate that the computer system (800) may include more than one processor (870) and the communication ports (860). The processor (870) may include various modules associated with embodiments of the present disclosure.

[0132] In an embodiment, the external storage device (810) may be any device that is commonly known in the art such as, but not limited to, a memory card, a memory stick, a solid-state drive, a hard disk drive (HDD), and so forth.

[0133] In an embodiment, the bus (820) may be communicatively coupled with the processor(s) (870) with the other memory, storage, and communication blocks. The bus (820) may be, e.g., a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, a Small Computer System Interface (SCSI), a Universal Serial Bus (USB) or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor (870) to the computer system (800).

[0134] In an embodiment, the main memory (830) may be a Random- Access Memory (RAM), or any other dynamic storage device commonly known in the art. The Read-only memory (840) may be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or Basic Input / Output System (BIOS) instructions for the processor (870).

[0135] In an embodiment, the mass storage device (850) may be any current or future mass storage solution, which may be used to store information and / or instructions. Exemplary mass storage solutions include, but are not limited to, a Parallel Advanced Technology Attachment (PATA) or a Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and / or Firewire interfaces), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g., an array of disks (e.g., SATA arrays).

[0136] Further, the communication port (860) may be any of an RS-232 port for use with a modem-based dialup connection, a 10 / 100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication port (860) may be chosen depending on the network (108), such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the computer system (800) connects.

[0137] Optionally, operator and administrative interfaces, e.g., a display, a keyboard, a joystick, and a cursor control device, may also be coupled to the bus (820) to support a direct operator interaction with the computer system (800). Other operator and administrative interfaces may be provided through network connections connected through the communication port (860). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system (800) limit the scope of the present disclosure.

[0138] FIG. 9 illustrates a flowchart of a method (900) for analyzing and visualizing geospatial data, in accordance with an embodiment of present disclosure.

[0139] At step (902), the method (900) includes a step of receiving the classified spatial data from input data sources (114). The classified spatial data may be classified raster data or polygon based classified data. Further, the input data sources (114) may be, but not limited to, simulation tools, telecommunication equipment vendors, GIS platforms, regulatory agencies, measurement campaigns, remote sensors, and so forth. The classified spatial data may be read and processed by a preprocessing unit (116). In an embodiment, processing of the classified spatial data may include various operations such as, data formatting, data cleaning, data transformation, class assignment, data aggregation, date filtering, and so forth.

[0140] At step (904), the method (900) includes a step of assigning, by a visual identifier generator (118), visual identifiers to corresponding classes withinthe classified spatial data by a processing unit (138). The visual identifiers are RGB color values. The processing unit (138) determines whether the classes of the classified spatial data are existing classes or new classes. If a class is found in a color index table (120), then the class is considered as an existing class. If the class is not found in the color index table (120), then the class is considered as a new class. Further, previously assigned visual identifiers for the corresponding existing classes of the classified spatial data may be retrieved from the color index table (120) when it is determined that the classes are the existing classes. If the classes are determined to be the new classes, then the processing unit (138) generates and assigns new visual identifiers to the corresponding new classes of the classified spatial data. The method (900) also includes a step of storing the mapping of the classes to the assigned visual identifiers in the color index table (120).

[0141] At step (906), the method (900) includes a step of generating, by an image generator (122), image tiles at multiple levels of details from the classified spatial data using the assigned visual identifiers by the processing unit (138). In an exemplary embodiment, the multiple levels of details may be multiple zoom levels ranging from a higher zoom level to a lower zoom level. In an exemplary embodiment, the higher zoom level may be 18 and the lower zoom level may be 4.

[0142] At step (908), the method (900) includes a step of storing the one or more generated image tiles in a database (124) by the processing unit (138). The database ( 124) is a non-relational database . The generated image tiles may be stored in the database (124) using a structured row key format. In an exemplary embodiment, the row key format may be [Data Name]-[Date]-[Zoomlevel].

[0143] At step (910), the method (900) includes a step of rendering a set of image tiles from the image tiles stored in the database (124) on a user interface (200) based on a current viewport of a map by the processing unit (138). The rendered image tiles may show colored representation of the classified spatial data on the map. Further, the method (900) includes a step of fetching class information corresponding to the at least one of the visual identifiers on the user interface (200)from the color index table (120), in response to user interaction with the map. The user interaction may be, but not limited to, touch, hover, click on a specific area of the map. The visual identifiers of the specific area may be identified based on the image tiles. In an embodiment, the color index table (120) includes reverse mappings of the visual identifiers to the classes for decoding the class information. In an embodiment, the class information may include, but not be limited to, class identifier, encoded value, color value, metadata, class description, and so forth.

[0144] In an embodiment, the present disclosure discloses a UE (102) configured for rendering one or more image tiles on a user interface (200). The UE (102) includes: a main processor (110). The UE (102) further includes a computer readable storage medium (112) storing one or more instructions for execution by the main processor (110) to: receive a request for map data corresponding to a current viewport of a map displayed on the user interface (200); determine a set of image tiles from the one or more image tiles corresponding to the current viewport based on a zoom level of the map; retrieve the determined set of image tiles from a database (124); and render the retrieved setofimage tiles on the user interface (200) based on the current viewport of the map.

[0145] The present disclosure provides technical advancements related to a field of classified spatial data visualization and storage. This advancement addresses limitations of existing solutions by the invention of a system and method for generating, storing, and rendering image tiles of the classified spatial data on a user interface. The disclosure provides innovative aspects such as assignment of RGB color values to classes within raster and polygon data, generation of multizoom level image tiles, and efficient storage of these tiles in a NoSQL database. By implementing this invention, the disclosed invention enhances accuracy and accessibility of geospatial data visualization, resulting in a more intuitive and efficient user experience for analyzing and interpreting spatial data in various applications, such as network coverage mapping.

[0146] While the foregoing describes various embodiments of the present disclosure, other and further embodiments of the present disclosure may be devised without departing from the basic scope thereof. The scope of the present disclosure is determined by the claims that follow. The present disclosure is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the present disclosure when combined with information and knowledge available to the person having ordinary skill in the art.

[0147] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be implemented merely as illustrative of the disclosure and not as a limitation.ADVANTAGES OF THE PRESENT DISCLOSURE

[0148] The present disclosure provides a system and a method for storing classified grid data for efficient web rendering and spatial analytics.

[0149] The present disclosure provides a visualization of big data scale classified map data.

[0150] The present disclosure provides a storage for all kinds of classified raster data (i.e. classified raster data or polygon based classified data) for web rendering.

[0151] The present disclosure provides a system and a method for storing processed raster classified data in tiles format for every zoom level for web visualization.

[0152] The present disclosure provides a system and a method for maintaining a definition of pixels or mapping of color to an actual class (in tiles) in a color index table.

Claims

CLAIMS1. A method (900) for analyzing and visualizing geospatial data, wherein the method (900) comprising steps of: receiving (902), by a receiving unit (132), classified spatial data from a plurality of input data sources (114); assigning (904), by a processing unit (138), visual identifiers to one or more classes within the classified spatial data; generating (906), by the processing unit (138), one or more image tiles at multiple levels of details from the classified spatial data using the assigned visual identifiers; storing (908), by the processing unit (138), the one or more generated image tiles in a database (124); and rendering (910), by the processing unit (138), a set of image tiles from the one or more image tiles stored in the database (124) on a user interface (200) based on a current viewport of a map.

2. The method (900) as claimed in claim 1, wherein the classified spatial data is selected from one of, classified raster data or polygon based classified data.

3. The method (900) as claimed in claim 1, wherein assigning the visual identifiers comprises: retrieving, by the processing unit (138), a color index table (120) from the database (124), wherein the color index table (138) comprises a predefined mapping of the one or more classes to the corresponding visual identifiers; determining, by the processing unit (138), whether each class in the received classified spatial data is an existing class or a new class based on the color index table (120); retrieving, by the processing unit (138), previously mapped visual identifiers from the color index table (120) when the classified spatial data comprises the existing class; andgenerating and assigning, by the processing unit (138), new visual identifiers when the classified spatial data comprises the new class.

4. The method (900) as claimed in claim 1, wherein rendering the one or more image tiles comprises: identifying, by the processing unit (138), the current viewport of the map displayed on the user interface (200) based on a user interaction with the map; calculating, by the processing unit (138), a geographic extent of the current viewport of the map; identifying, by the processing unit (138), the one or more image tiles corresponding to the geographic extent at a zoom level of the current viewport from the database (124); and retrieving, by the processing unit (138), the identified one or more image tiles from the database (124) for rendering the one or more image tiles on the user interface (200).

5. The method (900) as claimed in claim 1 , comprising a step of storing a mapping of the one or more classes to the corresponding visual identifiers in a color index table (120).

6. The method (900) as claimed in claim 1, comprising a step of fetching class information corresponding to at least one of the visual identifiers on the user interface (200) from a color index table (120) in response to a user interaction with the map.

7. The method (900) as claimed in claim 6, wherein fetching the class information comprises decoding the class information from reverse mappings of the visual identifiers to the one or more classes stored in the color index table (120).

8. A system (106) for analyzing and visualizing geospatial data, wherein the system (106) comprising:a receiving unit (132) configured to receive classified spatial data from a plurality of input data sources (114); and a processing unit (138) communicatively coupled to the receiving unit (132), wherein the processing unit (138) is configured to: assign visual identifiers to one or more classes within the classified spatial data; generate one or more image tiles at multiple levels of details from the classified spatial data using the assigned visual identifiers; store the one or more generated image tiles in a database (124); and render a set of image tiles from the one or more image tiles stored in the database (124) on a user interface (200) based on a current viewport of a map.

9. The system (106) as claimed in claim 8, wherein the classified spatial data is selected from one of, classified raster data or polygon based classified data.

10. The system (106) as claimed in claim 8, wherein the processing unit (138) is configured to: retrieve a color index table (120) from the database (124), wherein the color index table (138) comprises a predefined mapping of the one or more classes to the corresponding visual identifiers; determine whether each class in the received classified spatial data is an existing class or a new class based on the color index table (120); retrieve previously mapped visual identifiers from the color index table (120) when the classified spatial data comprises the existing class; and generate and assign new visual identifiers when the classified spatial data comprises the new class for assigning the visual identifiers to the one or more classes within the classified spatial data.

11. The system (106) as claimed in claim 8, wherein processing unit (138) is configured to:identify the current viewport of the map displayed on the user interface (200) based on a user interaction with the map; calculate a geographic extent of the current viewport of the map; identify the one or more image tiles corresponding to the geographic extent at a zoom level of the current viewport from the database (124); and retrieve the identified one or more image tiles from the database (124) for rendering the one or more image tiles on the user interface (200).

12. The system (106) as claimed in claim 8, wherein the processing unit (138) is configured to store a mapping of the one or more classes to the corresponding visual identifiers in a color index table (120).

13. The system (106) as claimed in claim 8, wherein the processing unit (138) is configured to fetch class information corresponding to at least one of the visual identifiers on the user interface (200) from a color index table (120) in response to a user interaction with the map.

14. The system (106) as claimed in claim 13, wherein the processing unit (138) is configured to fetch the class information by decoding the class information from reverse mappings of the visual identifiers to the one or more classes stored in the color index table (120).

15. A User Equipment (UE) ( 102) configured for rendering one or more image tiles on a user interface (200), the UE (102) comprising: a main processor (110); a computer readable storage medium (112) storing one or more instructions for execution by the main processor (110) to: receive a request for map data corresponding to a current viewport of a map displayed on the user interface (200); determine a set of image tiles from the one or more image tiles corresponding to the current viewport based on a zoom level of the map;retrieve the determined set of image tiles from a database (124); and render the retrieved set of image tiles on the user interface (200) based on the current viewport of the map.

16. A computer program product comprising a memory (134) having instructions that, when executed by a processing unit (138), cause the processing unit (138) to perform a method (900) for analyzing and visualizing geospatial data, the method (900) comprising steps of: receiving (902), by a receiving unit (132), classified spatial data from a plurality of input data sources (114); assigning (904), by a processing unit (138), visual identifiers to one or more classes within the classified spatial data; generating (906), by the processing unit (138), one or more image tiles at multiple levels of details from the classified spatial data using the assigned visual identifiers; storing (908), by the processing unit (138), the one or more generated image tiles in a database (124); and rendering (910), by the processing unit (138), a set of image tiles from the one or more image tiles stored in the database (124) on a user interface (200) based on a current viewport of a map.

Images