DESC:FIELD OF INVENTION
[0001] The present disclosure relates to systems and methods for geospatial data management and more particularly, to the system and method for efficient web-based ingestion, processing, and dissemination of heterogeneous geospatial data.
BACKGROUND
[0002] Geospatial data, collected from sources such as drones, satellites, aerial/UAV spaceborne sensors, and ground observations, is utilized for earth observation to derive meaningful insights across various domains including environment, defense/security, retail/commerce, and urban planning. This data typically encompasses multiple formats, complex technical parameters, sensor attributes, and exhibits significant variability in file sizes.
[0003] The volume of earth observation data that requires management, processing, and dissemination has grown exponentially in recent years due to the increasing number of satellites, drones, and UAVs in operation. The massive influx of heterogeneous geospatial data presents challenges for traditional data handling systems, which may struggle to efficiently ingest, catalog, and distribute such diverse information.
[0004] Existing approaches to geospatial data management often face difficulties in dealing with the sheer scale and complexity of modern datasets. Processing large volumes of data in various formats can lead to bottlenecks, while the technical intricacies of different sensor types and data structures may result in compatibility issues. Additionally, the time-sensitive nature of many geospatial applications demands rapid data processing and dissemination capabilities that conventional systems may lack.
[0005] A patent application CN102004748A titled “Heterogeneous geographic data query system and method” discloses about a geographic data query system and method, especially a heterogeneous geographic data query system and method. It includes at least the following parts: a signal receiving device, a heterogeneous data storage device, a complete heterogeneous geographical data query system, a wireless communication hardware, and an intelligent terminal; wherein, the signal receiving device receives monitoring data, and Store the data in workstations distributed all over the country. The heterogeneous geographic data storage device is responsible for classifying and storing the data on the workstations in a persistent manner. After receiving the query request from the smart terminal, it first checks whether the user is authorized. After confirming that it is correct, it will Query processing is carried out from the heterogeneous geographic data query system, and the query results are sent to the terminal after the wireless communication hardware is responsible for negotiating with the corresponding agent. The invention can effectively query the heterogeneous geographical data on the portable device, has high query efficiency, and is especially suitable for engineering personnel who go out to work.
[0006] Another patent application US6985929B1 titled “Distributed object-oriented geospatial information distribution system and method thereof” discloses about a distributed object-oriented geospatial database system and method thereof over the Internet using Web-based technology to perform data-driven queries, such as retrieving, viewing and updating, geospatial data of the object oriented geospatial database, such as vector, raster, hypertext and multimedia data, including data types or formats of ESRI shape files, GSF, oceanographic ASCII text data by NAVOCEANO and geospatial data with temporal information and supporting 3D display of the geospatial data. The object-oriented geospatial database system is implemented in a heterogeneous object-oriented development and integration environment through the Common Object Request Broker Architecture (CORBA).
[0007] It has been appreciated that a system is needed that overcomes one or more of these problems.

SUMMARY
[0008] The present invention provides a system and method for efficient web-based heterogeneous data management and dissemination. The system comprises a data ingestor and importer module for acquiring raw geospatial data from various sensors and creating an Ingested Data Repository; an authentication module for user access control; a network load balancer module for port exposure security and dynamic network load allocation; a central task manager module for synchronizing and controlling the overall task workflow in a hardware-aware manner; a raster optimizer module for converting rasters to optimized formats; and a geospatial database module for extracting and cataloguing metadata in a queryable manner.
[0009] The method involves receiving heterogeneous geospatial data from a number of sources, dynamically allocating to one or more components for processing tasks, processing the geospatial data using the one or more components, storing processed data and metadata in a queryable database, and disseminating the processed data through a web-based interface. The processing steps include analyzing raster attributes, converting rasters to optimized formats, extracting metadata, and organizing it in a structured format within the database.
[0010] This system and method provide significant technical advancements in the field of geospatial data management. By implementing hardware-aware resource allocation and optimization techniques, the invention enables efficient handling of large volumes of heterogeneous geospatial data. The web-based interface, coupled with dynamic load balancing and user authentication, allows for secure and scalable data dissemination. The ability to process and catalog various data formats in an optimized manner enhances data accessibility and usability for diverse applications in earth observation, environmental monitoring, urban planning, and other domains requiring geospatial analytics.

BRIEF DESCRIPTION OF FIGURES
[0011] Embodiments of the invention will be described, by way of example, with reference to the following drawings, in which:
[0012] Figure 1 illustrates the system architecture of the geospatial data processing invention;
[0013] Figure 2 illustrates the flowchart for raster registration, a crucial process in the invention's geospatial data handling;
[0014] Figure 3 illustrates the flowchart for merging multi-user annotations.
[0015] Common reference numerals are used throughout the figures to indicate similar features.

DETAILED DESCRIPTION

[0016] The following description describes various features and functions of the disclosed system. The illustrative aspects described herein are not meant to be limiting. It may be readily understood that certain aspects of the disclosed system can be arranged and combined in a wide variety of different configurations, all of which have not been contemplated herein.
[0017] Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.
[0018] Features that are described and/or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments and/or in combination with or instead of the features of the other embodiments.
[0019] The terms and words used in the following description are not limited to the bibliographical meanings but are merely used to enable a clear and consistent understanding of the invention. Accordingly, it should be apparent to those skilled in art that the following description of exemplary embodiments of the present invention are provided for illustrative purposes only and not for the purpose of limiting the invention.
[0020] It is to be understood that the singular forms “a”, “an” and “the” include plural referents unless the context clearly dictates otherwise.
[0021] It should be emphasized that the term “comprises/comprising” when used in this specification is taken to specify the presence of stated features, steps, or components but does not preclude the presence or addition of one or more other features, steps, components, or groups thereof. The equations used in the specification are only for computation purposes.
[0022] The present invention relates to a system for efficient web-based heterogeneous data management and dissemination. The system provides an automated approach to ingest, process, organize, manage, and disseminate heterogeneous geospatial data and associated analytics in a web application environment. The system is designed to handle multiple data formats, including rasters, videos, text, and vector layers. The system allows for the integration and management of diverse geospatial data types from various sources, such as aerial imagery, satellite data, drone footage, and ground observations.
[0023] Further, the system is adaptable to perform data fusion, combining information from the number of sources or formats to create a more comprehensive and accurate representation of the geospatial data. The fusion process of the information enables users to derive more meaningful insights from the combined dataset. The system also incorporates robust data dissemination capabilities, allowing users to access and share processed geospatial data and analytics through the web-based interface, which further facilitates collaboration and enables efficient distribution of valuable geospatial information to relevant stakeholders.
[0024] Figure 1 illustrates the system architecture of the geospatial data processing. Further, the system comprises several interconnected modules that work together to achieve efficient data management and dissemination. Further, the system comprise a raw data ingestor, a data-importer module, an authentication module, an input data storage, an ingested data repository, a hardware aware task monitor, a raster pre-processor, a raster optimizer module, a geospatial database, a network load balancer module, a central task manager module.
[0025] Figure 1 illustrates the system architecture of the geospatial data processing invention. The diagram shows nine interconnected modules that form the core of the system. At the center is the Central Task Manager Module, which coordinates operations between other key components. These include the Input Data Storage and Ingested Data Repository for handling data input and storage, an authentication module for security, and a Network Load Balancer for managing network traffic and security. The system also incorporates a Hardware Aware Task Monitor and a Raster Pre-Processor and Optimizer, both connected to a Geospatial DB Module. The system enables efficient processing, storage, and management of geospatial data while ensuring security and optimizing hardware resources.
[0026] Figure 1 illustrates the system architecture of the geospatial data processing invention. In an exemplary embodiment, the central task manager module coordinates operations between other key components. Further, the central task manager manages input data and storage by the input data storage and ingested data repository for handling data input and storage. Further, the authentication module is configured for security, and the network load balancer for managing network traffic and security. The system also incorporates a hardware aware task monitor and a raster pre-processor and optimizer, both connected to a geospatial data base module.
[0027] Further, data acquired from a range of image providers, including satellites, UAVs, and aerial surveys. Further, the data acquired is transmitted to a number of data sources by the data-importer module. Further, the number of data sources may include but not limited to personal computer and laptop.
[0028] Figure 2 illustrates the flowchart for raster registration, a crucial process in the invention's geospatial data handling. The workflow begins with aerial or satellite image input and progresses through several preprocessing steps, including cloud/snow detection, auto-enhancement based on contrast adjustment, and RGB band selection. The system then performs coordinate reference system correction. A key decision point determines whether a basemap is available for the image area. If available, the process moves to auto-ortho-rectification; if not, it generates a cloud-optimized image. The flowchart also includes steps for generating and storing image hashes, marking alignment states, and registering image URLs in the database. This process ensures that incoming geospatial data is properly registered, enhanced, and stored for further use in the system.
[0029] Further, the system includes the raw data ingestor that receives heterogeneous geospatial data from the number of data sources. Further, the data sources may include, but not limited to, system disks, external storage media, or other data repositories. The raw data ingestor and data-importer module is capable of handling multiple data formats, such as rasters, videos, text files, and vector layers. Upon acquisition, the raw data ingestor and data-importer module processes the incoming data and stores in the ingested data repository. The ingested data repository serves as a centralized storage location for all acquired geospatial data. This repository is designed to efficiently organize and manage the diverse types of data ingested by the system.
[0030] The data ingestion process may involve several steps, including data validation, format conversion, and initial metadata extraction, as shown in figure 2. The data processing pipeline for raster begins with the ingestion and preprocessing of raw raster data acquired from a range of image providers, including satellites, UAVs, and aerial surveys. The datasets vary in terms of resolution, spectral characteristics, and spatial references, and standardization is done through processes such as reformatting, unification of the coordinate system, and radiometric calibration. The process starts with cloud masking. Here, areas affected by obstructions, including clouds, shadows, and snow, are identified to be masked out. The second stage of the ingestion process involves the enhancement of an image to boost the quality analytically. Histogram equalization is applied to emphasize color balance in addition to making areas of interest stand out more. After this data is prepared to be accurately registered with base maps in the subsequent steps. Ortho-rectification also corrects the geometric distortion caused by sensor tilt and terrain variation along with platform motion, using GCPs and DEMs, to make images align with a basemap for spatial accuracy. Further, the spatially accurate data provide a seamless representation of the terrain that is also suitable for visualization and analysis purposes. Following improvement, the image is converted to an optimized tile-friendly data format for easy retrieval over low-bandwidth connectivity.
[0031] Vector is ingested either directly as user inputs in compatible file formats or in the form of user-generated annotations using the web UI (user interface). Further, the system is built for shared inputs, multiple users can annotate over the same rasters. To utilize such inputs, for AI training conflicts resulting from mismatches from inputs of different users need to be resolved. The algorithm used for this process is detailed in Fig. 2. The resulting vector data is then added to a centralized database as verified annotation data separately from the user folders and can be used for AI training. Vector inputs in the UI can also be utilized for providing feedback on AI results either by re-labelling or readjusting vector boundaries of the annotations.
[0032] Figure 3 illustrates the flowchart for merging multi-user annotations, addressing a critical aspect of collaborative geospatial data analysis in the invention. The process starts by selecting a raster from the database and fetching corresponding annotations. When multiple annotations exist, the system selects a reference for comparison and computes an Intersection over Union (IoU) score for each unique annotation. The flowchart then shows a series of threshold-based decision points that determine how conflicting annotations are handled. Based on the IoU scores, the system either keeps the reference annotation, allows users to choose between annotations, or prompts users to correct annotations. The process concludes with steps for publishing annotations for web viewing and creating export options. This methodology ensures that conflicting or overlapping annotations from multiple users are systematically resolved, maintaining data integrity in collaborative environments.
[0033] The ingested data repository may utilize appropriate data structures and indexing mechanisms to facilitate rapid retrieval and efficient management of the stored geospatial data. By centralizing the storage of ingested data in the Ingested Data Repository, the system enables streamlined access to the acquired geospatial information for further processing, analysis, and dissemination tasks. This centralized approach also supports data consistency and version control across the system's various modules and functions.
[0034] The final processed image and vector data are then used to create multi-resolution tiles with the objective of efficient storage and rendering. This hierarchical tiling approach supports visualization at zoom-level-specific resolutions. The tiles, along with their metadata-including spatial extent, timestamps, and sensor information-are stored in a spatially optimized database. This enables fast and efficient retrieval based on user queries.
[0035] The system includes an authentication module for controlling user access. When a user attempts to access the system via a web browser, the authentication module initiates a user authentication process. This process may involve prompting the user to enter login credentials, such as a username and password. The authentication module then verifies these credentials against a secure database of authorized users. Upon successful authentication, the user is granted access to the system's web-based interface. The level of access and available features may be determined based on the user's role or permissions, which are managed by the authentication module. This role-based access control ensures that users can only access the data and functionalities appropriate for their authorization level.
[0036] The authentication module may also implement additional security measures to protect user accounts and system integrity. These measures may include multi-factor authentication, session management, and logging of user activities. Multi-factor authentication adds an extra layer of security by requiring users to provide additional verification, such as a temporary code sent to their mobile device, in addition to their standard login credentials. Session management functionality within the authentication module helps maintain secure user sessions throughout their interaction with the system. This may involve generating and managing session tokens, implementing session timeouts for inactive users, and ensuring secure session termination when users log out or close their browser.
[0037] The authentication module may also provide functionality for user account management, including password reset procedures, account lockout policies to prevent unauthorized access attempts, and the ability for administrators to manage user accounts and permissions. By centralizing user authentication and access control within a dedicated module, the system ensures consistent and secure management of user access across all components of the web-based geospatial data management and dissemination platform.
[0038] The system includes a network load balancer module for managing network traffic and ensuring security. The network load balancer module performs two primary functions: port exposure security and dynamic network load allocation. For port exposure security, the network load balancer module acts as a gateway between external network traffic and the internal components of the system. The module controls which ports are exposed to external connections, limiting access to only the necessary and authorized ports. This approach helps protect the system from unauthorized access attempts and potential security vulnerabilities by minimizing the attack surface exposed to external networks.
[0039] The network load balancer module also implements dynamic network load allocation across all functions of the application. This capability allows the system to efficiently distribute incoming network traffic among multiple servers or processing units. The module continuously monitors the current load on each server or processing unit and dynamically routes new requests to the least busy resources. This load balancing approach helps prevent any single component from becoming overwhelmed with traffic, ensuring optimal performance and responsiveness of the system.
[0040] The dynamic load allocation functionality may use various algorithms to determine the most efficient distribution of network traffic. These algorithms may take into account factors such as current server load, response times, and available resources. By intelligently distributing network requests, the system can maintain consistent performance even during periods of high traffic or when certain components are under increased load. Additionally, the network load balancer module may implement health checks on the various system components. The network load balancer module may also provide scalability benefits by allowing new servers or processing units to be added to the system as needed. The module can automatically incorporate these new resources into its load balancing calculations, enabling the system to handle increased traffic or processing demands without manual reconfiguration.
[0041] The system includes a central task manager module that synchronizes and controls the overall task workflow of the application in a hardware-aware manner. This central task manager module monitors available hardware resources, including processor cores, RAM, disk space, and GPU resources (if available). By continuously tracking these resources, the central task manager module can make informed decisions about task allocation and execution. Based on the monitored hardware resources, the central task manager module dynamically allocates resources for various tasks within the system. This allocation process takes into account the current availability of each resource type and the specific requirements of different tasks. For example, computationally intensive tasks may be allocated more processor cores or GPU resources, while memory-intensive tasks may be given priority access to available RAM. This dynamic allocation helps ensure efficient utilization of hardware resources and optimizes overall system performance.
[0042] The central task manager module also plays a crucial role in managing task dependencies and prioritization. By synchronizing the execution of interdependent tasks, the module helps maintain data consistency and prevents conflicts that could arise from concurrent operations on shared resources. Additionally, the central task manager module may implement task prioritization algorithms to ensure that critical or time-sensitive operations are given precedence over less urgent tasks. This hardware-aware approach to task management allows the system to adapt to varying workloads and resource availability, ultimately improving the efficiency and responsiveness of the geospatial data management and dissemination processes.
[0043] The system includes a raster optimizer module that scans for attributes of fetched data such as rasters and converts the fetched data to an optimized format for rendering using low resource allocation. The raster optimizer module analyzes various attributes of the input raster data, such as resolution, color depth, compression format, and file size. Based on this analysis, the raster optimizer module determines the most appropriate optimization techniques to apply to each data such as raster data. The optimization process may involve several techniques to reduce the resource requirements for rendering the raster data. These techniques may include resampling the raster to a lower resolution while maintaining visual quality, reducing the color depth if the full range of colors is not necessary for the specific application, or applying more efficient compression algorithms. The raster optimizer module may also implement tiling strategies, where large rasters are divided into smaller tiles that can be loaded and rendered independently, reducing memory usage and improving rendering performance.
[0044] After applying the selected optimization techniques, the raster optimizer module converts the raster data into a format that is optimized for efficient rendering within the system's web-based environment. This optimized format may be designed to minimize data transfer sizes, reduce decompression time, and facilitate rapid rendering on various client devices. By performing these optimizations, the raster optimizer module enables the system to handle large volumes of geospatial raster data while maintaining responsive performance and efficient use of system resources.
[0045] The system includes a geospatial data base module that extracts and catalogues metadata from rasters in a query-able manner. This module analyzes the ingested raster data to identify and extract relevant metadata attributes. These attributes may include information such as spatial resolution, coordinate system, acquisition date, sensor type, spectral bands, and other pertinent details associated with the raster data. The extracted metadata is then organized and stored in a structured format within a geospatial database, enabling efficient querying and retrieval of information.
[0046] The geospatial database is designed to support spatial queries, allowing users to search for and retrieve raster data based on various criteria, including geographic location, temporal attributes, or specific metadata fields. This query-able structure enables users to efficiently locate and access relevant geospatial data within the system. For example, users may search for rasters covering a particular geographic area, captured within a specific date range, or possessing certain sensor characteristics.
[0047] By cataloguing the metadata in a queryable geospatial database, the system facilitates easy discovery and management of raster data. Users can quickly search for and identify relevant datasets without the need to manually inspect individual files. This capability enhances the overall efficiency of data management and analysis workflows within the system, enabling users to focus on deriving insights from the geospatial data rather than spending time on data discovery and organization tasks.
[0048] The system performs various data processing and analysis steps to handle the heterogeneous geospatial data ingested from multiple sources. These processing steps may include data validation, format conversion, georeferencing, and initial quality assessment. The system is designed to accommodate multiple data formats, such as raster images, vector data, point clouds, and textual information. This flexibility allows the system to ingest and process data from various sensors, including satellite imagery, aerial photography, LiDAR, and ground-based observations.
[0049] To facilitate data fusion, the system employs algorithms and techniques to integrate information from multiple sources or formats. This fusion process may involve spatial alignment, temporal synchronization, and resolution matching of different datasets. For example, high-resolution aerial imagery may be combined with lower-resolution satellite data to create a more comprehensive view of a geographic area. The system may also incorporate machine learning techniques to identify patterns, extract features, and derive insights from the fused datasets.
[0050] The data processing and analysis capabilities of the system enable users to perform various geospatial operations and analytics. These may include change detection, land cover classification, object detection, and terrain analysis. The system may provide tools for users to customize processing workflows, apply filters, and generate derived products from the raw or fused data. Additionally, the system may implement parallel processing techniques to efficiently handle large volumes of geospatial data, distributing computational tasks across available hardware resources to optimize processing speed and resource utilization.
[0051] The system implements efficient data dissemination capabilities to distribute processed and analyzed geospatial data to end-users or other systems. The data dissemination process is designed to handle various types of geospatial data, including raster images, vector layers, and derived analytics products. The system utilizes a web-based interface to facilitate access to the disseminated data, allowing users to retrieve information through standard web protocols and APIs. The data dissemination process incorporates several optimization techniques to ensure efficient delivery of geospatial data. These techniques may include data compression, tiling, and progressive loading of large datasets. The system may implement adaptive streaming algorithms that adjust the resolution and detail of transmitted data based on the user's viewing parameters and available network bandwidth. This approach helps minimize data transfer times while maintaining an appropriate level of detail for the user's current view or analysis requirements.
[0052] To support diverse user needs, the system offers multiple data dissemination formats and protocols. Users may access data through direct downloads, streaming services, or standardized geospatial web services such as Web Map Service (WMS) or Web Feature Service (WFS). The system may also provide options for data export in various file formats compatible with common GIS software and analysis tools. Additionally, the data dissemination capabilities may include features for managing data access permissions, ensuring that sensitive or restricted geospatial information is only shared with authorized users or systems.
[0053] The system implements dynamic resource allocation and load balancing techniques to optimize performance and ensure scalability when handling increasing data volumes and user requests. As the volume of geospatial data and concurrent user interactions grow, the system continuously monitors resource utilization across its components, including CPU usage, memory consumption, storage capacity, and network bandwidth. Based on these real-time metrics, the system dynamically adjusts resource allocation, redistributing computational tasks and data processing workloads to available resources. This adaptive approach allows the system to efficiently utilize available hardware and maintain responsiveness even during peak usage periods or when processing large datasets.
[0054] To further enhance scalability, the system employs a distributed architecture that allows for horizontal scaling by adding additional processing nodes or storage units as needed. The load balancing mechanisms distribute incoming requests and data processing tasks across multiple nodes, ensuring that no single component becomes a bottleneck. Additionally, the system may implement caching strategies to reduce redundant computations and data retrieval operations, improving overall performance and reducing response times for frequently accessed data or common query results. These optimization and scalability features enable the system to accommodate growing data volumes and increasing numbers of users while maintaining efficient performance and responsiveness.
[0055] The systems and methods described herein may be implemented in any form of computing or electronic device. The term "computer," as used herein, encompasses any device with processing capabilities sufficient to execute instructions. This includes, but is not limited to, personal computers, servers, mobile devices, personal digital assistants, and similar devices.
[0056] Such devices may include one or more processors, such as microprocessors, controllers, or other suitable types of processors, capable of executing instructions to control the device's operation. For example, in some implementations using a system-on-a-chip architecture, the processors may include fixed-function blocks (hardware accelerators) that perform parts of the method in hardware rather than software or firmware. Platform software, such as an operating system or similar, may be installed to support the execution of application software.
[0057] The described functionality may be implemented in hardware, software, or any combination thereof. When implemented in software, the instructions or code can be stored on or transmitted via a computer-readable medium. Such media include computer-readable storage media, which may be volatile or non-volatile, removable or non-removable, and implemented using any technology for storing information such as program code, data structures, or other data. Examples include, but are not limited to, ROM, EEPROM, RAM, magnetic or optical storage, flash memory, or any other storage medium accessible by a computer. Communication media that facilitate the transfer of software, such as via coaxial cables, fiber optics, DSL, or wireless signals, may also be considered part of computer-readable media.
[0058] Alternatively, or in addition, some or all of the described functionality may be implemented using hardware logic components. Examples include, but are not limited to, application-specific integrated circuits, system-on-a-chip systems, field-programmable gate arrays, application-specific standard products, and complex programmable logic devices. In some cases, software instructions may also be implemented in dedicated circuits, such as programmable logic arrays or digital signal processors.
[0059] The computing device may operate as a standalone system or as part of a distributed system, where tasks are performed collectively by multiple devices connected via a network. Such devices may communicate over a network connection to perform the described functionality. For instance, software may be stored on a remote computer and accessed by a local device, which may download and execute portions of the software as needed. Similarly, some instructions may be processed locally, while others may execute on remote systems or networks. In some cases, the computing device may be remote and accessible via a communication interface. Storage of program instructions may also be distributed across a network or stored in a combination of local and remote locations. For example, software may reside on a remote computer and be accessed by a local terminal, or the system may execute some software locally while other components operate on remote servers.
[0060] Features of any of the examples or embodiments outlined above may be combined to create additional examples or embodiments without losing the intended effect. It should be understood that the description of an embodiment or example provided above is by way of example only, and various modifications could be made by one skilled in the art. Furthermore, one skilled in the art will recognise that numerous further modifications and combinations of various aspects are possible. Accordingly, the described aspects are intended to encompass all such alterations, modifications, and variations that fall within the scope of the appended claims.
[0061] While this invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
,CLAIMS:1. A computer-implemented method for hardware-aware management and dissemination of heterogeneous geospatial data, the method comprising:
i. acquiring heterogeneous geospatial data from a number of image providers;
ii. transmitted, by the data-importer module, the acquired heterogeneous geospatial data to a number of data sources for storing;
iii. ingesting the stored heterogeneous geospatial data by a raw data ingestor;
iv. storing the ingested heterogeneous geospatial data in the ingested data repository which enables streamlined access to the acquired geospatial information for further processing, analysis, and dissemination tasks;
v. initiates a user authentication process by an authentication module, upon accessing the system, by a user, via a web browser;
vi. scanning the data for obtaining a plurality of attributes by a raster optimizer module;
vii. converting the data into an optimized format for rendering using low resource allocation;
viii. analyzing, by the geospatial data base, the ingested data to identify and extract relevant metadata attributes; and
ix. analyzing and storing the extracted metadata in a structured format within a geospatial database, enabling efficient querying and retrieval of information;
x. retrieving data, by the user, based on one or more criteria, from the geospatial database.

2. The method as claimed in claim 1, wherein the number of image providers includes but not limited to satellites, UAVs, and aerial surveys.

3. The method as claimed in claim 1, wherein the heterogeneous geospatial data including but not limited to one of raster data, vector data, video data, and textual data.

4. The method as claimed in claim 1, wherein disseminating the processed geospatial data comprises:
implementing at least one of data compression, tiling, and progressive loading for efficient delivery of the processed geospatial data.

5. The method as claimed in claim 1, wherein the metadata attributes include but not limited to spatial resolution, coordinate system, acquisition date, sensor type, spectral bands, and pertinent details associated with the raster data.

6. The method as claimed in claim 1, wherein the data ingestion comprises the steps of:
a) validating the heterogeneous geospatial data;
b) converting the format of the data; and
c) extracting the metadata from the heterogeneous geospatial data.

7. A system for efficient web based heterogenous data management and dissemination, comprising:
a) a number of image providers configured to acquire heterogeneous geospatial data and transmits to a to a number of data sources;
b) a raw data ingestor along with a data-importer module configured to process the heterogeneous geospatial data of one or more formats;
c) an ingested data repository configured to store the processed heterogeneous geospatial data;
d) an authentication module configured to control user access;
e) a network load balancer module for managing network traffic and ensuring security and configured to-
• port exposure security; and
• dynamic network load allocation;
f) a raster optimizer module configured to scan for a plurality of attributes of the data and converts the data to an optimized format for rendering using low resource allocation; and
g) a central task manager module synchronizes and controls the overall task workflow of the application in a in a hardware aware ask monitor and accordingly allocates the resources for tasks.
8. The system as claimed in claim 7, wherein the number of data sources may include but not limited to system disks, external storage media and data repositories.

9. The system as claimed in claim 7, wherein the one or more formats include but not limited to rasters, videos, text files, and vector layers.

10. The system as claimed in claim 7, wherein the plurality of attributes include but not limited to resolution, color depth, compression format, and file size.