Description:
FIELD OF THE INVENTION
The present invention relates to the field of designing the optimised route for transmission lines. More specifically, it pertains to a system and method for identifying the least cost path of transmission lines using satellite imagery, machine learning algorithms, and AI-based heuristics. The invention leverages advanced geospatial analysis and artificial intelligence to automate and optimize the process of transmission line route planning, thereby improving accuracy, reducing turnaround time, and standardizing the survey process.

Application of the Invention
The invention is applicable in the field of transmission line survey and planning, particularly for utility companies, engineering firms, and government agencies involved in the design and construction of electrical transmission infrastructure. The transmission route designing and optimization system can be used to:

1. Automate the process of identifying the least cost path for transmission lines, reducing the need for manual field surveys and on-ground digitization of features.
2. Generate multiple route options based on satellite imagery, Statutory compliances and business rules, allowing for comparative analysis and selection of the most feasible route.
3. Improve the accuracy and standardization of transmission line surveys by leveraging machine learning algorithms and AI-based heuristics.
4. Enable the preparation of Bill of Materials, based on the optimized least cost path.
5. Provide real-time feedback and visualization tools for users to interact with and adjust the route generation process, enhancing decision-making and project management.
6. Track the progress of route generation activities and visualize the current status of various projects, facilitating better project oversight and coordination.
7. Ensure compliance with regulatory and environmental requirements by validating generated routes against relevant standards and guidelines.

Overall, the invention offers a comprehensive solution for optimizing transmission line route planning, resulting in significant time and cost savings, improved accuracy, and enhanced project management capabilities.

BACKGROUND OF THE INVENTION
Transmission line route planning is a critical component in the design and construction of electrical transmission infrastructure. Traditionally, this process involves manual field surveys, on-ground digitization of features, and the use of basic mapping tools such as basemaps. These methods are labor-intensive, time-consuming, and prone to human error, leading to inefficiencies and increased costs. The advent of satellite imagery and advancements in machine learning and artificial intelligence (AI) present an opportunity to revolutionize the transmission line route planning process by automating and optimizing it.

Prior Art Problems

1. Manual Effort and Time-Consuming: Traditional transmission line route planning relies heavily on manual field surveys and on-ground digitization of features, which are labor-intensive and time-consuming.
2. Inaccuracy and Human Error: Manual methods are prone to human error, leading to inaccuracies in the route planning process. This can result in suboptimal routes that increase project costs and timelines.
3. Limited Scalability: Managing multiple transmission line projects simultaneously is challenging with manual methods, leading to difficulties in scaling operations and handling large volumes of data.
4. Lack of Standardization: The manual nature of traditional methods results in a lack of standardization in the route planning process, leading to inconsistencies and variability in the quality of the output.
5. Limited Use of Advanced Technologies: Traditional methods do not fully leverage the capabilities of satellite imagery, machine learning, and AI, limiting the potential for optimization and automation.

Disadvantages of the Prior Art

1. High Costs: The labor-intensive nature of manual field surveys and on-ground digitization leads to high operational costs.
2. Extended Timelines: The time-consuming nature of manual methods results in extended project timelines, delaying the overall transmission line construction process.
3. Inconsistent Quality: The lack of standardization and reliance on human judgment result in inconsistent quality and accuracy of the route planning output.
4. Limited Flexibility: Manual methods offer limited flexibility in adjusting routes based on changing conditions or new data, leading to suboptimal decision-making.

Technical Solution of the Present Invention

The present invention provides a system that design optimised transmission route that leverages satellite imagery, machine learning algorithms, and AI-based heuristics to automate and optimize the transmission line route planning process. The system comprises:

1. Satellite Imagery Acquisition Module: Obtains low-resolution satellite images of the region of interest (ROI). Subsequently high-resolution satellite image is obtained for finalized area of interest (AOI).
2. Preprocessing Module: Enhances the quality of the acquired satellite images for analysis readiness.
3. Land Use Land Classification (LULC) Generation Module: Generates LULC data by processing satellite images using a machine learning-based LULC model.
4. Cost Raster Generation Module: Creates a cost raster based on the LULC data, statutory compliances and business rules.
5. Area of Interest (AOI) Generation Module: Generates multiple AOIs by evaluating specimen routes along with adjusting feature layer weights and business rules.
6. Route Generation Module: Generates multiple route options within the AOI using a vector-based pathfinding algorithm and AI-based heuristics. This additionally calculates the required number of towers with their types thereby aiding in the BoQ generation
7. User Interface: Allows user interaction to adjust feature layer buffers and weights, upload custom feature layers, and modify transmission towers along the generated routes.
8. Project Tracking Module: Tracks the progress of route generation activities and visualizes the current status of various projects.
9. Explainability Feature: Provides visibility to the user regarding route generation decisions and suggestions for adjustments.
10. Reporting Module: Generates system-based reports for comparative analysis of stacked AOIs , routes and compare route length and tower types.

Technical Effect
The technical effect of the present invention includes:

1. Increased Efficiency: Automating the route planning process significantly reduces the time and effort required, leading to faster project completion.
2. Improved Accuracy: Leveraging high-resolution satellite imagery and machine learning algorithms enhances the accuracy of the route planning process, reducing the likelihood of errors.
3. Scalability: The system can handle multiple transmission line projects simultaneously, improving scalability and operational efficiency.
4. Standardization: The use of business rules and automated processes ensures a standardized and consistent approach to route planning.
5. Optimization: The AI-based heuristics and machine learning models optimize the route planning process, resulting in the identification of the least cost path.

Need of the Present Invention

The present invention addresses the critical need for a more efficient, accurate, and scalable solution for transmission line route planning. By automating and optimizing the process, the invention overcomes the limitations of traditional manual methods, leading to significant time and cost savings, improved accuracy, and enhanced project management capabilities. The invention is particularly valuable for utility companies, engineering firms, and government agencies involved in the design and construction of electrical transmission infrastructure, enabling them to achieve better outcomes and maintain a competitive edge in the industry.

OBJECT OF THE INVENTION
The primary object of the present invention is to provide a system that design optimised transmission route that automates and optimizes the process of identifying the least cost path for transmission lines using high-resolution satellite imagery, machine learning algorithms, and AI-based heuristics.

Specific objects of the invention include:

1. To Automate Route Planning: To develop a system that automates the transmission line route planning process, thereby reducing the need for manual field surveys and on-ground digitization of features.
2. To Improve Accuracy: To enhance the accuracy of transmission line route planning by leveraging high-resolution satellite imagery and advanced machine learning algorithms.
3. To Reduce Turnaround Time: To significantly reduce the time required to evaluate and generate optimized transmission line routes, enabling faster project completion.
4. To Standardize the Process: To ensure a standardized and consistent approach to transmission line route planning by using business rules, statutory compliances and automated processes.
5. To Optimize Costs: To identify the least cost path for transmission lines by optimizing the route planning process using AI-based heuristics and machine learning models.
6. To Enhance User Interaction: To provide a user-friendly interface that allows users to interact with the system, adjust feature layer weights and buffers, upload custom feature layers, and modify transmission towers along the generated routes.
7. To Enable Comparative Analysis: To generate multiple route options and provide tools for comparative analysis, allowing users to select the most feasible and cost-effective route.
8. To Track Project Progress: To include a project tracking module that visualizes the current status of various projects and tracks the progress of route generation activities.
9. To Provide Explainability: To incorporate an explainability feature that provides visibility to users regarding route generation decisions and suggestions for adjustments.
10. To Generate Reports: To generate system-based reports that summarize key metrics and provide comparative analysis of stacked AOIs and routes, including feasibility of installations and time/cost implications.
11. To Integrate with External Data Sources: To enable integration with external geographic information system (GIS) databases and other relevant data sources to enhance the accuracy and comprehensiveness of the route planning process.
12. To Ensure Compliance: To validate generated routes against business logics based on regulatory and environmental compliance requirements, ensuring complete adherence to relevant standards and guidelines.
13. To Provide Scalability: To develop a scalable system capable of handling multiple transmission line projects simultaneously, improving operational efficiency and project management capabilities.

By achieving these objects, the present invention aims to revolutionize the transmission line route planning process, resulting in significant time and cost savings, improved accuracy, and enhanced project management capabilities for utility companies, engineering firms, and government agencies involved in the design and construction of electrical transmission infrastructure.

SUMMARY OF THE INVENTION
The present invention provides a system that design optimised transmission route designed to automate and optimize the process of identifying the least cost path for transmission lines. The system leverages high-resolution satellite imagery, machine learning algorithms, and AI-based heuristics to enhance the accuracy, efficiency, and standardization of transmission line route planning.

The system comprises the following key components:

1. Satellite Imagery Acquisition Module: This module is responsible for obtaining high-resolution satellite images of the region of interest (ROI).
2. Preprocessing Module: This module preprocesses the acquired satellite images to enhance their quality for analysis readiness. Preprocessing steps include image correction, noise reduction, and enhancement.
3. Land Use Land Classification (LULC) Generation Module: This module generates LULC data by processing the satellite images using a machine learning-based LULC model. The module defines the ROI, prepares feature layers, and processes the images to generate LULC data.
4. Cost Raster Generation Module: This module creates a cost raster based on the LULC data, statutory compliances required and business rules, including feature buffers, crossing spans, weights and Right of Way (ROW) Cost. The cost raster is dynamically updated based on changes in feature layer weights and buffers. ML algorithm also identifies decision logics from existing transmission lines.
5. Area of Interest (AOI) Generation Module: This module generates multiple AOIs by evaluating specimen routes and adjusting feature layer weights. The AOIs are compared based on area statistics of feature layers to determine the most probable areas for the transmission line.
6. Route Generation Module: This module generates multiple route options within the AOI using a vector-based pathfinding algorithm and AI-based heuristics. The routes are compared based on total route length, transmission tower types, and Right of Way (RoW) costs to determine the optimized least cost path.
7. User Interface: The user interface allows users to interact with the system, adjust feature layer buffers and weights, upload custom feature layers, and modify transmission towers along the generated routes. The interface provides real-time feedback and visualization tools for enhanced decision-making.
8. Project Tracking Module: This module tracks the progress of route generation activities and visualizes the current status of various projects using a timeline-based visualization.
9. Explainability Feature: This feature provides visibility to users regarding route generation decisions, including reasons for route termination and suggestions for adjusting weights or buffers to achieve route convergence.
10. Reporting Module: This module generates system-based reports that summarize key metrics and provide comparative analysis of stacked AOIs and routes, including feasibility of installations and time/cost implications.

The method of the present invention involves acquiring satellite images, preprocessing the images, generating LULC data, creating a cost raster, generating AOIs, generating multiple route options, displaying the routes on a user interface, tracking project progress, providing explainability, and generating reports.

The technical effect of the present invention includes increased efficiency, improved accuracy, scalability, standardization, adherence to rules and optimization of the transmission line route planning process. The invention addresses the limitations of traditional manual methods, resulting in significant time and cost savings, enhanced project management capabilities, and better outcomes for utility companies, engineering firms, and government agencies involved in the design and construction of electrical transmission infrastructure.

Overall, the present invention provides a comprehensive solution for designing a optimised transmission route, revolutionizing the transmission line route planning process and enabling the preparation of Bill of Materials. The optimized route and tower placement results in significant cost savings by way of reduction in procurement of various materials, with steel being a major contributor.

Thus in accordance with an aspect of the present invention there is provided a system to design optimised transmission route that identify the least cost path of a transmission line, comprising:
a. a satellite imagery acquisition module configured to obtain satellite images of a region of interest (ROI);
b. a preprocessing module configured to preprocess the acquired satellite images for analysis readiness;
c. a Land Use Land Classification (LULC) generation module configured to:
i. define the ROI and prepare feature layers including forest, transmission lines, wildlife sanctuaries, and other relevant features;

ii. process the satellite images to generate LULC data using a machine learning-based LULC model;

iii. Enhance LULC data by further processing high-resolution satellite images with the machine learning-based LULC model;

d. a cost raster generation module configured to create a cost raster based on the LULC data and business rules, including feature buffers, crossing spans, weights and Right of Way (ROW) Cost;
e. an Area of Interest (AOI) generation module configured to:
i. evaluate specimen routes to determine probable areas for the transmission line;
ii. generate multiple AOIs by adjusting feature layer weights and comparing area statistics of feature layers;

f. a route generation module configured to:
i. define beeline coordinates by specifying start and end points;

ii. generate multiple route options within the AOI by applying a vector-based pathfinding algorithm using AI-based heuristics and adjusting feature layer weights;

iii. compare the generated routes based on total route length, transmission tower types, Right of Way (ROW) cost and respective costs to determine the optimized least cost path;

g. a user interface configured to:

i. display the generated routes and allow user interaction to adjust feature layer buffers and weights;

ii. upload custom feature layers;

iii. add, delete, or move transmission towers along the generated routes;

h. a project tracking module configured to track the progress of route generation activities and visualize the current status of various projects;
i. an explainability feature configured to provide visibility to the user regarding route generation decisions, including reasons for route termination and suggestions for adjusting weights or buffers to achieve route convergence;
j. a reporting module configured to generate system-based reports for comparative analysis of stacked AOIs and routes, including feasibility of installations and time/cost implications;
wherein the system is configured to generate optimized least cost paths for transmission lines by leveraging high-resolution satellite imagery, machine learning algorithms, and AI-based heuristics, thereby reducing the turnaround time for route evaluation, improving the accuracy and standardization of the transmission line survey process, and enabling the preparation of Bill of Materials.

In another aspect of the present invention there is provided a method for identifying the least cost path of a transmission line using a digital system, comprising:
a. acquiring high-resolution satellite images of a region of interest (ROI);
b. preprocessing the acquired satellite images to enhance their quality for analysis readiness;
c. generating Land Use Land Classification (LULC) data by:

i. defining the ROI and preparing feature layers including forest, transmission lines, wildlife sanctuaries, and other relevant features;

ii. processing the satellite images to generate low-resolution LULC data using a machine learning-based LULC model;

iii. generating high-resolution LULC data by further processing high-resolution satellite images with the machine learning-based LULC model;

d. creating a cost raster based on the LULC data and decision logics, including feature buffers, crossing spans, weights and Right of Way (ROW) Cost, Wherein ML algorithm also learns decision logics from existing constructed lines;
e. generating an Area of Interest (AOI) by:
i. evaluating specimen routes to determine probable areas for the transmission line;

ii. generating multiple AOIs by adjusting feature layer weights and comparing area statistics of feature layers;

f. generating multiple route options within the AOI by:

i. defining beeline coordinates by specifying start and end points;

ii. applying a vector-based pathfinding algorithm using AI-based heuristics and adjusting feature layer weights;

iii. comparing the generated routes based on total length, tower types, and respective costs to determine the optimized least cost path;

g. displaying the generated routes on a user interface and allowing user interaction to:

i. adjust feature layer buffers and weights;

ii. upload custom feature layers;

iii. add, delete, or move transmission towers along the generated routes;

h. tracking the progress of route generation activities and visualizing the current status of various projects;
i. providing visibility to the user regarding route generation decisions, including reasons for route termination and suggestions for adjusting weights or buffers to achieve route convergence;
j. generating system-based reports for comparative analysis of stacked AOIs and routes, including feasibility of installations and time/cost implications;
wherein the method leverages high-resolution satellite imagery, machine learning algorithms, and AI-based heuristics to generate optimized least cost paths for transmission lines, thereby reducing the turnaround time for route evaluation, improving the accuracy and standardization of the transmission line survey process, and enabling the preparation of Bill of Materials.

Other aspects, advantages, and salient features of the invention will become apparent to those skilled in the art from the following detailed description, which, taken in conjunction with the annexed drawings, discloses exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
The accompanying figures illustrate various components of the system to design optimised transmission route. These figures are provided to enhance the understanding of the invention and are not intended to limit its scope.

Figure 1: System Architecture Diagram

This figure illustrates the overall architecture of the system to design optimised transmission route. It includes the key components such as the satellite imagery acquisition module, preprocessing module, Land Use Land Classification (LULC) generation module, cost raster generation module, Area of Interest (AOI) generation module, route generation module, user interface, project tracking module, explainability feature, and reporting module. The diagram shows the interaction and data flow between these components.

Figure 2: Flowchart of the Method

This figure presents a flowchart detailing the step-by-step process of the method for identifying the least cost path of a transmission line. It includes steps such as acquiring satellite images, preprocessing images, generating LULC data, creating a cost raster, generating AOIs, generating multiple route options, displaying routes, tracking project progress, providing explainability, and generating reports.

Figure 3: Satellite Imagery Acquisition

This figure depicts the process of acquiring high-resolution satellite images of the region of interest (ROI). It shows the coverage area and resolution of the images obtained by the satellite imagery acquisition module, highlighting the importance of high-resolution data for accurate route planning.

Figure 4: Preprocessing of Satellite Images

This figure illustrates the preprocessing steps applied to the acquired satellite images. It includes image correction, noise reduction, and enhancement processes that improve the quality of the images for subsequent analysis and LULC generation.

Figure 5: Land Use Land Classification (LULC) Generation

This figure shows the process of generating LULC data from the satellite images. It includes defining the ROI, preparing feature layers, and using a machine learning-based LULC model to generate LULC data. The figure highlights the classification of different land use and land cover types.

Figure 6: Cost Raster Generation

This figure illustrates the creation of a cost raster based on the LULC data, statutory compliances and decision logics. It shows how feature buffers, crossing spans, weights and Right of Way (ROW) Cost are applied to generate a dynamic cost raster that influences route planning decisions.

Figure 7: Area of Interest (AOI) Generation

This figure depicts the generation of multiple AOIs by evaluating specimen routes and adjusting feature layer weights. It includes a comparison of area statistics of feature layers to determine the most probable areas for the transmission line. The figure highlights the flexibility and adaptability of the AOI generation process.

Persons skilled in the art will appreciate that elements in the figures are illustrated for simplicity and clarity and may not have been drawn to scale. For example, the dimensions of some of the elements in the figure may be exaggerated relative to other elements to help to improve understanding of various exemplary embodiments of the present disclosure.

DETAILED DESCRITION OF THE INVENTION
The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of various embodiments of the present disclosure as defined by the claims and their equivalents. It includes various specific details to assist in that understanding, but these are to be regarded as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the various embodiments described herein can be made without departing from the scope and spirit of the present disclosure. In addition, descriptions of well-known functions and constructions may be omitted for clarity and conciseness.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments belong. Further, the meaning of terms or words used in the specification and the claims should not be limited to the literal or commonly employed sense but should be construed in accordance with the spirit of the disclosure to most properly describe the present disclosure.

The terminology used herein is for the purpose of describing particular various embodiments only and is not intended to be limiting of various embodiments. 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" used herein specify the presence of stated features, integers, steps, operations, members, components, and/or groups thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, members, components, and/or groups thereof.
The present disclosure will now be described more fully with reference to the accompanying drawings, in which various embodiments of the present disclosure are shown.

The present invention relates to a system that design a optimised transmission route to automate and optimize the process of identifying the least cost path for transmission lines. The system leverages satellite imagery, machine learning algorithms, and AI-based heuristics to enhance the accuracy, efficiency, and standardization of transmission line route planning.

System Architecture

The system comprises the following key components:

1. Satellite Imagery Acquisition Module: This module is responsible for obtaining high-resolution satellite images of the region of interest (ROI).
2. Preprocessing Module: This module preprocesses the acquired satellite images to enhance their quality for analysis readiness. Preprocessing steps include image correction, noise reduction, and enhancement.
3. Land Use Land Classification (LULC) Generation Module: This module generates LULC data by processing the satellite images using a machine learning-based LULC model. The module defines the ROI, prepares feature layers, and processes the images to generate LULC data.
11. Cost Raster Generation Module: This module creates a cost raster based on the LULC data, statutory compliances required and business rules, including feature buffers, crossing spans, weights and Right of Way (ROW) Cost. The cost raster is dynamically updated based on changes in feature layer weights and buffers. ML algorithm is also trained for Business rules from existing transmission lines.
4. Area of Interest (AOI) Generation Module: This module generates multiple AOIs by evaluating specimen routes and adjusting feature layer weights. The AOIs are compared based on area statistics of feature layers to determine the most probable areas for the transmission line.
5. Route Generation Module: This module generates multiple route options within the AOI using a vector-based pathfinding algorithm and AI-based heuristics. The routes are compared based on total route length, transmission tower types, and Right of Way (ROW) costs to determine the optimized least cost path.
6. User Interface: The user interface allows users to interact with the system, adjust feature layer buffers and weights, upload custom feature layers, and modify transmission towers along the generated routes. The interface provides real-time feedback and visualization tools for enhanced decision-making.
7. Project Tracking Module: This module tracks the progress of route generation activities and visualizes the current status of various projects using a timeline-based visualization.
8. Explainability Feature: This feature provides visibility to users regarding route generation decisions, including reasons for route termination and suggestions for adjusting weights or buffers to achieve route convergence.
9. Reporting Module: This module generates system-based reports that summarize key metrics and provide comparative analysis of stacked AOIs and routes, including feasibility of installations and time/cost implications.

Method for Identifying the Least Cost Path

The method for identifying the least cost path of a transmission line using digital system involves the following steps:

1. Acquiring High-Resolution Satellite Images: The satellite imagery acquisition module obtains high-resolution satellite images of the ROI
2. Preprocessing the Acquired Satellite Images: The preprocessing module enhances the quality of the acquired satellite images by performing image correction, noise reduction, and enhancement.
3. Generating Land Use Land Classification (LULC) Data: The LULC generation module processes the satellite images to generate LULC data. This involves defining the ROI, preparing feature layers, and using a machine learning-based LULC model to generate both low-resolution and high-resolution LULC data.
4. Creating a Cost Raster: The cost raster generation module creates a cost raster based on the LULC data and business rules, including feature buffers, crossing spans, weights and Right of Way (ROW) cost. The cost raster is dynamically updated based on changes in feature layer weights and buffers.
5. Generating Areas of Interest (AOIs): The AOI generation module generates multiple AOIs by evaluating specimen routes and adjusting feature layer weights. The AOIs are compared based on area statistics of feature layers to determine the most probable areas for the transmission line.
6. Generating Multiple Route Options: The route generation module generates multiple route options within the AOI using a vector-based pathfinding algorithm and AI-based heuristics. The routes are compared based on total length, tower types, and respective costs to determine the optimized least cost path.
7. Displaying the Generated Routes: The user interface displays the generated routes and allows users to interact with the system, adjust feature layer buffers and weights, upload custom feature layers, and modify transmission towers along the generated routes. The interface provides real-time feedback and visualization tools for enhanced decision-making.
8. Tracking Project Progress: The project tracking module tracks the progress of route generation activities and visualizes the current status of various projects using a timeline-based visualization.
9. Providing Explainability: The explainability feature provides visibility to users regarding route generation decisions, including reasons for route termination and suggestions for adjusting weights or buffers to achieve route convergence.
10. Generating Reports: The reporting module generates system-based reports that summarize key metrics and provide comparative analysis of stacked AOIs and routes, including feasibility of installations and time/cost implications.

Embodiments of the Invention

Embodiment 1: High-Resolution Satellite Imagery Acquisition
In this embodiment, the satellite imagery acquisition module obtains high-resolution satellite images of the ROI. The high-resolution images provide detailed information about the terrain and features within the ROI, enabling accurate LULC generation and cost raster creation.

Embodiment 2: Preprocessing of Satellite Images
In this embodiment, the preprocessing module enhances the quality of the acquired satellite images by performing image correction, noise reduction, and enhancement. These preprocessing steps improve the accuracy of the LULC generation and subsequent route planning processes.

Embodiment 3: Machine Learning-Based LULC Generation
In this embodiment, the LULC generation module uses a machine learning-based LULC model to process the satellite images and generate LULC data. The model is trained using a dataset comprising various terrain types to improve classification accuracy. The module generates LULC data, providing detailed information about land use and land cover within the ROI.

Embodiment 4: Dynamic Cost Raster Generation
In this embodiment, the cost raster generation module creates a cost raster based on the LULC data, statutory compliances and business rules, including feature buffers, crossing spans, weights and Right of Way (ROW) cost. The cost raster is dynamically updated based on changes in feature layer weights and buffers, allowing for real-time adjustments and optimization of the route planning process.

Embodiment 5: AOI Generation and Comparative Analysis
In this embodiment, the AOI generation module generates multiple AOIs by evaluating specimen routes and adjusting feature layer weights. The AOIs are compared based on area statistics of feature layers to determine the most probable areas for the transmission line. The user interface provides tools for comparative analysis, allowing users to visualize and compare stacked AOIs.

Embodiment 6: AI-Based Route Generation
The AI-based route generation module is a critical component of system which designs a optimised transmission route. This module leverages advanced artificial intelligence (AI) techniques and heuristics to generate multiple route options for transmission lines within a defined Area of Interest (AOI). The goal is to identify the optimized least cost path by considering various factors such as total route length, transmission tower types, and right of way cost. The AI-based route generation process involves several key steps and features, which are described in detail below.

1. Defining Beeline Coordinates
The first step in the AI-based route generation process is to define the beeline coordinates. This involves specifying the start and end points of the transmission line. The beeline represents the direct, straight-line path between these two points. The beeline coordinates serve as a reference for generating potential routes within the AOI.

2. Vector-Based Pathfinding Algorithm
The core of the AI-based route generation module is a vector-based pathfinding algorithm. This algorithm uses AI-based heuristics to explore possible points in vector space for placing transmission towers and building the route. The algorithm operates as follows:

• Initialization: The algorithm initializes by setting the start point as the initial node and the end point as the target node.
• Exploration: The algorithm explores the vector space by generating potential points (nodes) for placing transmission towers. It evaluates each point based on predefined criteria such as distance, terrain, and cost.
• Heuristics: AI-based heuristics are applied to guide the exploration process. These heuristics consider factors such as minimizing the total route length, avoiding obstacles, and optimizing the placement of transmission towers.
• Pathfinding: The algorithm searches for the optimal path by connecting the nodes to form a continuous route from the start point to the end point. It evaluates multiple potential routes in parallel to identify the best possible outcome.

3. Generating Multiple Route Options
The AI-based route generation module generates multiple route options within the AOI by adjusting feature layer weights and applying the vector-based pathfinding algorithm. The process involves the following steps:

• Feature Layer Weights: The system allows users to adjust the weights and right of way cost assigned to different feature layers (e.g., forest, transmission lines, wildlife sanctuaries). These weights and Right of Way (ROW) cost influence the overall cost and feasibility of routing through specific areas.
• Route Generation: The algorithm generates multiple route options by varying the feature layer weights and evaluating the resulting paths. Each route option is assessed based on predefined criteria such as total length, number of transmission towers, and right of way costs.
• Comparison: The generated routes are compared to determine the optimized least cost path. The comparison considers factors such as total route length, tower types, right of way cost and overall cost.
• Tower Placement: Prepares the tower placements based on the angle and crossing rules. Optimization is carried out between adjacent towers in small patches to further eliminate towers wherever possible and fine tune the route.
• Optimization: The AI model learns from past user behaviour to arrive at the final route and tower schedule in most optimized manner to have the least cost path.

4. Real-Time Feedback and Visualization
The user interface provides real-time feedback and visualization tools to enhance the route generation process. Users can interact with the system to:

• Adjust Weights and Buffers: Users can dynamically adjust the weights and Right of Way (ROW) cost assigned to feature layers and the buffers around specific features. These adjustments influence the cost raster and the resulting route options.
• Upload Custom Feature Layers: Users can upload custom feature layers to incorporate additional data into the route generation process. This allows for greater flexibility and customization.
• Modify Transmission Towers: Users can add, delete, or move transmission towers along the generated routes. This manual intervention allows for fine-tuning and optimization of the final route.

5. Explainability and Decision Support
The AI-based route generation module includes an explainability feature that provides visibility to users regarding route generation decisions. This feature enhances user understanding and supports informed decision-making by:

• Explaining Route Termination: The system provides explanations for why a particular route may have terminated prematurely. It identifies the specific factors (e.g., feature weights, buffers) that influenced the decision.
• Suggestions for Adjustments: The system offers suggestions for adjusting weights or buffers to achieve route convergence. This helps users make informed decisions to optimize the route.

6. Comparative Analysis and Reporting
The AI-based route generation module supports comparative analysis and reporting by:

• Comparing Routes: The system compares multiple generated routes based on key metrics such as total length, number of transmission towers, and respective costs. This comparison helps identify the most feasible and cost-effective route.
• Generating Reports: The reporting module generates detailed reports that summarize the key metrics and provide comparative analysis of the generated routes. These reports support project planning and management by providing insights into the route planning process.

Embodiment 7: User Interaction and Real-Time Feedback
In this embodiment, the user interface allows users to interact with the system, adjust feature layer buffers and weights, upload custom feature layers, and modify transmission towers along the generated routes. The interface provides real-time feedback and visualization tools, enhancing decision-making and allowing for dynamic adjustments to the route planning process.

Embodiment 8: Project Tracking and Visualization
In this embodiment, the project tracking module tracks the progress of route generation activities and visualizes the current status of various projects using a timeline-based visualization. This feature provides project managers with an overview of ongoing activities and helps in coordinating and managing multiple transmission line projects simultaneously.

Embodiment 9: Explainability and Decision Support
In this embodiment, the explainability feature provides visibility to users regarding route generation decisions, including reasons for route termination and suggestions for adjusting weights or buffers to achieve route convergence. This feature enhances user understanding and supports informed decision-making.

Embodiment 10: Comprehensive Reporting
In this embodiment, the reporting module generates system-based reports that summarize key metrics and provide comparative analysis of stacked AOIs and routes, including feasibility of installations and time/cost implications. The reports support project planning and management by providing detailed insights into the route planning process.

Technical Effect
The technical effect of the present invention includes increased efficiency, improved accuracy, scalability, standardization, and optimization of the transmission line route planning process. The invention addresses the limitations of traditional manual methods, resulting in significant time and cost savings, enhanced project management capabilities, and better outcomes for utility companies, engineering firms, and government agencies involved in the design and construction of electrical transmission infrastructure.

The present invention provides a comprehensive solution to design optimised transmission route, revolutionizing the transmission line route planning process. By leveraging high-resolution satellite imagery, machine learning algorithms, and AI-based heuristics, the system automates and optimizes the identification of the least cost path for transmission lines, resulting in significant time and cost savings, improved accuracy, and enhanced project management capabilities. The invention enables the preparation of Bill of Materials and supports informed decision-making through real-time feedback, explainability, and comprehensive reporting.

Detailed Description of the Figures

Figure 1: System Architecture Diagram

Figure 1 illustrates the comprehensive architecture of the system used to designed optimized transmission route. The diagram provides a visual representation of the key components and their interactions within the system. The components include:

• Satellite Imagery Acquisition Module: Responsible for obtaining high-resolution satellite images of the region of interest (ROI). This module interfaces with satellite data providers to ensure the acquisition of satellite images
• Preprocessing Module: Enhances the quality of the acquired satellite images through processes such as image correction, noise reduction, and enhancement. This module prepares the images for further analysis.
• Land Use Land Classification (LULC) Generation Module: Utilizes a machine learning-based LULC model to process the satellite images and generate LULC data. This module defines the ROI, prepares feature layers, and produces LULC data.
12. Cost Raster Generation Module: Creates a cost raster based on the LULC data and business rules, including feature buffers, crossing spans, weights and Right of Way (ROW) Cost. The cost raster is dynamically updated to reflect changes in feature layer weights and buffers. ML algorithm also identifies decision logics from existing transmission lines.
• Area of Interest (AOI) Generation Module: Generates multiple AOIs by evaluating specimen routes and adjusting feature layer weights. This module compares area statistics of feature layers to identify the most probable areas for the transmission line.
• Route Generation Module: Employs a vector-based pathfinding algorithm and AI-based heuristics to generate multiple route options within the AOI. The module compares routes based on total route length, transmission tower types, and Right of Way (RoW) costs determine the optimized least cost path.
• User Interface: Provides an interactive platform for users to adjust feature layer buffers and weights, upload custom feature layers, and modify transmission towers along the generated routes. The interface offers real-time feedback and visualization tools.
• Project Tracking Module: Tracks the progress of route generation activities and visualizes the current status of various projects using a timeline-based visualization.
• Explainability Feature: Offers visibility into route generation decisions, including reasons for route termination and suggestions for adjustments.
• Reporting Module: Generates system-based reports that summarize key metrics and provide comparative analysis of stacked AOIs and routes.

Figure 2: Flowchart of the Method

Figure 2 presents a detailed flowchart outlining the method for identifying the least cost path of a transmission line. The flowchart includes the following steps:

• Acquiring High-Resolution Satellite Images: The process begins with the acquisition of high-resolution satellite images of the ROI.
• Preprocessing the Acquired Satellite Images: The images undergo preprocessing to enhance their quality for analysis readiness.
• Generating Land Use Land Classification (LULC) Data: The LULC generation module processes the satellite images to produce LULC data, defining the ROI and preparing feature layers.
• Creating a Cost Raster: A cost raster is created based on the LULC data and decision logics, dynamically updated to reflect changes in feature layer weights and buffers.
• Generating Areas of Interest (AOIs): Multiple AOIs are generated by evaluating specimen routes and adjusting feature layer weights.
• Generating Multiple Route Options: The route generation module generates multiple route options within the AOI using a vector-based pathfinding algorithm and AI-based heuristics.
• Displaying the Generated Routes: The user interface displays the generated routes, allowing users to interact with the system and make adjustments.
• Tracking Project Progress: The project tracking module monitors the progress of route generation activities and visualizes the current status of projects.
• Providing Explainability: The explainability feature provides insights into route generation decisions and suggestions for adjustments.
• Generating Reports: The reporting module generates comprehensive reports that summarize key metrics and provide comparative analysis.

Figure 3: Satellite Imagery Acquisition

Figure 3 depicts the process of acquiring high-resolution satellite images of the region of interest (ROI). The figure highlights the following aspects:

• Coverage Area: The satellite imagery acquisition module ensures comprehensive coverage of the ROI, capturing detailed information about the terrain and features.
• Resolution: The images are acquired with a resolution of 10 meters or better, providing high accuracy for subsequent analysis and route planning.
• Data Sources: The module interfaces with satellite data providers to obtain the necessary imagery, ensuring timely and reliable data acquisition.

Figure 4: Preprocessing of Satellite Images

Figure 4 illustrates the preprocessing steps applied to the acquired satellite images. The figure includes:

• Image Correction: The preprocessing module corrects any distortions or anomalies in the images to ensure accuracy.
• Noise Reduction: The module reduces noise in the images to enhance clarity and detail.
• Enhancement: The images are enhanced to improve their quality and readiness for analysis, ensuring optimal input for the LULC generation module.

Figure 5: Land Use Land Classification (LULC) Generation

Figure 5 shows the process of generating LULC data from the satellite images. The figure highlights:
• Defining the ROI: The LULC generation module defines the region of interest and prepares feature layers, including forest, transmission lines, wildlife sanctuaries, and other relevant features.
• Machine Learning-Based LULC Model: The module uses a machine learning-based LULC model to process the satellite images and generate both low-resolution and high-resolution LULC data.
• Classification: The figure illustrates the classification of different land use and land cover types, providing detailed information for route planning.

Figure 6: Cost Raster Generation

Figure 6 illustrates the creation of a cost raster based on the LULC data and decision logics. The figure includes:

• Feature Buffers and Crossing Spans: The cost raster generation module applies feature buffers and crossing spans to the LULC data, influencing route planning decisions.
• Weights: The module assigns weights to different features, dynamically updating the cost raster based on changes in feature layer weights and buffers.
• Dynamic Updates: The figure shows how the cost raster is continuously updated to reflect real-time adjustments, ensuring accurate and optimized route planning.

Figure 7: Area of Interest (AOI) Generation

Figure 7 depicts the generation of multiple AOIs by evaluating specimen routes and adjusting feature layer weights. The figure highlights:

• Specimen Routes: The AOI generation module evaluates specimen routes to determine probable areas for the transmission line.
• Feature Layer Weights: The module adjusts feature layer weights to generate multiple AOIs, allowing for flexibility and adaptability in route planning.
• Comparative Analysis: The figure illustrates the comparison of area statistics of feature layers to identify the most probable areas for the transmission line, supporting informed decision-making.

Advantages of the Invention

1. Increased Efficiency: The system automates the transmission line route planning process, significantly reducing the time and effort required to generate optimized routes. This leads to faster project completion and improved operational efficiency.

2. Improved Accuracy: By leveraging high-resolution satellite imagery and machine learning algorithms, the system enhances the accuracy of the route planning process. It provides precise land use and land cover classification, accurate cost rasters, and optimized route options, minimizing the likelihood of errors.

3. Cost Savings: The system identifies the least cost path for transmission lines by optimizing the route planning process using AI-based heuristics and machine learning models. This results in significant cost savings in terms of material procurement, construction, and overall project expenses.

4. Scalability: The system is capable of handling multiple transmission line projects simultaneously, improving scalability and allowing organizations to manage large volumes of data and projects more effectively.

5. Standardization: The use of decision logics and automated processes ensures a standardized and consistent approach to transmission line route planning. This reduces variability and improves the quality of the output, leading to more reliable and predictable project outcomes.

6. Enhanced Decision-Making: The user interface provides real-time feedback, visualization tools, and explainability features that enhance decision-making. Users can interact with the system, adjust parameters, and receive insights into route generation decisions, leading to more informed and effective planning.

7. Regulatory Compliance: The system can validate generated routes against regulatory and environmental compliance requirements, ensuring adherence to relevant standards and guidelines. This reduces the risk of non-compliance and associated penalties.

8. Comprehensive Reporting: The reporting module generates detailed reports that provide comparative analysis and key metrics. These reports support project planning and management by offering valuable insights into the route planning process, facilitating better oversight and coordination.

9. Flexibility and Customization: The system allows users to adjust feature layer weights, upload custom feature layers, and modify transmission towers along the generated routes. This flexibility enables users to tailor the route planning process to specific project requirements and constraints.

10. Environmental Considerations: By incorporating environmental data and considerations into the route planning process, the system helps minimize the environmental impact of transmission line construction, supporting sustainable development practices.

Overall, the invention provides a comprehensive and advanced approach to transmission line route planning, offering significant improvements in terms of efficiency, accuracy, cost savings, and project management capabilities. It is a valuable tool for utility companies, engineering firms, and government agencies involved in the design and construction of electrical transmission infrastructure.

The descriptions and illustrations provided in this document are intended to explain the principles of the invention and its best mode of working. They are not intended to limit the scope of the invention, which is defined by the claims. Variations and modifications to the described embodiments may be made without departing from the scope of the invention. The specific embodiments described in this document are examples of the invention and are not intended to limit the scope of the claims. The claims should be interpreted broadly to cover all equivalent structures and methods that fall within the scope of the invention. The technical specifications and details provided in this document are for illustrative purposes only. Actual implementations of the invention may vary based on specific design requirements, manufacturing processes, and application needs.

Any references to prior art documents, patents, or publications are provided for informational purposes only. The inclusion of such references does not imply that the present invention is limited by or dependent on the prior art. While these embodiments illustrate different aspects and processes of the invention, the architecture depicted is a conceptual representation and may vary based on specific implementation requirements and technological advancements. Users should ensure compatibility with existing systems and infrastructure. The process flow is illustrative and may require adaptation to accommodate different geographic regions, data availability, and project-specific constraints. It is to be noted that the accuracy and reliability of satellite data are contingent upon the capabilities of the satellite sensors and the quality of the data transmission. Users should verify the data integrity and consider potential limitations in data resolution and coverage. While the pre-processing and LULC generation steps are based on current machine learning models and image processing techniques. Users should be aware that advancements in technology may necessitate updates to these processes. The data harmonization and cleaning processes are critical for ensuring accurate analysis. Users should implement robust data validation and quality control measures to maintain data integrity. The initialization of the geospatial database and the calculation of suitability and path cost rasters are dependent on the accuracy of input data and the effectiveness of the applied business rules. The initialization of the geospatial database and the calculation of suitability and path cost rasters are dependent on the accuracy of input data and the effectiveness of the applied business rules. The route optimization algorithm and validation steps are designed to provide optimal solutions based on available data and defined criteria.
, Claims:
1. A system to design optimised transmission route that identify the least cost path of a transmission line, comprising:
a. a satellite imagery acquisition module configured to obtain satellite images of a region of interest (ROI);
b. a preprocessing module configured to preprocess the acquired satellite images for analysis readiness;
c. a Land Use Land Classification (LULC) generation module configured to:
i. define the ROI and prepare feature layers including forest, transmission lines, wildlife sanctuaries, and other relevant features;

ii. process the satellite images to generate LULC data using a machine learning-based LULC model;

iii. Enhance LULC data by further processing high-resolution satellite images with the machine learning-based LULC model;

d. a cost raster generation module configured to create a cost raster based on the LULC data and business rules, including feature buffers, crossing spans, weights and Right of Way (ROW) Cost;
e. an Area of Interest (AOI) generation module configured to:
i. evaluate specimen routes to determine probable areas for the transmission line;
ii. generate multiple AOIs by adjusting feature layer weights and comparing area statistics of feature layers;

f. a route generation module configured to:
i. define beeline coordinates by specifying start and end points;

ii. generate multiple route options within the AOI by applying a vector-based pathfinding algorithm using AI-based heuristics and adjusting feature layer weights;

iii. compare the generated routes based on total route length, transmission tower types, Right of Way (ROW) cost and respective costs to determine the optimized least cost path;

g. a user interface configured to:

i. display the generated routes and allow user interaction to adjust feature layer buffers and weights;

ii. upload custom feature layers;

iii. add, delete, or move transmission towers along the generated routes;

h. a project tracking module configured to track the progress of route generation activities and visualize the current status of various projects;
i. an explainability feature configured to provide visibility to the user regarding route generation decisions, including reasons for route termination and suggestions for adjusting weights or buffers to achieve route convergence;
j. a reporting module configured to generate system-based reports for comparative analysis of stacked AOIs and routes, including feasibility of installations and time/cost implications;
wherein the system is configured to generate optimized least cost paths for transmission lines by leveraging high-resolution satellite imagery, machine learning algorithms, and AI-based heuristics, thereby reducing the turnaround time for route evaluation, improving the accuracy and standardization of the transmission line survey process, and enabling the preparation of Bill of Materials.

2. The system of claim 1, wherein the satellite imagery acquisition module is configured to obtain satellite images with a resolution of 10 meters or better.

3. The system of claim 1, wherein the preprocessing module is configured to perform image correction, noise reduction, and enhancement to improve the quality of the satellite images.

4. The system of claim 1, wherein the machine learning-based LULC model is trained using a dataset comprising various terrain types to improve classification accuracy.

5. The system of claim 1, wherein the cost raster generation module is configured, based on the LULC data and decision logics, to dynamically update the cost raster based on changes in feature layer weights and buffers.

6. The system of claim 1, wherein the AOI generation module is configured to generate AOIs by applying different combinations of feature layer weights and comparing the resulting routes.

7. The system of claim 1, wherein the vector-based pathfinding algorithm uses AI-based heuristics to explore possible points in vector space for placing transmission towers and building the route.

8. The system of claim 1, wherein the user interface is configured to provide real-time feedback to the user regarding the impact of adjustments to feature layer weights and buffers on the generated routes.

9. The system of claim 1, wherein the user interface includes a map interface for visualizing the stacked AOIs and routes for comparative analysis.

10. The system of claim 1, wherein the project tracking module includes a timeline-based visualization to track the progress of route generation activities.

11. The system of claim 1, wherein the explainability feature provides detailed explanations for route generation decisions, including the specific business rules and feature layer weights that influenced the decisions.

12. The system of claim 1, wherein the reporting module generates summary reports that include key metrics such as total route length, number and type of transmission towers, ROW Cost and estimated costs.

13. The system of claim 1, wherein the system is configured to allow the user to manually intervene and adjust the route generation process by adding, deleting, or moving transmission towers.

14. The system of claim 1, wherein the system includes a retraining module configured to update the machine learning-based LULC model based on new satellite imagery and user feedback.

15. The system of claim 1, wherein the system is configured to integrate with external geographic information system (GIS) databases to enhance the accuracy of the LULC data.

16. The system of claim 1, wherein the system includes a collaboration module that allows multiple users to work on the same project simultaneously and share updates in real-time.

17. A method for identifying the least cost path of a transmission line using a digital system, comprising:
a. acquiring high-resolution satellite images of a region of interest (ROI);
b. preprocessing the acquired satellite images to enhance their quality for analysis readiness;
c. generating Land Use Land Classification (LULC) data by:

i. defining the ROI and preparing feature layers including forest, transmission lines, wildlife sanctuaries, and other relevant features;

ii. processing the satellite images to generate low-resolution LULC data using a machine learning-based LULC model;

iii. generating high-resolution LULC data by further processing high-resolution satellite images with the machine learning-based LULC model;

d. creating a cost raster based on the LULC data and decision logics, including feature buffers, crossing spans, weights and Right of Way (ROW) Cost, Wherein ML algorithm also learns decision logics from existing constructed lines;
e. generating an Area of Interest (AOI) by:
i. evaluating specimen routes to determine probable areas for the transmission line;

ii. generating multiple AOIs by adjusting feature layer weights and comparing area statistics of feature layers;

f. generating multiple route options within the AOI by:

i. defining beeline coordinates by specifying start and end points;

ii. applying a vector-based pathfinding algorithm using AI-based heuristics and adjusting feature layer weights;

iii. comparing the generated routes based on total length, tower types, and respective costs to determine the optimized least cost path;

g. displaying the generated routes on a user interface and allowing user interaction to:

i. adjust feature layer buffers and weights;

ii. upload custom feature layers;

iii. add, delete, or move transmission towers along the generated routes;

h. tracking the progress of route generation activities and visualizing the current status of various projects;
i. providing visibility to the user regarding route generation decisions, including reasons for route termination and suggestions for adjusting weights or buffers to achieve route convergence;
j. generating system-based reports for comparative analysis of stacked AOIs and routes, including feasibility of installations and time/cost implications;
wherein the method leverages high-resolution satellite imagery, machine learning algorithms, and AI-based heuristics to generate optimized least cost paths for transmission lines, thereby reducing the turnaround time for route evaluation, improving the accuracy and standardization of the transmission line survey process, and enabling the preparation of Bill of Materials.

18. The method of claim 17, wherein the high-resolution satellite images are acquired with better resolution.

19. The method of claim 17, wherein preprocessing the acquired satellite images includes performing image correction, noise reduction, and enhancement.

20. The method of claim 17, wherein the machine learning-based LULC model is trained using a dataset comprising various terrain types to improve classification accuracy.

21. The method of claim 17, wherein creating the cost raster includes dynamically updating the cost raster based on changes in feature layer weights and buffers.

22. The method of claim 17, wherein generating multiple AOIs includes applying different combinations of feature layer weights and comparing the resulting routes.

23. The method of claim 17, wherein the vector-based pathfinding algorithm uses AI-based heuristics to explore possible points in vector space for placing transmission towers and building the route.

24. The method of claim 17, wherein the user interface provides real-time feedback to the user regarding the impact of adjustments to feature layer weights and buffers on the generated routes.

25. The method of claim 17, wherein the user interface includes a map interface for visualizing the stacked AOIs and routes for comparative analysis.

26. The method of claim 17, wherein tracking the progress of route generation activities includes using a timeline-based visualization.

27. The method of claim 17, wherein providing visibility to the user regarding route generation decisions includes detailing the specific business rules and feature layer weights that influenced the decisions.

28. The method of claim 17, wherein generating system-based reports includes summarizing key metrics such as total route length, number of transmission towers, RoW Cost and estimated costs.

29. The method of claim 17, wherein the user is allowed to manually intervene and adjust the route generation process by adding, deleting, or moving transmission towers.

30. The method of claim 17, wherein the machine learning-based LULC model is periodically retrained based on new satellite imagery and user feedback.

31. The method of claim 17, wherein the method integrates with external geographic information system (GIS) databases to enhance the accuracy of the LULC data.

32. The method of claim 17, wherein multiple users can collaborate on the same project simultaneously and share updates in real-time.

33. The method of claim 17, wherein the cost raster is generated by assigning different weights to various feature layers based on business rules.

34. The method of claim 17, wherein the method includes generating a Bill of Materials based on the optimized least cost path.

35. The method of claim 17, wherein the method includes generating alerts for the user when the route generation algorithm encounters obstacles or deviations from the predefined path.

36. The method of claim 17, wherein the method includes storing historical route data for future reference and analysis.

37. The method of claim 17, wherein the method includes providing recommendations for alternative routes based on user-defined criteria such as cost, time, or environmental impact.

38. The method of claim 17, wherein the method includes validating the generated routes against regulatory and environmental compliance requirements.

39. The method of claim 17, wherein the method includes simulating the impact of different route options on the surrounding environment and infrastructure.

40. The method of claim 17, wherein the method includes generating 3D visualizations of the generated routes for enhanced analysis and presentation.

41. The method of claim 17, wherein the method includes incorporating real-time weather data to adjust the route generation process based on current and forecasted conditions.

42. The method of claim 17, wherein the method includes providing a user-friendly dashboard for monitoring and managing multiple transmission line projects simultaneously.

43. The method of claim 17, wherein the method includes generating detailed reports on the feasibility of installations, including time and cost implications for each generated route.

44. The method of claim 17, wherein the method includes incorporating user feedback to continuously improve the accuracy and efficiency of the route generation algorithm.

45. The method of claim 17, wherein the method includes providing training and support resources to help users effectively utilize the digital route optimization system.

46. The method of claim 17, wherein the method includes ensuring data security and privacy by implementing encryption and access control measures for all data processed and stored by the system.

47. The method of claim 17, wherein the method includes generating alerts for potential conflicts or overlaps with existing infrastructure or planned projects.