Description:FIELD OF THE INVENTION:
[001] The present invention relates to the field of environmental science, and more particularly forestry, the present invention relates to the computer vision, augmented reality and generative AI system for environmental data visualization and prediction.
BACKGROUND FOR THE INVENTION:
[002] The following discussion of the background to the invention is intended to facilitate an understanding of the present invention. However, it should be appreciated that the discussion is not an acknowledgment or admission that any of the material referred to was published, known, or part of the common general knowledge in any jurisdiction as of the priority date of the application. The details provided herein the background if belongs to any publication is taken only as a reference for describing the problems, in general terminologies or principles or both of science and technology in the associated prior art.
[003] Currently, forest data is typically gathered by measuring a small plot of land, which is only about 1/25,000th the size of the entire forest, and then extrapolating these measurements to estimate the total forest data. This method relies heavily on statistical extrapolation but often fails to adhere to the fundamental premise of statistics, which is to accurately measure the population. As a result, the sample taken is not truly representative of the entire forest, leading to inaccurate estimates.
[004] This issue is particularly problematic for carbon credit generation, where annual measurements must account for carbon stocks accurately. The inherent variance in the current method allows for significant discrepancies, which can result in green washing within forestry-based carbon projects.
[005] In light of the foregoing, there is a need for the computer vision, augmented reality and generative AI system for environmental data visualization, updation and prediction that overcomes problems prevalent in the prior art.
OBJECTS OF THE INVENTION:
[006] Some of the objects of the present disclosure, which at least one embodiment herein satisfies, are as follows.
[007] The principal object of the present invention is to overcome the disadvantages of the prior art by providing the computer vision, augmented reality and generative AI system for environmental data visualization and prediction.
[008] An object of the present invention is to provide the Computer vision, augmented reality and generative AI system for environmental data visualization, updation and prediction, wherein the system provides precise measurements of forest inventory, reducing errors associated with traditional methods.
[009] Another object of the present invention is to provide the computer vision, augmented reality and generative AI system for environmental data visualization and prediction that reduces the need for specialized equipment and personnel, lowering the overall cost of data collection in subsequent years.
[010] Another object of the present invention is to provide the Computer vision, augmented reality and generative AI system for environmental data visualization and prediction that enables real-time access to updated forest data through AR, enhancing decision-making processes.
[011] Other objects and advantages of the present disclosure will be more apparent from the following description, which is not intended to limit the scope of the present disclosure.
SUMMARY OF THE INVENTION:
[012] The present invention provides a computer vision, augmented reality and generative AI system for environmental data visualization and prediction.
[013] The present invention combines Augmented Reality (AR) and Generative AI to create an advanced tool for visualizing, collecting, and predicting forest data, including carbon stocks. The focus is on measuring and understanding the carbon content in forests.
[014] Generative AI plays a key role by analyzing historical data to generate representative datasets. After gathering data for these new sets using augmented reality, the Generative AI uses this information to make accurate predictions about carbon stocks, which supports long-term planning and sustainability efforts. By providing reliable forecasts and insights, the system helps forest managers, stakeholders, and policymakers make better-informed decisions.
[015] One of the standout features of this system is the AR block, a virtual display that can be viewed through AR glasses or other compatible devices. This AR block shows and updates environmental data in real-time, such as the Tree Details and Diameter at Breast Height (DBH) of trees and carbon data, in an easy-to-understand format.
[016] Additionally, this system offers a cost-effective digital solution bereft of manual gathering and reduces the reliance on drones for yearly data collection, which can be problematic in restricted areas or red zones. By utilizing Generative AI on comprehensive baseline data sets and utilizing digital tools for efficient data collection, the invention provides a thorough understanding of forest conditions without the need for extensive and sometimes dangerous drone surveys.
[017] Generative AI analyzes the metrics, predicts carbon stocks, and generates new datasets, providing deeper insights into forest conservation. This approach ensures that the data is accurate, up-to-date, and easily accessible, enhancing the ability to manage forests sustainably. This technology revolutionizes how environmental data is collected and used, making it a crucial tool for conserving forest ecosystems and supporting carbon management initiatives.
[018] The invention offers:
- Creation of Representative Datasets: Generates new representative datasets using Generative AI and AR blocks for efficient data gathering.
- Real-time Visualization: AR technology displays and updates environmental data in an easy-to-understand format.
- Cost-effective Data Collection: Eliminates the need for manual gathering and reduces reliance on drones, making data collection more economical.
- Accurate Predictions: Generative AI analyzes collected and historical data to predict carbon stocks, aiding long-term planning.
- Enhanced Forest Management: Provides accurate, up-to-date data that supports sustainable forest management and carbon accounting.
- This system transforms the way we gather and use environmental data, supporting better forest conservation and carbon management efforts.
BRIEF DESCRIPTION OF DRAWINGS:
[019] Reference will be made to embodiments of the invention, examples of which may be illustrated in accompanying figures. These figures are intended to be illustrative, not limiting. Although the invention is generally described in the context of these embodiments, it should be understood that it is not intended to limit the scope of the invention to these particular embodiments.
[020] Figure 1: System Architecture; and
[021] Figure 2: Data Collection Process.
DETAILED DESCRIPTION OF DRAWINGS:
[022] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and the detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claim.
[023] As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one” and the word “plurality” means “one or more” unless otherwise mentioned. Furthermore, the terminology and phraseology used herein are solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers, or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles, and the like are included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.
[024] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase “comprising”, it is understood that we also contemplate the same composition, element, or group of elements with transitional phrases “consisting of”, “consisting”, “selected from the group of consisting of, “including”, or “is” preceding the recitation of the composition, element or group of elements and vice versa.
[025] The present invention is described hereinafter by various embodiments with reference to the accompanying drawing, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, several materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
[026] The present invention provides Computer vision, augmented reality and generative AI system for environmental data visualization and prediction.
[027] The proposed system integrates computer vision, augmented reality (AR), and generative AI to enhance environmental data visualization and prediction. This advanced tool is designed to improve the accuracy, efficiency, and economics of forest inventory management, specifically focusing on carbon stock assessment.
[028] System Components:
Computer Vision, LiDAR and Photogrammetry Module
- Function: Captures and processes Lidar Data and images of the forest to gather data on tree coordinates , dimensions and species.
- Components: LiDAR Drones, cameras, image processing software.
- Operation: Analyzes LiDAR and visual data to measure tree DBH and identify tree species.
Generative AI Module
- Function: Analyzes collected and historical data to predict carbon stocks. Create datasets that are representative of forest carbon stocks for easy data collection
- Components: Machine learning algorithms, data processing units.
- Operation: Utilizes large datasets to create new models and datasets that can predict future carbon stock levels based on current and past data.
Augmented Reality Module:
- Function: Provides real-time data visualization through AR interfaces.
- Components: AR glasses, mobile devices, AR software.
- Operation: Displays environmental data, such as Diameter at Breast Height (DBH), directly in the user's field of view and allows for its updation.
Baseline Data Collection:
- Process: LiDAR and High-resolution cameras mounted on drones capture data and images of the forest.
- Tools: LiDAR equipment, Cameras, drones.
- Details: The system uses computer vision to identify and measure each tree.
Data Processing:
- Process: The collected LiDAR feed and images are processed using computer vision algorithms to extract relevant features (e.g., DBH, tree height, species).
- Tools: LiDAR processing software, machine learning algorithms.
- Details: The system converts visual data into numerical data, which is then fed into the generative AI module.
Data Analysis:
- Process: The generative AI module analyzes the processed data to predict carbon stocks and other relevant metrics.
- Tools: Machine learning models, statistical analysis software.
- Details: The AI uses historical and current data to generate accurate predictions for future forest conditions.
Data Visualization:
- Process: The AR module displays the analyzed data in real-time, allowing users to visualize and update forest metrics directly in their environment.
- Tools: AR glasses, mobile devices, AR software.
- Details: Users can see overlays of data such as DBH, carbon stocks, and species information while in the field.
[029] Following are some of the advantages of the present invention:
- Enhanced Accuracy: The invention uses Generative AI and AR to provide precise measurements of tree metrics, such as Diameter at Breast Height (DBH), which are crucial for accurately estimating carbon stocks. This reduces the errors associated with traditional methods that rely on extrapolating data from small sample plots.
- Cost-Effectiveness: By eliminating the need for expensive drone-based LiDAR surveys and specialized equipment, the system significantly reduces the costs of data collection. This makes forest monitoring and carbon accounting more affordable for stakeholders.
- Real-time Data Visualization: The AR module allows for real-time visualization and updates of environmental data. This immediate feedback enhances decision-making processes for forest managers and policymakers, providing them with up-to-date information on forest conditions.
- Ease of Use: The system is designed to be user-friendly, requiring less specialized training compared to existing DMRV platforms. This makes it accessible to a wider range of users, including local forest managers and community members.
- Comprehensive Data Collection: The integration of AR and Generative AI enables comprehensive data collection by generating new representative datasets. This approach ensures that the data gathered is more representative of the entire forest, leading to better-informed management practices.
- Improved Sustainability: By providing accurate predictions of carbon stocks and other forest metrics, the system supports long-term sustainability efforts. This helps in the planning and implementation of conservation projects, ultimately contributing to better forest management and carbon sequestration.
- Reduced Environmental Impact: The digital nature of the system minimizes the need for physical interventions in the forest, such as extensive drone flights or manual data collection. This reduces the environmental footprint associated with traditional data collection methods.
- Scalability: The system can be easily scaled to cover larger forest areas without a significant increase in costs or complexity. This makes it suitable for application in various forest types and sizes, from small community forests to large national parks.
- Better Data Security and Management: The use of secure databases and cloud storage ensures that the collected data is safely stored and easily accessible for future analysis. This improves data management and reduces the risk of data loss.
- Support for Carbon Credit Projects: The accurate and reliable data provided by the system enhances the credibility of forest-based carbon credit projects. This helps in securing funding and support for conservation initiatives, promoting sustainable forest management practices.
[030] In summary, the current invention offers significant improvements in accuracy, cost-effectiveness, ease of use and sustainability over existing technologies. It provides a comprehensive, user-friendly solution for forest data collection and management, supporting better-informed decisions and long-term conservation efforts.
[031] A system incorporating a Generative AI module that analyzes both historical and newly collected forest data to generate new representative datasets, predict carbon stocks, and forecast future forest conditions. A system comprising an Augmented Reality (AR) module configured to visualize and update environmental data, including tree metrics and carbon stocks, in real-time through AR glasses or compatible devices. The AR module also includes AR blocks that facilitate the updating of records by overlaying new data on the user’s view of the forest. A method wherein the Generative AI module integrates with the AR module to provide updated predictions and visualizations of forest data, including carbon stocks, based on the analysis of historical and newly collected datasets. The system also enables real-time updates of records through AR block. A method wherein the Generative AI creates new representative datasets based on historical and collected data, and the AR module is used to update these datasets in real-time by overlaying new information directly onto the user's view of the forest through AR blocks. A system wherein the AR module provides real-time visualization and updation of forest inventory data, including Diameter at Breast Height (DBH) and carbon content, and continuously updates records using AR blocks based on the data generated and processed by the Generative AI module. A method employing Generative AI to perform predictive analytics on forest data, including predicting changes in carbon stocks and forest conditions, thereby supporting long-term forest management and conservation planning. The results are visualized and updated in real-time through the AR module.
Table 1: Comparison of Traditional and Proposed Methods
Criteria Traditional or Current Methods Proposed System
Accuracy Based on extrapolation from small sample plots Direct measurement of each tree using computer vision and more accurate annual estimation using generative AI
Data Collection Manual measurement of a 1/25000 plot of land Automated data collection through Augmented Reality(AR) Blocks
Carbon Stock Assessment High variance, potential for greenwashing Highly accurate predictions using generative AI
Equipment Traditionally Human collection of data. Currently some movement towards on ground or drone based terrestrial LiDAR data collection. Utilizes Above canopy drones, and AR devices
Cost per year High due to specialized equipment and labor Lower costs in subsequent years due to reduced need for specialized equipment and personnel
Time Efficiency Time-consuming manual processes. LiDAR processing is expensive, making yearly accounting very costly. Faster automated data collection and processing
Data Accessibility Delays in processing and accessibility Real-time data visualization via AR
User-Friendliness Requires extensive training for operators More intuitive and user-friendly interface with AR
Environmental Impact Potential disturbance to forest during data collection Minimal disturbance with non-intrusive data collection methods
Sustainability Less emphasis on long-term planning Supports sustainable forest management with accurate data
[032] The disclosure has been described with reference to the accompanying embodiments herein and the various features and advantageous details thereof are explained with reference to the non-limiting embodiments in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein.
[033] The foregoing description of the specific embodiments so fully revealed the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the scope of the embodiments as described herein.

, Claims:1) A computer vision, augmented reality and generative AI system for environmental data visualization and prediction, the system comprises an electronic system having a memory device, image capturing devices; input-output units, a network connectivity and a processor; wherein the processor comprises:
- Computer Vision, LiDAR and Photogrammetry Module that processes LiDAR Data and images of the forest to gather data on tree coordinates , dimensions and species;
- A Generative AI Module that analyses collected and historical data to predict carbon stocks; and utilizes large datasets to train new models and datasets that can predict future carbon stock levels based on current and past data;
- An Augmented Reality Module that provides real-time data visualization through AR interfaces and displays environmental data, comprising: Diameter at Breast Height (DBH), directly in the user's field of view on a display of the electronic device; wherein said module allows for data updation using the interface.
2) The system as claimed in claim 1, wherein the image capturing devices comprising LiDAR Drones, cameras.
3) A method for system as claimed in claim 1, the method comprises steps of:
- Data Collection: wherein the LiDAR and High-resolution cameras mounted on the drones capture data and images of the forest;
- Data Processing: wherein the collected LiDAR feed and images are processed using computer vision algorithms to extract relevant features comprising: DBH, tree height, species;
- Data Analysis: wherein the generative AI module analyses the processed data to predict carbon stocks and other relevant metrics; and
- Data Visualization: wherein the AR module displays the analyzed data in real-time, allowing users to visualize and update forest metrics directly in their environment.