Description:FIELD OF THE INVENTION
The present invention pertains to the field of remote sensing data processing, specifically utilizing a hybrid neural network model for land cover classification. It integrates advanced data acquisition from multispectral and hyperspectral sensors with augmented reality technology for real-time environmental monitoring and decision support.
BACKGROUND OF THE INVENTION
The increasing need for accurate and timely land cover classification has posed challenges in remote sensing applications. Traditional methods often rely on static models that struggle to adapt to complex environmental conditions. The lack of real-time analysis and limited interpretability of classification results further hinder effective decision-making in environmental monitoring, urban planning, and disaster management.
Moreover, conventional neural networks may fail to efficiently capture dynamic spatial-temporal patterns in large-scale remote sensing data. Augmented Reality (AR)-enabled systems offer an innovative approach by visualizing classification outcomes in real-time, enhancing situational awareness. However, the integration of robust deep learning models is crucial for accurate classification.
This invention proposes a Hybrid ConvNeXt-Liquid Neural Network Model that leverages the advantages of both convolutional neural networks (CNNs) and liquid neural networks (LNNs). The ConvNeXt model ensures effective feature extraction, while the LNN provides adaptive learning capabilities, enabling superior performance in varying landscapes. Coupled with an AR-enabled interface, the system empowers users with an interactive and immersive data exploration experience, bridging the gap between raw data and actionable insights.
• Google Earth Engine: Provides cloud-based geospatial analysis using satellite imagery and machine learning, but lacks real-time adaptive learning capabilities.
• Sentinel Hub: Offers on-demand satellite data analysis and visualization; however, it does not integrate augmented reality for interactive decision-making.
• Esri ArcGIS: A comprehensive GIS platform with AI-powered analysis, though it primarily relies on conventional neural networks with limited adaptability.
• Pix4D: Specializes in photogrammetry and 3D mapping but lacks a hybrid neural network approach for robust land cover classification.
Commercial practices currently involve using static AI models that require frequent retraining for dynamic environmental changes. While these platforms provide visual insights, they often lack immersive augmented reality applications that can enhance situational awareness. The proposed Hybrid ConvNeXt-Liquid Neural Network Model addresses these gaps by integrating adaptive learning with an AR interface for real-time, accurate land cover classification.
The shortcomings of existing systems include the lack of real-time data processing, limited integration of augmented reality for visual insights, inadequate adaptability to changing environmental conditions, reliance on static AI models, and insufficient accuracy in land cover classification across diverse terrains.
OBJECTIVES OF THE INVENTION
Main objective of the present invention is to develop a system that integrates a hybrid ConvNeXt-Liquid Neural Network model for efficient remote sensing data processing and adaptive land cover classification.
Another objective of the present invention is to enable real-time visualization and decision-making through augmented reality (AR) technology, facilitating enhanced environmental monitoring.
Another objective of the present invention is to create an automated classification pipeline capable of processing large remote sensing datasets with minimal human involvement.
Another objective of the present invention is to design a scalable system that can be customized for various applications such as agriculture, forestry, urban planning, and disaster management.
Another objective of the present invention is to provide actionable and accurate land cover classification data to stakeholders, governments, and research institutions at both regional and pan-European levels.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
Remote sensing data processing receives support from a hybrid ConvNeXt-Liquid Neural Network model combined with augmented reality (AR) technology through this invention's system and method.
Herein enclosed a remote sensing data processing system comprising:
an advanced data acquisition layer configured to collect remote sensing data via multispectral and hyperspectral sensors mounted on satellites and drones;
a hybrid neural network model comprising ConvNeXt and Liquid Neural Network architectures, adapted to enhance feature extraction and support environmental adaptability;
an automated classification pipeline configured to process large-scale remote sensing data and generate land cover maps with minimal human intervention;
an augmented reality interface for real-time visualization of classified land cover data to support decision-making in environmental monitoring; and
customizable application modules for specific domains including agriculture, forestry, urban planning, and disaster management.
A method for remote sensing data processing system comprising the steps of:
acquiring multispectral and hyperspectral imagery through satellites and drones;
feeding the acquired data into a hybrid neural network model integrating ConvNeXt and Liquid Neural Networks;
classifying land cover features using an automated classification pipeline;
visualizing the classified data via an augmented reality interface; and
applying the processed data to domain-specific modules for agriculture, forestry, urban planning, and disaster management.
The hybrid neural network model integrates convolutional feature extraction (ConvNeXt) with dynamic temporal modeling (Liquid Neural Networks) to improve accuracy and adaptability of classification.
The augmented reality interface provides interactive spatial overlays of classified data on physical environments for enhanced user engagement and decision-making.
The automated classification pipeline is configured to operate with minimal staff input and high scalability, allowing deployment at both regional and continental scales.
The customizable modules are configured to adapt the output data to user-specific applications, enabling practical use by environmental stakeholders, administrative bodies, and research institutions.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example 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,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, 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 example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
In some embodiments of the present invention, introduces a novel system and method for remote sensing data processing, integrating a hybrid ConvNeXt-Liquid Neural Network model with augmented reality (AR) technology.
In some embodiments of the present invention, the system leverages multispectral and hyperspectral sensors aboard satellites and drones to acquire high-resolution remote sensing images.
In some embodiments of the present invention, it employs a hybrid neural network combining ConvNeXt and Liquid Neural Networks, enhancing image feature extraction and enabling automatic adaptability to varying environmental conditions.
In some embodiments of the present invention, the augmented reality interface facilitates real-time visualization of classified land cover data, aiding decision-making in environmental monitoring.
In some embodiments of the present invention, an automated classification pipeline processes large datasets efficiently, generating accurate land cover maps with minimal human involvement. The system is customizable, applicable across diverse sectors such as agriculture, forestry, urban planning, and disaster management.
In some embodiments of the present invention, its scalability ensures usability at both regional and pan-European levels, offering actionable insights to stakeholders, governments, and research institutions.
In some embodiments of the present invention, this invention presents a breakthrough in remote sensing technology by providing real-time visual and decision support with minimal human intervention, marking a significant advancement in land cover classification and environmental monitoring.
Herein enclosed a remote sensing data processing system comprising:
an advanced data acquisition layer configured to collect remote sensing data via multispectral and hyperspectral sensors mounted on satellites and drones;
a hybrid neural network model comprising ConvNeXt and Liquid Neural Network architectures, adapted to enhance feature extraction and support environmental adaptability;
an automated classification pipeline configured to process large-scale remote sensing data and generate land cover maps with minimal human intervention;
an augmented reality interface for real-time visualization of classified land cover data to support decision-making in environmental monitoring; and
customizable application modules for specific domains including agriculture, forestry, urban planning, and disaster management.
A method for remote sensing data processing system comprising the steps of:
acquiring multispectral and hyperspectral imagery through satellites and drones;
feeding the acquired data into a hybrid neural network model integrating ConvNeXt and Liquid Neural Networks;
classifying land cover features using an automated classification pipeline;
visualizing the classified data via an augmented reality interface; and
applying the processed data to domain-specific modules for agriculture, forestry, urban planning, and disaster management.
The hybrid neural network model integrates convolutional feature extraction (ConvNeXt) with dynamic temporal modeling (Liquid Neural Networks) to improve accuracy and adaptability of classification.
The augmented reality interface provides interactive spatial overlays of classified data on physical environments for enhanced user engagement and decision-making.
The automated classification pipeline is configured to operate with minimal staff input and high scalability, allowing deployment at both regional and continental scales.
The customizable modules are configured to adapt the output data to user-specific applications, enabling practical use by environmental stakeholders, administrative bodies, and research institutions.
EXAMPLE 1
BEST METHOD
Remote sensing data processing receives support from a hybrid ConvNeXt-Liquid Neural Network model combined with augmented reality (AR) technology through this invention's system and method. The system includes:
• Advanced Data Acquisition Layer: The system acquires remote sensing images using both multispectral and hyperspectral sensors which are collected through satellites and drones.
• Hybrid Neural Network Model: The system efficiently combines both ConvNeXt and Liquid Neural Networks to obtain better image features along with automatic environmental adaptability.
• Augmented Reality Interface: Through interactive AR applications provides real time visualization of data classified land cover data for helping in decision making for environmental monitoring.
• Automated Classification Pipeline: The system works with big datasets to make accurate land cover maps at minimal staff involvement.
• Customizable Modules: Adaptable in agriculture, forestry, urban planning and for disaster management.
The system is scalable being usable in different cases. It provides easy applicable information directly to end users and administrative both at regional level (environment management stakeholders, government administration) as pan-European level (research related institutions).
NOVELTY:
A prominent reality -competent remote measurement system uses a hybrid context -wide neural network model for accurate and adaptive land cover classification, and offers real -time visual and decision support with minimal human intervention.
ADVANTAGES OF THE INVENTION
• The system employs a hybrid ConvNeXt-Liquid Neural Network model for more suitable accuracy in land cowl type compared to traditional fashions.
• Integrates augmented fact for actual-time visualization, facilitating intuitive and interactive analysis of faraway sensing records.
• Reduces the need for extensive preprocessing and manual feature extraction by utilizing advanced feature representation.
• Scalable and adaptable for diverse geographic regions and varying environmental conditions, unlike traditional static systems.
, Claims:1. A remote sensing data processing system comprising:
an advanced data acquisition layer configured to collect remote sensing data via multispectral and hyperspectral sensors mounted on satellites and drones;
a hybrid neural network model comprising ConvNeXt and Liquid Neural Network architectures, adapted to enhance feature extraction and support environmental adaptability;
an automated classification pipeline configured to process large-scale remote sensing data and generate land cover maps with minimal human intervention;
an augmented reality interface for real-time visualization of classified land cover data to support decision-making in environmental monitoring; and
customizable application modules for specific domains including agriculture, forestry, urban planning, and disaster management.
2. A method for remote sensing data processing system as claimed in claim 1, comprising the steps of:
a) acquiring multispectral and hyperspectral imagery through satellites and drones;
b) feeding the acquired data into a hybrid neural network model integrating ConvNeXt and Liquid Neural Networks;
c) classifying land cover features using an automated classification pipeline;
d) visualizing the classified data via an augmented reality interface; and
e) applying the processed data to domain-specific modules for agriculture, forestry, urban planning, and disaster management.
3. The method as claimed in claim 2, wherein the hybrid neural network model integrates convolutional feature extraction (ConvNeXt) with dynamic temporal modeling (Liquid Neural Networks) to improve accuracy and adaptability of classification.
4. The method as claimed in claim 2, wherein the augmented reality interface provides interactive spatial overlays of classified data on physical environments for enhanced user engagement and decision-making.
5. The method as claimed in claim 2, wherein the automated classification pipeline is configured to operate with minimal staff input and high scalability, allowing deployment at both regional and continental scales.
6. The method as claimed in claim 2, wherein customizable modules are configured to adapt the output data to user-specific applications, enabling practical use by environmental stakeholders, administrative bodies, and research institutions.