Cloud Image Management for Multi-Sensor Aerial Data Access
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Solution Overview
Problem
Existing systems lack efficient methods for managing and processing large volumes of image data captured by advanced aerial vehicles, particularly those capable of vertical take-off and landing, which often require sophisticated sensors like RGB, LIDAR, and multi-spectral cameras, and integrating this data for precise analysis and user-friendly access.
Innovation Solution
A cloud-based system that processes and manages image data from aerial vehicles, utilizing a network-connected server with image management software, enabling data storage, metadata generation, co-registration, filtering, and user-friendly access, along with tools for analyzing and displaying multi-spectral images, and generating reports.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If image data is stored locally on aerial vehicles, then data access speed is improved, but device complexity and storage capacity requirements increase
Solution Approach 1:
The patent extracts the storage function from the aerial vehicle itself and relocates it to cloud-based servers. Image data captured by aerial vehicles is transferred to and stored on remote servers, allowing the aerial vehicle to maintain minimal local storage while enabling fast access through networked retrieval. This resolves the contradiction by removing the storage burden from the aerial vehicle while maintaining data availability.
Solution Approach 2:
The patent introduces a cloud-based server system as an intermediary between aerial vehicles and users. The server acts as a mediator that receives, stores, and manages image data, providing centralized access points for multiple users and aerial vehicles. This intermediary infrastructure enables efficient data sharing and access without requiring complex local storage systems on each aerial vehicle.
2Measurement precision
If multiple sensors (RGB, LIDAR, multi-spectral) are integrated on aerial vehicles, then data quality and measurement precision are improved, but device complexity increases
Solution Approach 1:
The patent implements a universal data management platform that handles multiple sensor types (RGB, LIDAR, multi-spectral) through a single integrated system. The cloud-based infrastructure provides unified data reception, storage, and processing capabilities that work across all sensor modalities, eliminating the need for separate specialized systems for each sensor type while maintaining high measurement precision for land monitoring applications.
Solution Approach 2:
The cloud server system serves as an intermediary that manages the complexity of multiple sensor integrations. Rather than requiring the aerial vehicle to process and manage diverse sensor data locally, the system transfers all sensor data to the cloud where specialized processing algorithms handle each sensor type appropriately. This mediator approach maintains measurement precision while reducing on-vehicle system complexity.
3Productivity
If image data is processed and analyzed in real-time, then productivity and anomaly detection speed are improved, but use of energy and computational resources increase
Solution Approach 1:
The patent extracts the computational processing function from the aerial vehicle and relocates it to cloud-based servers. Image data is captured by the aerial vehicle and transmitted to remote servers for processing and analysis. This extraction allows rapid anomaly detection through powerful cloud computing resources while the aerial vehicle maintains minimal onboard processing, significantly reducing energy consumption during flight operations.
Solution Approach 2:
The patent transitions the processing dimension from local (on-vehicle) to remote (cloud-based). By moving computational resources to another dimension (the cloud infrastructure), the system enables high-speed processing and anomaly detection without constraining the aerial vehicle's energy budget. This dimensional shift allows access to virtually unlimited computational power while maintaining mobile operation.
4Measurement precision
If co-registration of aerial and satellite imagery is performed to high precision, then measurement precision is improved, but loss of time in processing increases
Solution Approach 1:
The patent implements preliminary georeferencing and coordinate system standardization during the data capture phase. Aerial and satellite imagery are pre-processing aligned to common coordinate frameworks before detailed co-registration is performed. This preliminary action reduces the computational complexity and time required for high-precision co-registration while maintaining accurate spatial alignment, as the bulk of alignment work has already been completed in advance.
Data Source
AI summary
Systems, devices, and methods for receiving image data; transferring the captured image data to a server having a processor and addressable memory via a network-connected computing device; storing the captured image data on the server; generating captured image metadata based on the stored captured image data; providing access to the captured image data and captured image metadata via an image management component; displaying, by the image management component, the captured image data; and filtering, by the image management component, the captured image data based on the generated captured image metadata.