Image Geolocation Using Skyline Analysis and Modular CNN Filtering
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Solution Overview
Problem
Existing image analysis systems for determining geolocation require manual annotation and are time-consuming and inefficient, as they involve manual review and annotation of thousands of images to identify relevant geographic markers.
Innovation Solution
A system utilizing modular Convolution Neural Networks (CNNs) to automatically analyze images, determining probabilities of outdoor environments, user interest, and locatability, and then analyzing skylines and regions of interest to identify geographic locations, using global geographic data for comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation and review processes are used to determine geolocation from images, then accuracy in identifying geographic markers can be maintained, but the time consumption and operational complexity increase significantly
Solution Approach 1:
The system performs preliminary automated analysis of images to identify potential geographic markers and pre-filter images before manual review. This preliminary action reduces the volume of images requiring manual annotation while maintaining accuracy, as the automated pre-processing identifies promising candidates that analysts can then verify more efficiently
Solution Approach 2:
An automated image analysis system acts as an intermediary between the raw images and human analysts. This intermediary performs initial processing, identifies potential geographic features, and prioritizes images for manual review, thereby reducing the overall time consumption while preserving the accuracy benefits of human analysis
2Reliability
If manual review of thousands of images is conducted to identify images of interest, then comprehensive analysis can be achieved, but productivity decreases due to the intensive manual effort required
Solution Approach 1:
The image analysis process is segmented into multiple stages: automated pre-processing, initial filtering, prioritization, and selective manual review. This segmentation allows the system to handle large volumes of images efficiently through automated means while directing comprehensive analysis only to the most promising candidates, thereby improving overall productivity without sacrificing reliability
Solution Approach 2:
The system applies automated analysis selectively to subsets of images rather than attempting to manually review every single image. By using automated algorithms to perform preliminary analysis and filtering on the entire dataset, the system achieves comprehensive coverage at scale, then applies partial manual review only where needed, significantly improving productivity while maintaining reliability
3Measurement precision
If analysts manually compare candidate lists with geolocation data, then accurate location determination can be achieved, but the complexity and time required for the process increase
Solution Approach 1:
The manual mechanical process of comparing candidate lists with geolocation data is replaced with automated computational algorithms. The system uses machine learning models and automated matching algorithms to compare image features with geographic database entries, thereby maintaining accuracy while reducing process complexity and eliminating the need for manual comparison operations
Data Source
AI summary
Methods and media for determining a list of geographic location candidates from an image of an environment are described. Open-source data indicative of the Earth's surface may be obtained and compared with images obtained from online sources. The images may be automatically analyzed using a plurality of modular convolution neural networks to determined probabilities of interest, environment, and if the image is locatable. Further, the resulting images may be analyzed for skyline and ridgeline depth orders and Region of Interest. A geolocation depicted in the image may be determined by comparing the results of the analysis with global geographic data.