Geo-Spatial Object Representation with Iterative Quality Control
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Solution Overview
Problem
Current methods for monitoring widely distributed and loosely connected objects such as railway grids, electricity grids, or oil and gas pipelines are inefficient and error-prone, leading to issues like power outages, wildfires, derailments, and leaks due to inadequate detection of necessary measures, and involve high costs and long reaction times.
Innovation Solution
A fully automated method for processing geo-spatial data to derive spatial object representations, using artificial intelligence techniques like neural networks, and ensuring quality conditions are met through iterative data collection and processing, allowing for continuous improvement and reduced errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual inspection methods are used for monitoring distributed objects, then operational control can be maintained, but monitoring efficiency is low and costs are high
Solution Approach 1:
The patent replaces manual mechanical inspection methods with automated optical measurement systems (satellite imagery, aerial photography, ground-based sensors) to monitor railway infrastructure. This substitution enables continuous automated detection of vegetation encroachment, track geometry changes, and other spatial objects, dramatically improving monitoring efficiency while reducing reaction times from days/weeks to hours or minutes.
Solution Approach 2:
The system enables self-service monitoring where the infrastructure itself is continuously observed by automated detection systems. The spatial object representations automatically update and alert operators to issues without requiring manual intervention, allowing the system to monitor itself and trigger maintenance workflows autonomously.
2Measurement precision
If manual inspection processes are used, then operational control is possible, but detection accuracy is insufficient leading to errors
Solution Approach 1:
Manual visual inspection is replaced with automated image processing and neural network-based object recognition systems. These systems consistently identify spatial objects (vegetation, tracks, signals, bridges) with high accuracy and uniformity, eliminating human errors such as missed detections, misidentifications, and fatigue-related mistakes that plague manual inspection.
Solution Approach 2:
The system implements feedback loops where detected spatial objects and their characteristics are continuously analyzed, and results are fed back into the monitoring system. Quality conditions are evaluated and fed back to trigger additional data collection or re-processing when detection confidence is insufficient, ensuring high accuracy through iterative refinement.
3Area of stationary object
If comprehensive monitoring is implemented, then coverage is improved, but data quality consistency becomes difficult to maintain
Solution Approach 1:
The system dynamically adjusts processing parameters based on the specific characteristics of each spatial object and data source. Different neural network models and processing algorithms are applied depending on the object type (vegetation, track, signal), sensor modality (optical, radar, LiDAR), and environmental conditions, ensuring consistent quality across diverse monitoring areas while maintaining adaptability to local variations.
Solution Approach 2:
Quality conditions are systematically evaluated for all derived spatial object representations, and feedback mechanisms trigger re-processing or additional data collection when quality thresholds are not met. This ensures that despite the large and varied monitoring coverage, all data maintain consistent quality standards through automated quality control loops.
Data Source
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AI summary
Embodiments related to a method for analyzing geo-spatial data (11) comprising: c. Processing (S1) at least parts of the geo-spatial data (11) to derive spatial object representations (12); d. Collecting (S3) further geo-spatial data (11') if a quality condition of the spatial object representation (11') is not fulfilled. The proposed embodiments further comprise a system and a computer program.