Geospatial Temporal Graphs for Remote Sensing Activity Patterns
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Solution Overview
Problem
Current machine-assisted search techniques in remote sensing data fail to capture semantically relevant patterns, especially over time, due to their reliance on image pixel characteristics, which limits their ability to identify and track activity in large geographical datasets.
Innovation Solution
The use of geospatial-temporal (GST) graph representations to encode categorization data from remote sensing image data, where nodes represent features and edges describe relationships, allowing for efficient identification of activity patterns by distinguishing between persistent and non-persistent objects through time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If machine-assisted search techniques rely on image pixel characteristics, then search operations can be performed on remote sensing data, but the ability to capture semantically relevant patterns over time deteriorates
Solution Approach 1:
The patent segments the remote sensing data analysis into two distinct graph types: geospatial graphs that capture spatial relationships between objects in individual images, and temporal graphs that capture changes over time between multiple images. This segmentation allows each graph type to specialize in capturing specific patterns, with the temporal graph specifically addressing the loss of semantic temporal patterns that occurs when relying solely on pixel characteristics.
Solution Approach 2:
The patent transitions from analyzing only spatial dimensions (pixel characteristics within single images) to incorporating the temporal dimension by creating temporal graphs that represent object persistence and changes across multiple images taken at different times. This dimensional expansion enables the system to capture semantically relevant temporal patterns that were previously lost.
2Measurement precision
If graph representations include both geospatial and temporal relationships, then activity pattern identification improves, but computational complexity increases
Solution Approach 1:
The patent divides the complex GST graph construction into sequential stages: first building geospatial graphs from individual images, then constructing temporal graphs by comparing corresponding geospatial graphs across time. This segmentation of the graph construction process reduces computational complexity by breaking down the overall task into manageable sub-tasks that can be performed independently and then integrated.
Solution Approach 2:
The patent performs preliminary processing by extracting object features and building geospatial graphs from individual images before attempting to construct the full temporal GST graph. This preliminary action prepares the data in advance, organizing it into structured graph representations that make subsequent temporal analysis more efficient and less computationally intensive.
3Loss of information
If nodes represent both persistent and non-persistent objects, then comprehensive scene understanding is achieved, but graph construction complexity increases
Solution Approach 1:
The patent applies local quality by treating different types of graph nodes differently based on their properties. Persistent objects (those appearing across multiple images) are handled through temporal graph connections that track their persistence, while non-persistent objects (appearing in single images) are handled through geospatial graph connections only. This differentiated treatment simplifies graph construction by applying appropriate processing rules to each node type rather than using a uniform approach for all nodes.
Data Source
AI summary
Various technologies pertaining to modeling patterns of activity observed in remote sensing images using geospatial-temporal graphs are described herein. Graphs are constructed by representing objects in remote sensing images as nodes, and connecting nodes with undirected edges representing either distance or adjacency relationships between objects and directed edges representing changes in time. Activity patterns may be discerned from the graphs by coding nodes representing persistent objects like buildings differently from nodes representing ephemeral objects like vehicles, and examining the geospatial-temporal relationships of ephemeral nodes within the graph.