Geospatial-Temporal Graph Search for Remote Sensing Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current machine-assisted search techniques for remote sensing data fail to capture semantically relevant patterns, especially over time, due to the computational expense and relational characteristics of geospatial-temporal (GST) graphs, making it difficult to identify features of interest in large datasets.
Innovation Solution
The generation of GST graphs that represent geospatial and temporal relationships using nodes and edges, allowing for efficient search techniques such as star graph search and heterogeneous complex search to simplify the identification of features by encoding temporal and spatial relationships, and using graph search modules to conduct user-defined searches over these graphs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If subgraph isomorphism methods are used to search GST graphs, then search completeness is improved, but computational expense increases significantly
Solution Approach 1:
The patent segments the complex GST graph search problem into multiple phases: candidate generation using simplified filters, candidate verification using subgraph isomorphism, and result refinement. This segmentation allows the system to quickly eliminate non-matching candidates without performing expensive subgraph isomorphism checks on all possible candidates, thus maintaining search completeness while reducing computational expense.
Solution Approach 2:
The patent performs preliminary filtering actions before executing the computationally expensive subgraph isomorphism algorithm. By pre-processing the GST graph to identify candidate regions based on simplified geometric and attribute constraints, the system prepares a reduced candidate set that is then subjected to rigorous verification, avoiding unnecessary computational expense while ensuring no true matches are missed.
2Ease of manufacture
If traditional image search techniques are used, then implementation simplicity is maintained, but ability to capture semantically relevant patterns over time deteriorates
Solution Approach 1:
The patent transitions from traditional 2D image space search to a multi-dimensional GST graph space that incorporates spatial coordinates, temporal information, and semantic relationships as additional dimensions. This dimensional expansion enables the system to capture semantically relevant patterns over time by querying relationships across multiple dimensions simultaneously, while the graph abstraction maintains implementation simplicity through standardized query interfaces.
Solution Approach 2:
The patent introduces the GST graph as an intermediary data structure that bridges traditional image search and semantic pattern recognition. The graph mediates between raw image data and search queries by encoding temporal and spatial relationships in a structured format, allowing complex semantic searches to be performed without directly processing the full complexity of temporal image sequences, thus preserving implementation simplicity while enhancing pattern capture capability.
3Measurement precision
If GST graphs are constructed to represent temporal relationships, then temporal pattern recognition is improved, but graph complexity increases
Solution Approach 1:
The patent applies local quality by differentiating node and edge properties within the GST graph based on their specific functional roles. Temporal nodes represent observations at specific times with associated metadata, while spatial nodes represent geographic locations. Edges are typed to indicate specific relationship kinds (e.g., temporal succession, spatial proximity). This local differentiation enables precise temporal pattern recognition through targeted queries on specific node and edge types without requiring the entire graph structure to be uniformly complex.
Data Source
AI summary
Various technologies pertaining to identifying objects of interest in remote sensing images by searching over geospatial-temporal graph representations 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. Geospatial-temporal graph searches are made computationally efficient by taking advantage of characteristics of geospatial-temporal data in remote sensing images through the application of various graph search techniques.