Satellite Image Grid Coordinate Translation System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The processing of vast amounts of satellite image data is inefficient due to the limited number of researchers available, hindering the discovery of important geographical features, and there is a need for a fast and accurate system to determine the geographical location of features in satellite images, especially in emergency situations like natural disasters.
Innovation Solution
A method and apparatus that obtain satellite images, define a grid corresponding to the image, receive grid locations from a crowdsourcing platform, and translate these locations into geographical coordinates using pixel space conversion, enabling rapid and accurate mapping of features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual processing methods are used, then measurement precision and reliability are maintained, but productivity is extremely low due to limited number of researchers
Solution Approach 1:
The patent introduces a crowdsourcing platform as an intermediary between satellite image data and geographical location data. The platform enables automated translation of grid locations to geographical coordinates through pixel space conversion, eliminating the need for manual processing while maintaining accuracy. This intermediary system resolves the contradiction by providing high productivity through automation without requiring complex manual intervention.
Solution Approach 2:
The patent replaces manual mechanical processing with automated computational methods. Instead of researchers manually identifying and mapping features, the system uses algorithmic pixel space conversion to automatically translate grid locations into geographical coordinates. This substitution of mechanical manual work with automated computational processes dramatically increases productivity while reducing system complexity.
2Productivity
If crowdsourcing is used to process large amounts of image data, then productivity increases significantly, but measurement precision may be compromised without proper verification
Solution Approach 1:
The patent implements feedback mechanisms through the crowdsourcing platform where multiple users can verify and validate geographical location data. The system receives grid locations from crowdsourcing participants and feeds this information back through verification processes to ensure measurement precision is maintained while benefiting from high productivity. This feedback loop allows the system to scale processing capacity while preserving accuracy through collective verification.
3Measurement precision
If manual mapping of geographical locations is performed, then measurement precision is maintained, but loss of time increases due to the sheer volume of data
Solution Approach 1:
The patent applies preliminary action by pre-dividing satellite images into grid cells with known geographical coordinates before the actual mapping process. This preliminary grid setup enables rapid translation of feature locations into geographical coordinates without time-consuming manual measurement during processing. The pre-established grid framework eliminates time loss while maintaining precision through systematic coordinate transformation.
Solution Approach 2:
The patent substitutes manual mechanical mapping with automated computational translation. Instead of researchers manually measuring and calculating geographical locations, the system uses programmed pixel space conversion algorithms to automatically transform grid locations into accurate geographical coordinates. This mechanical substitution dramatically reduces processing time while preserving measurement precision through consistent algorithmic transformation.
Data Source
AI summary
A method for mapping a geographical location of a feature in a satellite image is provided. An image of a geographical surface is obtained, the image comprising a plurality of pixels, data indicating the latitude and longitude of each vertex of the image and data relating to a grid corresponding to the image is obtained. A grid location of a feature in the image is received, the grid location identifying a cell in the grid. The grid location is translated into a geographical location, which comprises calculating a pixel space location of at least one vertex of the identified grid cell; converting the pixel space into a latitude/longitude space based on the obtained data indicating the latitude and longitude of each vertex; and determining the latitude and longitude of the at least one vertex of the identified cell, based on the calculated pixel space location and the latitude/longitude space.