Geographical Data Analysis Using Statistical Knowledge Bases
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Solution Overview
Problem
The large volume of digital data generated from frequent observations of geographical regions by platforms like drones, airplanes, and satellites makes it difficult to interpret and visualize, leading to inefficient data search and visualization challenges.
Innovation Solution
A semi-automatic method for analyzing geographical regions using a sensor and digital data processing center, which involves a learning phase to create a knowledge base and a detection phase to identify areas of interest, utilizing a multimodal and multi-localized statistical model to classify and process data, and alerting operators when unusual patterns are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequent and recurring observations are made of the same geographical region, then the precision of geographical knowledge is improved, but the volume of digital data increases making interpretation and visualization difficult
Solution Approach 1:
The patent extracts only the essential and relevant information from the large volume of digital data by creating a knowledge base that stores structured geographical knowledge. Instead of processing all raw data, the system extracts meaningful patterns and representations, thereby reducing data volume while maintaining precision.
Solution Approach 2:
The patent transforms raw digital data into structured knowledge representations by changing parameters from raw pixel values to semantic geographical concepts. This transformation allows the system to work with compressed, meaningful parameters rather than raw data volumes.
2Loss of information
If a large volume of digital data is stored to precisely know each geographical region, then the completeness of information is improved, but the efficiency of data search deteriorates
Solution Approach 1:
The patent segments the large volume of digital data into organized knowledge bases with hierarchical structures. Data is divided into manageable categories and relationships, enabling efficient indexing and retrieval without sacrificing information completeness.
Solution Approach 2:
The patent introduces an intermediary knowledge base layer between raw data and user queries. This intermediary structure facilitates efficient search by providing organized access paths and reducing the complexity of searching through raw data volumes.
3Measurement precision
If manual interpretation of digital data is performed, then the accuracy of area detection is improved, but the productivity of data analysis deteriorates
Solution Approach 1:
The patent implements automated detection algorithms that perform area of interest detection without requiring manual operator intervention. The system serves itself by automatically processing data, detecting changes, and generating results, thereby maintaining accuracy while dramatically improving productivity.
Solution Approach 2:
The patent replaces manual mechanical interpretation processes with automated computational algorithms. This substitution maintains or improves detection accuracy while eliminating the time-consuming nature of manual analysis, thereby increasing productivity.
Data Source
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AI summary
The invention essentially concerns a method for analysing geographical regions and detecting areas of interest by means of a scanning means comprising a sensor and a digital data processing centre, characterised in that it comprises a first phase (100) of learning the intrinsic appearance of each geographical region, in which a database of knowledge comprising groups of digital data is created, followed by a second phase of detecting areas of interest in the geographical regions, said phases (100) comprising the steps consisting of: a. acquiring (101), by means of the sensor, a piece of digital data corresponding to a geographical region, b. transmitting (102) the digital data acquired in step a) to the digital data processing centre; c. processing (103) the digital data transmitted in step b) by means of the digital data processing centre.