17915841. INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM simplified abstract (Rakuten Group, Inc.)
INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM
Organization Name
Inventor(s)
Vinayaraj Poliyapram of Tokyo (JP)
Jeremiah Anderson of Tokyo (JP)
INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM - A simplified explanation of the abstract
This abstract first appeared for US patent application 17915841 titled 'INFORMATION PROCESSING APPARATUS, INFORMATION PROCESSING METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM
The abstract describes an information processing apparatus that utilizes machine learning to predict and generate images representing population values and land types based on satellite images.
- Acquisition unit acquires satellite image
- First generation unit predicts population distribution using machine learning
- Second generation unit predicts land type probabilities using machine learning
Potential Applications: - Urban planning - Environmental monitoring - Disaster response and management
Problems Solved: - Efficient population distribution analysis - Accurate land type classification
Benefits: - Improved decision-making - Enhanced resource allocation - Increased efficiency in various applications
Commercial Applications: - Geographic information systems - Remote sensing technology markets
Prior Art: Research on machine learning algorithms for image prediction and analysis in satellite imagery.
Frequently Updated Research: Advancements in machine learning algorithms for satellite image analysis.
Questions about the technology: 1. How does this technology improve upon traditional methods of population distribution analysis? 2. What are the key factors influencing the accuracy of land type predictions in this system?
Original Abstract Submitted
An information processing apparatus () includes: an acquisition unit () configured to acquire a satellite image; a first generation unit () configured to recursively predict and generate a first image representing a distribution of population values with respect to the satellite image, through machine learning, using the satellite image as an input; and a second generation unit () configured to predict and generate a second image representing probabilities of land types with respect to the satellite image, through the machine learning.
