List : 機械学習
Automatic Detection of Parking-Lot Space With Satellite Data Challenges and Points of Improvement for the Joint Project by Sakura Internet, akippa, and Ridge-i
During a talk session on August 4, it was announced that Sakura Internet, akippa, and Ridge-i are working together to develop an algorithm that uses satellite data to find space that can be rented out for parking cars, so we decided to talk with them about the challenges they face whilst developing this service, and the success they have made using Sharp's super-resolution technology.
The 4th Tellus Satellite Challenge is now underway! The theme is “coastline extraction.”
Here is an overview of the 4th Tellus Satellite Challenge, a competition for segmentation using satellite images, with details of the competition, helpful papers, and material.
The “Extracting Difference Between Two Points of Satellite Data” Challenge — A Look at ABEJA’s Difference Extracting Algorithm
We went behind the scenes to ask ABEJA about the applications and future of their difference extracting algorithm!
The conversion of SAR to optical image using pix2pix and analysis of another SAR image with this generator
Using GANs, which has become a popular topic in recent years as an image generation algorithm, I tried to convert non-intuitive SAR images into optical images.
Get super-resolution for satellite images using SRCNN [with code]
In this article, we will try to get super-resolution images of actual satellite data using Tellus.
Super-Resolution Processing of Satellite Images Using Sharp’s Deep Learning Model
Super-resolution is a technique to artificially raise the resolution of an image. Super-resolution is one of the hot topics in the field of machine learning, but what happens when you combine it with satellite imagery? We went to the Sharp Corporation Research and Development HQ, and asked the manager of the 3rd Research Team for Communication & Image Technology Laboratories, Tomohiro Ikai, and researcher, Eiichi Sasaki about the future of this technology.