Tellus Satellite BootCamp is a hands-on seminar aimed at promoting the creation of new Tellus users.
In order to teach the participants how to analyze satellite data, with the data and tools mounted on Tellus, lectures will be given regarding the fundamentals of how to use Tellus as well as practical techniques.

*In fiscal 2019, a total of 80 attendees participated in the events held at venues in Tokyo and Osaka.
Tokyo venue: Saturday, October 26 and Sunday, October 27, 2019
Osaka venue: Saturday, November 16 and Sunday, November 17, 2019

Tellus Trainer consists of e-Learning contents which offer lectures on the contents of the Tellus Satellite BootCamp that can be taken online instead.
It is a platform where one can learn the fundamentals of satellite data and its analysis techniques.

*In fiscal 2019, more than 1,000 applicants were able to receive the lectures.

While TellusTrainer was designed on the assumption that attendees have a certain level of programming knowledge (can use Python), the Beginner-oriented Tellus Learning Course offers content aimed at users who have not yet reached that level, with the aim of further discovering new users and training them.
In this course, one can learn simple image processing and satellite image processing, as well as techniques for numerical estimations online using the programming language Python, along with chat support.

*In fiscal 2019, 200 applicants were able to participate in this course.

In recent years, with the increasing risk of natural disasters, rapid understanding of landslides during natural disasters is extremely important from a standpoint of saving human lives, so we have been using observation satellites to monitor these situations as a form of emergency response.
While the evaluation of landslides through the data obtained is analyzed by highly skilled experts, such evaluation is not a simple task. In this contest, we decided to work towards the development of an algorithm that can detect regions where landslides may occur with high precision using satellite image data.

For Japan, an island country surrounded by the sea, a comprehensive grasp of the various activities related to ships in water bodies is extremely important for the operation and safety of the water transportation and fishing industries.
In this contest, using the special characteristics of the data from a wide area observation satellite, we aimed at developing a high precision algorithm for ship detection in water areas.
Further, we attempted to determine whether the ship was still or moving, as well as distinguish between the various types of ships through the data used this time which had high resolution characteristics.

Artificial satellites which can grasp the conditions of the ocean surface from a wide area at once can provide useful data.
Among them, deciphering the areas covered with sea ice from the data captured by the Synthetic Aperture Radar (SAR), which can capture images in day and night regardless of weather conditions, requires a high level of skill. In this contest, we aimed at developing an algorithm which can detect areas with sea ice from SAR data with higher precision.