Background

The Japanese government has put forth its "Space Industry Vision 2030" in hopes of expanding applicability beyond the space equipment industry to other relevant industries as well, with the aim of fast-paced doubling of the overall space industry's size by 2030. Amidst these circumstances, the Ministry of Economy, Trade and Industry launched Japan’s first satellite data platform “Tellus” intended for industry use on February 21, 2019. Tellus facilitates the use of traditionally difficult-to-handle satellite data by everyone from private companies, universities and research institutions through to individuals while also aiding in the creation of businesses in a broad range of industries and business categories made possible with this free access to space data.

The Tellus Satellite Challenge is a data analysis contest aimed at promoting the use of Tellus, namely in the form of producing a visual representation of satellite data use case models, uncovering outstanding human resources specializing in data analysis, and disseminating information and educating others on the types of satellite data and its various formats. This is the 4th time the event has been held.

The theme of this fourth event is the “Coastline extraction using SAR data.”

As an island nation surrounded by oceans, Japan holds a coast line which nearly has 35,000km in total extension. This is the 6th length in the world, and the 2nd after Philippines for the coast length per land area. Also, it is said that the total beach area in Japan is now 19,000ha, but coastal erosion continues on some coasts after the disappearance of about 13%( which is about 2,400ha) of the coast in the 15 years after 1978. If most of the beaches disappears, the reproduction will be a huge cost.

A coast plays an important role in storm surges and tsunami disaster prevention, by weakening the power of the wave. Also, it will affect the plants & animals that use the coast as a habitat. The serious coast erosion in all over the country occurs when the normal supply cycle of the sand collapse. It is caused due to disasters like earthquakes and typhoons, the change of directions of waves and wind due to the change of the weather, sea level rise due to global warming, and due to human factors like port improvement or dam constructions.

Now, the Japanese government is monitoring all of the coast in the nation by a satellite, to study to discover the signs of beach erosion as soon as possible, and prevent it before it gets too late. By using a satellite, it is possible to monitor a wide range of the coast area with high frequency. But the reality is, that a special skill is required to find out the coast area from the observed data. Therefore, in this challenge, we will like to develop an algorithm that can extract a coast line in a high accuracy from a SAR data.

We will use the observed data of PALSAR-2, loaded on the Advanced Land Observing Satellite “ALOS-2” which is for part of the coast in Japan. PALSAR-2 is a sensor called Synthetic aperture radar (SAR) that irradiates a radio wave to the ground surface and gains the information by the reflected radio wave from the ground surface. The feature of the SAR is that it can record the surface day or night, and also regardless of the weather. Check here for details.

Task Description

Extract the coastline from the L-band SAR data.

Data Description

・L-band SAR image

    Satellite: ALOS-2 (JAXA)
    Size: 600×600～10000×10000 (Depends on the image)
    resolution: 3m × 3m
    HH polarization, wavelength: About 24 cm
    Number of coasts: 17
    Number of images: 55 images, (Train) 25 images、(Test) 30 images

*Polarization is one indicator of the properties of radio waves and indicates the direction of vibration of an electric field. HH transmits horizontally polarized waves and receives horizontally polarized waves.

・Annotation
    Format：JSON

Reference 

　This article summarizes reference papers  regarding coastline extraction.
　https://sorabatake.jp/14130/

Evaluation method

We will evaluate by calculating the error from the actual coast line and the evaluation line, lined vertical from the evaluation point assigned to actual coast line.

　① For one scene, detect the tangent point in pixel level (● in the picture)  where the extracted coast line and evaluation line overlapped.
　② Defining the distance from the evaluation point (● n the picture) to the tangent point detected in ① as an error, calculate the error for each evaluation line.
　　- In case detecting the tangent point from an evaluation line fails (extraction failure),the maximum value of the length of one side (land side or ocean side) of the whole evaluation line in that scene, will be calculated as an error.
　　- In case several tangent points are detected from an evaluation line, the farthest tangent point from the evaluation point on this evaluation line will be adopted to calculate the error.
　③ Calculate the average value of ② for all evaluation line.
　④ Calculate the average of ③ of all of the scene in the evaluation target.

    * The number of the evaluation line depends on the image.
    * For information of the formats and terms of the submitting file, check the readme.txt

Determining Winners

1. The final ranking will be determined using evaluation values based on a correct data set that differs from that used during the contest. Score board rankings and scores will automatically change to a final evaluation when the contest ends.
2. In the event of a tie, the participant that submitted their entry first will be declared the higher ranking contestant.
3. Participants on top of the final rankings chart will be contacted by the secretariat as winner candidates.
4. Winner candidates will be required to submit the following on the condition that the submitting party guarantees that the secretariat shall bear no financial burden for such, and shall not be considered to have infringed on any rights.
　・Source code for model (or the equivalent of such)
　・Learning-complete model
　・Procedure description document enabling reproduction of the prediction process (must clearly explain preprocessing, learning, and prediction processes) The source (include details as to the location of the data, links to pertinent theses, etc.) of any data other than the submitted data, learning-complete models, APIs, etc., if used 
　・Computer/equipment operating environment information, including OS version, software utilized and analysis methods
　・Random number seed (if Random Forest or similar was used to generate random numbers)
　・Degree of model contribution for all explanatory variables (when using a method that enables calculation of degree of prediction)
　・Insights gained through data interpretation, manipulation and modeling
5. Winner candidates will be disqualified should any of the following apply to the winner candidates, or their submitted models, during the reproducibility verification period.
　・The contestant does not respond within the specified timeframe to contact from, or a request/demand from, the contest administrative office regarding contest-related procedures
　・The contestant fails to adhere to participant requirements, rules, etc.
　・The model's prediction results cannot be reproduced
　・Predictions cannot be made when using new data
　・Any other reason for which the secretariat deems the candidate unworthy of winning the contest
6. Winners will be selected from candidates for which reproducibility can be verified.

SIGNATE Ranking
・ This competition is subject to SIGNATE rankings (scores and medals).

Contestant Attitude
・We ask that all contestants strive to achieve practical, useful approaches with top priority placed on achieving corporate goals, helping to solve social problems, sharing research results with the public, and other overarching goals.

System Usage
・The maximum number of submission per day is five.
・Each person is limited to one account only. Using several accounts with a single user or using a single account with several people is prohibited.
・Persons wishing to participate as a team, read here, and  click the “Create Team” button to create a team.(Team creating deadline: 2020/9/30. Up to 5 people including the representative.)
・For the submitted file, the maximum number of pixel will be limited to 30000 for the predicted coast line. Files over 30000 will be an error.

Information handling
・When opening your own algorithm, idea and other materials that were made related to this competition to the public, by specifying that the material was made related to this competition, there will be no restriction.
・But, when opening to the public, specify (including links) that this will be opened in the forum of this competition, so all the participant can browse.

Data Usage
・Submission of manually labeled result data is prohibited.
・Pseudo-labeling evaluation data to use the evaluation data again for learning is prohibited.
・Rewriting of learning data labels and then using said data for model learning is allowed.
・Model learning and predicting using any data aside from that distributed to contestants is prohibited.
・Use of Google Cloud Vision API and other such APIs is prohibited. However, use of open-source learning-complete models, libraries and similar is allowed (source code publicly open and available to anyone, and not designed for commercial use; e.g., Tensorflow, Chainer, Keras, etc.). When using a learning-complete model, clearly cite the source using a hyperlink to relevant documentation or similar. 

Implementation Method
・Learning tools for models are limited to open-source, free tools (Python, R, etc.).
・Divide source code in three parts as shown below (preprocessing, learning and prediction), and carry out implementation for each.
        1. Preprocessing
            Module that reads supplied data, carries out preprocessing, and outputs each file in a format that can be input into the model. Divide into files for learning and files for evaluation ("get_train_data" and "get_test_data" or similar), and define a function for preprocessing. 
        2. Learning
            Module that reads files created in step 1 to enable model learning. Define a function that can output the learning-complete model, feature quantity importance level and cross validation assessment results.
        3. Prediction
            Module that reads the test data created in step 1 and model created in step 2 and outputs prediction results in file format.

Contest sponsor: Japanese Ministry of Economy, Trade and Industry (METI)
Support: SAKURA Internet Inc.
Present: Japan Aerospace eXploration Agency
Cooperation: National Institute for Land and Infrastructure Management

【Additional Notes】
Use of ALOS-2 data:
When using ALOS-2 data, contestants must understand and adhere to the following.
(1) Regarding ALOS-2 data received from SIGNATE Inc., the intellectual property rights and all other rights for this data belong to the Japan Aerospace eXploration Agency (JAXA).
(2) Neither SIGNATE Inc. nor JAXA provides any guarantee regarding the quality of data supplied for this competition, and neither organization will take responsibility for results stemming from ALOS-2 data usage or for any damages incurred through use of said data.

【Matters to be updated】
(The provisions of paragraph 1 of Article 3 [Reward and Vesting of Rights] of the Terms below shall be amended as follows [including the surviving provisions].):
1. Unless otherwise provided for, any Participant shall satisfy the following requirements in order to be entitled to receive a reward in any Competition that offers a reward:
(1) To be a winner;
(2) To grant to the Host and its supporting companies (hereinafter referred to as the “Licensees”) a non-exclusive license without restriction as to term and territory and free of charge to use all rights necessary for modifying the Final Submissions and Submissions for Final Judgment for commercial or non-commercial purposes;
(3) To grant to the Licensees a non-exclusive license without restriction as to term and territory and free of charge to use all rights necessary for publishing and use, the Final Submissions and Submissions for Final Judgment and the Final Submissions and Submissions for Final Judgment as modified on the satellite data platform “Tellus” to be started to be provided in future (hereinafter referred to as “Tellus”) for commercial or non-commercial purposes, and for granting a sublicense to any contractor for the operation of Tellus and user of Tellus (together with the rights as prescribed in the preceding item, hereinafter collectively referred to as the “Rights”);
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(8) Not to breach any provision of these Terms and the Terms of Use.

(The provisions of Article 4 [Confidentiality] of the Terms below shall be amended as follows [including the surviving provisions].):
1. Any Participant shall treat any information and data that they receive from the Company in relation to each Competition (hereinafter referred to as the “Company-Provided Information”) as confidential information and shall not disclose the same to any third party and use the same for any purpose other than for such Competition and purpose specified by the Company separately; provided, however, that the confidential information shall not include any information that falls under any of the following items:
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2. Any Participant shall delete or return to the Company the Company-Provided Information immediately after the completion of each Competition.
3. If there is any separate arrangement in relation to the confidential information in each Competition, the provisions of such arrangement shall prevail over the provisions of these Terms.
4. If any dispute occurs between the Host or other third party and the Company due to the breach by any Participant of the provisions of this Article and such other party makes any claim against the Company, such Participant shall compensate for any damage, loss, expenses (including, but not limited to, attorneys’ fees), lost profits and lost revenues, etc. incurred by the Company.
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