お知らせ

2018/11/12     最終評価に対するPrecision, Recall, F-measureの値一覧を「各スコアの一覧」タブに掲載しました。参考にしてください。
2018/12/19     本コンペティションの元データを用いた研究論文が出版されました。詳細(プレスリリース)はこちら


背景

台風やハリケーンなどの大型の熱帯低気圧は人間生活、農業、林業、漁業、インフラに対して甚大な被害をもたらします。熱帯低気圧の発生をできる限り早く予測することは非常に重要な課題です。ところが、発生前の熱帯低気圧(熱帯低気圧のタマゴ)は専門家でさえも正しく識別するのは難しく、熱帯低気圧の発生予測を精度よく行うのは難しいのが現状です。

今回は海洋研究開発機構が提供する雲解像気象モデルNICAM [1], [2], [3]によって生成された1984年から2000年の合計17年分の大気シミュレーションデータ(画像化されたデータ)を用いて熱帯低気圧とそのタマゴを検出するアルゴリズムの作成に挑戦していただきます。 

※ NICAMは海洋研究開発機構、東京大学、理化学研究所によって共同開発されました。
※※ 本データは文部科学省のHPCI戦略プログラム分野3「防災・減災に資する地球変動予測」のもと、理化学研究所のスーパーコンピュータ「京」を利用して得られたものです(課題番号:hp120279,hp130010,hp140219)。

タスク説明

パッチ画像に対してそれが熱帯低気圧またはそのタマゴを含むか否かを判別していただきます。



懸賞

1位 NVIDIA賞:NVIDIA TITAN V    ※1

2位 GDEP賞:GeForce RTX 2080 Ti    ※2

3位 アーク情報システム賞:ELSA GeForce GTX 1080 Ti 11GB ST    ※3

4位 CRAY賞:GoPro HERO6    ※4

※1 エヌビディア合同会社より提供
※2 GDEPソリューションズ株式会社株式会社GDEPアドバンスより提供
※3 (株)アーク情報システムより提供
※4 クレイ・ジャパン・インクより提供


データ説明

画像:64px×64pxのグレースケールパッチ画像

  水平解像度:14 km

学習用画像データ:2,244,223枚(1984年~1998年の15年分)

  正例:71,779枚
  負例:2,172,444枚

評価用画像データ:299,135枚(1999年~2000年の2年分)

参考文献

[1] C. Kodama, Y. Yamada, A. T. Noda, K. Kikuchi, Y. Kajikawa, T. Nasuno, T. Tomita, T. Yamaura, H. G. Takahashi, M. Hara, Y. Kawatani, M. Satoh, and M. Sugi “A 20-year climatology of a NICAM AMIPtype simulation,” Journal of the Meteorological Society of Japan, vol. 93, no. 4, pp. 393–424, 2015.

[2] H. Tomita, and M. Satoh, “A new dynamical framework of nonhydrostatic global modeling using the icosahedral grid,” Fluid Dynamics, vol. 1, no. 8, pp. 357–400, 2004. 

[3]M. Satoh, H. Tomita, H. Yashiro, H. Miura, C. Kodama, T. Seiki, A. T. Noda, Y. Yamada, D. Goto, M. Sawada, T. Miyoshi, Y. Niwa, M. Hara, T. Ohno, S. Iga, T. Arakawa, T. Inoue, and H. Kubokawa, “The Non-hydrostatic icosahedral atmospheric model: Description and development,” Progress in Earth and Planetary Science, vol. 1, pp. 1–32, 2014. 

評価関数 

精度評価には、評価関数「条件付きPrecision」を使用します(下式参照)。 この関数はモデルの予測精度の”良さ”を表すため、大きい値であるほど優れています。


最終順位の決定 

1. コンペ最終日までの評価(暫定評価)は評価用データセットの一部で評価し、コンペ終了後の評価(最終評価)は評価用データセットの残りの部分で評価します(※リーダーボードはコンペ終了時に自動的に最終評価に切り替わり、それを元に最終順位を決定します。このため、開催中と終了後では順位が大きく変動する場合もあります。)。

2. 評価指標が、タイ(同値)の場合は、早い日時でご応募いただいた参加者を上位とします。 

3. 順位確定の際に下記の情報を提出していただきます。 

 ・予測モデルのソースコード
 ・学習済モデル
 ・予測結果の再現の為の手順書(前処理部分、学習部分、予測部分が分かるよう明記)
 ・実行環境(OSのバージョン、使用ソフトウェア及び解析手法)
 ・乱数シード(Random Forest等の乱数を利用した手法の場合)
 ・各説明変数の予測モデルへの寄与度(寄与度の算出が可能な手法を用いた場合)
 ・データの解釈、工夫点、モデリングから得られる示唆等 

4. 再現性検証期間中、入賞候補者及び、その提出モデルが下記いずれかに該当する場合は懸賞の獲得資格を失います。 

 ・事務局からの手続き上の連絡・要求に対して指定された期限内に対応しない
 ・参加条件やルールを満たしていない
 ・モデルの予測結果を再現できない
 ・新しい画像に対して予測できない

1ユーザにつき1アカウント

 コンテスト参加者は1人につき1アカウントまでです。ただし、アカウントが1つという条件の元であれば、チームでの参加も可能とします。
 チームで参加される場合は以下の手続きをお願いします。
     1. チームメンバーの全員がSIGNATEへユーザー登録を行う
     2. チーム代表者は自身と全チームメンバーのemailアドレスをお問い合わせフォームより送信

他参加者との情報共有は禁止

 コンテスト参加者が同じチーム以外の参加者と本コンテストの予測に関連するデータ・ソースコードを共有する行為は禁止です。

手動でのラベル付は禁止

 手動でラベル付けした結果を提出することは禁止します。

学習データの改ざんは禁止

 学習データのラベルを書き換えてモデルを学習することは禁止します。
 ただし、学習用画像データを画像処理手法により自動で水増しして学習用データとして利用することは可能です。

学習データは提供データのみ

 配布するデータ以外のデータを用いてモデルを学習することは禁止します。
 評価用データに対して擬似ラベリングを行い、評価用データを学習用として改めて利用することは禁止します。

APIの使用(Google Cloud Vision API等)は禁止

 ただし、 オープンソース(ソースが明確で、誰でもアクセスでき、かつ商用ではない) の学習済みモデルやライブラリ(tensorflow, chainer, keras, etc)は使用可能です。
 学習済みモデルを使用する際は、ソース(論文のリンク等)を明記してください。

分析ガイドライン

 1. 前処理、学習、予測がわかるようにそれぞれ分けて実装してください。

  ・例えばPythonで実装する場合、前処理はpreprocess.py、学習はtrain.py、予測はpredict.pyをそれぞれ実行すれば処理が進むように実装する。

 2. ソースコードとともに、学習済みモデルも提出してください。

  ・検収する際は学習済みモデルにより評価用データに対する予測値(応募用ファイル)を出力し、評価したときにリーダーボードのスコアを再現できるかどうかを確認します。

 3. ソースコードを提出する前に、最終的に提出した分析結果の予測値とご自身の環境で出力した予測値とが一致するか確認してください。

  ・評価用データや学習済みモデルなどのパスが正しいか確認してください。
  ・処理を実行するためのコマンドが正しいか確認してください。

主催:

国立研究開発法人 海洋研究開発機構

協賛:


 

 

最終評価に対するPrecision, Recall, F-measureの値一覧


 

Disclosure policy

As a general rule, in accordance with Article 4, Paragraph 1 of the terms of participation, diclosing any contents such as insights and deliverables transmitted through the information or data provided by our company in relation to this competition is not permitted, however, only after the completion of this competition and for non-commercial purposes, it will be possible to disclose the contents within the score of the table below
Model *1
Public
Analysis results *2
Public
Public : Posting to social media sites, blogs and source repositories, and citing to papers
Restricted : Using in a limited range from research, education to seminars, where many unspecified people cannot access
*1 Execution unit source code and learned models
*2 The insights obtained using the information and data provided, or the solutions including scripts and processed data such as summary statistics

※Notes

・データに関して、ファイルそのものの再配布は禁止ですが、分析結果として何枚かの画像データの公開は可能です。
・本データを用いた情報をSNSやブログ、論文、セミナーなどのあらゆる場所において公開する場合は下記の情報を明記すること。

〇コンペティションについて
「海洋研究開発機構 熱帯低気圧(台風等)検出アルゴリズム作成」コンペティション
https://signate.jp/competitions/134

〇データについて
C. Kodama, Y. Yamada, A. T. Noda, K. Kikuchi, Y. Kajikawa, T. Nasuno,
T. Tomita, T. Yamaura, H. G. Takahashi, M. Hara, Y. Kawatani, M.
Satoh, and M. Sugi “A 20-year climatology of a NICAM AMIPtype
simulation,” Journal of the Meteorological Society of Japan, vol. 93,
no. 4, pp. 393–424, 2015.
https://www.jstage.jst.go.jp/article/jmsj/93/4/93_2015-024/_article

D. Matsuoka, M. Nakano, D. Sugiyama, and S. Uchida "Deep learning
approach for detecting tropical cyclones and their precursors in the
simulation by a cloud-resolving global nonhydrostatic atmospheric
model", Progress in Earth and Planetary Science, doi:
10.1186/s40645-018-0245-y, 2018
https://progearthplanetsci.springeropen.com/articles/10.1186/s40645-018-0245-y

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