Data Fusion Contest 2016 (DFC2016)
Notice bibliographique
Résumé
The Data Fusion Contest 2016: Goals and OrganizationThe 2016 IEEE GRSS Data Fusion Contest, organized by the IEEE GRSS Image Analysis and Data Fusion Technical Committee, aimed at promoting progress on fusion and analysis methodologies for multisource remote sensing data. New multi-source, multi-temporal data including Very High Resolution (VHR) multi-temporal imagery and video from space were released. First, VHR images (DEIMOS-2 standard products) acquired at two different dates, before and after orthorectification: Panchromatic data at 1 m spatial resolution point spacing Multispectral data at 4 m spatial resolution point spacing Second a video from in-orbit camera: High-Definition Video acquired from the International Space Station (ISS), at 1-m spatial resolution The DataThe imaging data were acquired on March, 31, and May, 30, 2015, over Vancouver, Canada (49°15’N 123°6’W), from the DEIMOS-2 satellite. DEIMOS-2 operates from a Sun-synchronous orbit at a mean altitude of 620km. The spacecraft design is based on an agile platform for fast and precise off-nadir imaging (up to +/-30° over nominal scenarios and up to +/-45° in emergency cases), and carries a push-broom very high resolution camera with 5 spectral channels (1 panchromatic, 4 multispectral with red, green, blue and NIR bands). For each date, four images are provided: panchromatic images at 1 m resolution and multispectral product (R, G, B, NIR) at 4 m resolution, both at levels 1B (a calibrated and radiometrically corrected product, not resampled; with the geometric information contained in a RPC separated file) and 1C (a calibrated and radiometrically corrected product, manually orthorectified and resampled to a map grid; the geometric information is contained in the GeoTIFF tags.) Level 1C images cover exactly the same ground area for both dates. The full colour, UHD video was acquired over Vancouver on July, 2nd, 2015. The High-Resolution camera, Iris, is installed on the Zvezda module of the International Space Station (ISS). Iris uses a CMOS detector to capture RGB videos with a Ground Sample Distance as fine as 1-meter, at 3 frames per second. Iris videos use image frames that have been fully ortho-rectified and resampled to 1-meter. Frame format is 3840×2160 pixels and cover approximately 3.8km × 2.1km.Results, Awards, and Prizes2016 IEEE GRSS Data Fusion Contest ResultsAcknowledgmentThe Contest is being organized in collaboration with Deimos Imaging and UrtheCast. The IADF TC wishes to express its greatest appreciation to Deimos Imaging and UrtheCast, for acquiring and providing the data used in the competition and for indispensable contribution to the organization of the Contest, and to the IEEE GRSS for continuously supporting the annual Data Fusion Contest through funding and resources.How to Get the DataThe data were provided for the purpose of participation in the 2016 Data Fusion Contest, however they remain available to the community for futher research purposes. To request the data set, please click here to proceed to registration.By submitting the registration form, participants acknowledge that they have read the following Contest Terms and Conditions, and that they agree to these terms and conditions: The owner of the data and of the copyright on the data is Deimos Imaging, Spain. The data are only available for the scientific purposes of the 2016 IEEE GRSS Data Fusion Contest. Any dissemination or distribution of the data by any registered user is strictly forbidden. The data can be used in scientific publications subject to approval by the IEEE GRSS Image Analysis and Data Fusion Technical Committee and by the data owner on a case-by-case basis. To submit a scientific publication for approval, the publication shall be sent as an attachment to an e-mail addressed to iadf_chairs@grss-ieee.org and roberto.fabrizi@deimos.com. Any scientific publication using the data shall include a section “Acknowledgement”. This section shall include the following sentence: “The authors would like to thank Deimos Imaging for acquiring and providing the data used in this study, and the IEEE GRSS Image Analysis and Data Fusion Technical Committee.” In any scientific publication using the data, the data shall be identified as “grss_dfc_2016” and shall be referenced as follows: “[REF. NO.] 2016 IEEE GRSS Data Fusion Contest. Online: http://www.grss-ieee.org/community/technical-committees/data-fusion”. The following Open-Access article which summarized the outcomes of the 2016 Data Fusion Contest should be cited: https://ieeexplore.ieee.org/document/7948767@ARTICLE{7948767,author={L. {Mou} and X. {Zhu} and M. {Vakalopoulou} and K. {Karantzalos} and N. {Paragios} and B. {Le Saux} and G. {Moser} and D. {Tuia}},journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},title={Multitemporal Very High Resolution From Space: Outcome of the 2016 IEEE GRSS Data Fusion Contest},year={2017},volume={10},number={8},pages={3435-3447},doi={10.1109/JSTARS.2017.2696823},ISSN={},month={Aug},}
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,021 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,011 | 0,006 |
| Science ouverte | 0,005 | 0,009 |
| Intégrité de la recherche | 0,005 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,054 | 0,055 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».