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Record W6963374840 · doi:10.21227/tr54-c620

Data Fusion Contest 2016 (DFC2016)

2019· dataset· en· W6963374840 on OpenAlexaboutno aff

Bibliographic record

VenueIEEE DataPort · 2019
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPanchromatic filmMultispectral imageOrthophotoImage resolutionSensor fusionImage fusionInterpolation (computer graphics)Radiometry

Abstract

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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 spacingSecond, a video from an in-orbit camera: High-Definition Video acquired from the International Space Station (ISS), at 1-m spatial resolutionThe 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 Data The 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},}

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.056
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.018
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.004
Science and technology studies0.0040.001
Scholarly communication0.0110.006
Open science0.0050.009
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0560.057

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.130
GPT teacher head0.358
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes1
Has abstractyes

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