AI Collection

Conference item : Poster

Integrating field observations and machine learning for national-scale agricultural yield classification from sentinel-2

Publication status:: Not published

Peer review status:: Reviewed (other)

Actions

Email

Email this record

Send the bibliographic details of this record to your email address.

Your Email
Please enter the email address that the record information will be sent to.

-
Your message (optional)
Please add any additional information to be included within the email.
Share
Cite

Cite this record

APA Style

Nazir, U., & Khalid, S. (2026). Integrating field observations and machine learning for national-scale agricultural yield classification from sentinel-2. 5th ECMWF-ESA Machine Learning Workshop.

MLA Style

Nazir, U, and S Khalid. “Integrating Field Observations and Machine Learning for National-Scale Agricultural Yield Classification from Sentinel-2.” 5th ECMWF-ESA Machine Learning Workshop, 2026.

Chicago Style

Nazir, U, and S Khalid. 2026. “Integrating Field Observations and Machine Learning for National-Scale Agricultural Yield Classification from Sentinel-2.” In 5th ECMWF-ESA Machine Learning Workshop.
Print

Access Document

Files:: Nazir_and_Khalid_2026_Integrating_field_observations.pdf

(Preview, Accepted manuscript, pdf, 54.3KB, Terms of use)

Authors

+ Nazir, U More by this author

Institution:: University of Oxford
Division:: MSD
Department:: NDORMS
Sub department:: Botnar Institute for Musculoskeletal Sciences
Role:: Author
ORCID:: 0000-0001-5741-9062

+ Khalid, S More by this author

Institution:: University of Oxford
Division:: MSD
Department:: NDORMS
Sub department:: Botnar Institute for Musculoskeletal Sciences
Role:: Author
ORCID:: 0000-0002-2845-5731

Acceptance date:: 2026-01-12
Event title:: 5th ECMWF-ESA Machine Learning Workshop
Event location:: Bologna, Italy
Event website:: https://events.ecmwf.int/event/488/
Event start date:: 2026-04-13
Event end date:: 2026-04-17

Language:: English
Subtype:: Poster
Pubs id:: 2405327
Local pid:: pubs:2405327
Deposit date:: 2026-04-13
ARK identifier:: ark:/29072/ora_cb264d5719e34dd5bf4aecee56993257

Terms of use

Copyright date:: 2026
Notes:: The author accepted manuscript (AAM) of this paper has been made available under the University of Oxford's Open Access Publications Policy, and a CC BY public copyright licence has been applied.

Licence:: CC Attribution (CC BY)

Views and Downloads

About views and downloads

If you are the owner of this record, you can report an update to it here: Report update to this record

TO TOP