ml-model random-forest classification supervised |
This model classifies crop types for each field based on the field as well as on its surroundings. In the Zindi AgriFieldNet India Challenge this was the third place solution by the team re-union
in the final round to classify crop types in agricultural fields across Northern India using multispectral observations from Sentinel-2 satellite.
Model ID | model_ecaas_agrifieldnet_bronze_v1 |
DOI | 10.34911/rdnt.iaki1s |
Creator | MG Ferreira, Tien Dung |
License | CC-BY-4.0 |
Applicable Temporal Extent | 2022-01-01 / present |
Ferreira, M.G. and Dung, T. "Looking further: a crop type classification model for fields", Version 1.0, Radiant MLHub. [Date Accessed] Radiant MLHub <https://doi.org/10.34911/rdnt.iaki1s>
docker pull docker.io/radiantearth/model_ecaas_agrifieldnet_bronze:1
Inference Runtime | Model Inferencing Runtime |
Checkpoint | Final Model Checkpoint |
Training Data | AgriFieldNet Competition Dataset - Source Imagery, AgriFieldNet Competition Dataset - Train Labels |
Inferencing Image | docker.io/radiantearth/model_ecaas_agrifieldnet_bronze:1 |
Related MLHub Dataset | AgriFieldNet Competition Dataset |