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base repository: radiantearth/model_ecaas_agrifieldnet_silver
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  • 2 commits
  • 2 files changed
  • 1 contributor

Commits on Dec 21, 2022

  1. Add Zindi challenge info

    Alex G Rice committed Dec 21, 2022
    Copy the full SHA
    524d747 View commit details
  2. Merge pull request #2 from radiantearth/fix/docs-add-zindi

    Add Zindi challenge info
    Alex G Rice authored Dec 21, 2022
    Copy the full SHA
    a4513c4 View commit details
Showing with 13 additions and 6 deletions.
  1. +7 −1 README.md
  2. +6 −5 docs/index.md
8 changes: 7 additions & 1 deletion README.md
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# Weighted Tree-based Crop Classification Models for Imbalanced Datasets

Second place solution to classify crop types in agricultural fields across Northern India using multispectral observations from Sentinel-2 satellite. Ensembled weighted tree-based models "LGBM, CATBOOST, XGBOOST" with stratified k-fold cross validation, taking advantage of spatial variability around each field within different distances.
Second place solution
in the [Zindi AgriFieldNet India Challenge](https://zindi.africa/competitions/agrifieldnet-india-challenge)
to classify crop types in agricultural fields across Northern India using
multispectral observations from Sentinel-2 satellite. Ensembled weighted
tree-based models "LGBM, CATBOOST, XGBOOST" with stratified k-fold cross
validation, taking advantage of spatial variability around each field within
different distances.

![model_ecaas_agrifieldnet_silver_v1](https://radiantmlhub.blob.core.windows.net/frontend-ml-model-images/model_ecaas_agrifieldnet_silver_v1.png)

11 changes: 6 additions & 5 deletions docs/index.md
Original file line number Diff line number Diff line change
@@ -1,10 +1,11 @@
# Weighted Tree-based Crop Classification Models for Imbalanced Datasets

Second place solution to classify crop types in agricultural fields across
Northern India using multispectral observations from Sentinel-2 satellite.
Ensembled weighted tree-based models "LGBM, CATBOOST, XGBOOST" with stratified
k-fold cross validation, taking advantage of spatial variability around each
field within different distances.
Second place solution in the [Zindi AgriFieldNet India Challenge](https://zindi.africa/competitions/agrifieldnet-india-challenge)
to classify crop types in agricultural fields across Northern India using
multispectral observations from Sentinel-2 satellite. Ensembled weighted
tree-based models "LGBM, CATBOOST, XGBOOST" with stratified k-fold cross
validation, taking advantage of spatial variability around each field within
different distances.

![model_ecaas_agrifieldnet_silver_v1](https://radiantmlhub.blob.core.windows.net/frontend-ml-model-images/model_ecaas_agrifieldnet_silver_v1.png)