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.

MLHub model id: model_ecaas_agrifieldnet_silver_v1. Browse on Radiant MLHub.

ML Model Documentation

Please review the model architecture, license, applicable spatial and temporal extents and other details in the model documentation.

System Requirements

• Git client
• Docker with Compose v1.28 or newer.

Hardware Requirements

Inferencing	Training
30 GB RAM	30 GB RAM

Get Started With Inferencing

First clone this Git repository.

git clone https://github.com/masawdah/model_ecaas_agrifieldnet_silver.git
cd model_ecaas_agrifieldnet_silver/

After cloning the model repository, you can use the Docker Compose runtime files as described below.

Pull or Build the Docker Image

Pull pre-built image from Docker Hub (recommended):

# cpu
docker pull docker.io/masawdah/model_ecaas_agrifieldnet_silver:1

Or build image from source:

# cpu
docker build -t masawdah/model_ecaas_agrifieldnet_silver:1 -f Dockerfile_cpu .

Run Model to Generate New Inferences

1. Prepare your input and output data folders. The data/ folder in this repository contains some placeholder files to guide you.

   • The data/ folder must contain:

     • input/chips Sentinel-2 10m imagery chips for inferencing:

       • Images Sentinel-2 10m imagery chips for inferencing:
         • Folder name chip_id e.g. 00c23 Sentinel-2 bands 10m:

           • File name: B01.tif Type=Byte, ColorInterp=Coastal

           • File name: B02.tif Type=Byte, ColorInterp=Blue

           • File name: B03.tif Type=Byte, ColorInterp=Green

           • File name: B04.tif Type=Byte, ColorInterp=Red

           • File name: B05.tif Type=Byte, ColorInterp=RedEdge

           • File name: B06.tif Type=Byte, ColorInterp=RedEdge

           • File name: B07.tif Type=Byte, ColorInterp=RedEdge

           • File name: B08.tif Type=Byte, ColorInterp=NIR

           • File name: B8A.tif Type=Byte, ColorInterp=NIR08

           • File name: B09.tif Type=Byte, ColorInterp=NIR09

           • File name: B11.tif Type=Byte, ColorInterp=SWIR16

           • File name: B12.tif Type=Byte, ColorInterp=SWIR22

           • File Format: GeoTIFF, 256x256

           • Coordinate Reference System: WGS84 / UTM

       • fields Corresponding field ids for each pixel in Sentinel-2 images:
         • Folder name: chip_id e.g. 00c23 Corresponding field ids:

           • File name: field_ids.tif

           • File Format: GeoTIFF, 256x256

           • Coordinate Reference System: WGS84 / UTM

     • /input/checkpoint the model checkpoint lgbms, xgbms, cats. Please note: the model checkpoint is included in this repository.

   • The output/ folder is where the model will write inferencing results.

2. Set INPUT_DATA and OUTPUT_DATA environment variables corresponding with your input and output folders. These commands will vary depending on operating system and command-line shell:

   # change paths to your actual input and output folders
   export INPUT_DATA="/home/my_user/model_ecaas_agrifieldnet_silver/data/input"
   export OUTPUT_DATA="/home/my_user/model_ecaas_agrifieldnet_silver/data/output"

3. Run the appropriate Docker Compose command for your system

   # cpu
   docker-compose up model_ecaas_agrifieldnet_silver_v1_cpu
   
   #  If the user is not added to docker group
   
   sudo -E docker-compose up model_ecaas_agrifieldnet_silver_v1_cpu

4. Wait for the docker compose to finish running, then inspect the OUTPUT_DATA folder for results.

Understanding Output Data

Please review the model output format and other technical details in the model documentation.