The AOI we will be working with is located in South Africa. We will use data from the 2019 Zindi Farm Pin Crop Detection Challenge and an abridged pipeline from Sinergise's eo-flow. The architecture we will use is the TensorFlow based TFCN.

Install eo-flow 

!pip install git+https://github.com/sentinel-hub/eo-flow

Download data

Make an account on Zindi and proceed to https://zindi.africa/competitions/farm-pin-crop-detection-challenge/data to download the training and testing shapefiles: train.zip and test.zip

Import required libraries 

import os

# Jupyter notebook related
%reload_ext autoreload
%autoreload 2
%matplotlib inline
%pylab inline

# Basics of Python data handling and visualization
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from mpl_toolkits.axes_grid1 import make_axes_locatable
import pandas as pd
from shapely.geometry import Polygon

# Basics of GIS
import geopandas as gpd

# The core of this example
from eolearn.core import EOTask, EOPatch, LinearWorkflow, FeatureType, OverwritePermission, LoadFromDisk, SaveToDisk
from eolearn.io import S2L1CWCSInput, ExportToTiff
from eolearn.mask import AddCloudMaskTask, get_s2_pixel_cloud_detector, AddValidDataMaskTask
from eolearn.geometry import VectorToRaster, PointSamplingTask, ErosionTask
from eolearn.features import LinearInterpolation, SimpleFilterTask
from sentinelhub import BBoxSplitter, BBox, CRS, CustomUrlParam

# Machine learning 
import lightgbm as lgb
from sklearn.externals import joblib
from sklearn import metrics
from sklearn import preprocessing

# Misc
import pickle
import sys
import os
import datetime
import itertools
from tqdm import tqdm_notebook as tqdm
import enum

Preprocess data

1) Generate convex hull geometries enveloping the training and testing shapefiles, to serve as AOI geometries used when generating EOPatches with Sentinel 2 imagery.

2) Split the AOI into smaller tiles

3) Fill EOPatches with data, to include:

  • L1C list of select bands [B02, B03, B04, B08, B11, B12], corresponding to [B, G, R, NIR, SWIR1, SWIR2] wavelengths.
  • Cloud probability map and cloud mask from SentinelHub
  • NDVI, NDWI, euclidean NORM information, which we will calculate
  • A mask of pixel validity, derived from the acquired Senitnel data and cloud coverage information. We define a valid pixel if its metadata: IS_DATA == True, CLOUD_MASK == 0 (1 indicates that pixel was identified to be occluded by cloud)

Import eo-flow modules 

import tensorflow as tf
import json

from eoflow.models import TFCNModel
from eoflow.input.eopatch import eopatch_dataset
from eoflow.input.operations import augment_data, cache_dataset, extract_subpatches
from eoflow.utils import create_dirs

Convert EOPatch data to tfrecords

tfrecord is TensorFlow's dataset format optimized for ML workflows

Augment the data

Horizontal and vertical flips

Configure the model hyperparameters

To include:

  • learning rate
  • number of classes
  • loss
  • metrics
  • number of iterations and/or epochs (training cycles)

Train model

Predict with the trained model

Evaluate the trained model

We will calculate Intersection over Union as a measure for the quality of our model.

Visualize predicted crop type maps