How To: Land-Use-Land-Cover Prediction for Slovenia

This notebook shows the steps towards constructing a machine learning pipeline for predicting the land use and land cover for the region of Republic of Slovenia. We will use satellite images obtained by ESA’s Sentinel-2 to train a model and use it for prediction. The example will lead you through the whole process of creating the pipeline, with details provided at each step.

Before you start


In order to run the example you’ll need a Sentinel Hub account. If you do not have one yet, you can create a free trial account at Sentinel Hub webpage. If you are a researcher you can even apply for a free non-commercial account at ESA OSEO page.

Once you have the account set up, please configure the sentinelhub package’s configuration file following the configuration instructions. For Processing API request you need to obtain and set your oauth client id and secret.


Part 1:

  1. Define the Area-of-Interest (AOI):

  • Obtain the outline of Slovenia (provided)

  • Split into manageable smaller tiles

  • Select a small 5x5 area for classification

  1. Use the integrated sentinelhub-py package in order to fill the EOPatches with some content (band data, cloud masks, …)

  • Define the time interval (this example uses the whole year of 2019)

  1. Add additional information from band combinations (norm. vegetation index - NDVI, norm. water index - NDWI)

  2. Add a reference map (provided)

  • Convert provided vector data to raster and add it to EOPatches

Part 2:

  1. Prepare the training data

  • Remove too cloudy scenes

  • Perform temporal interpolation (filling gaps and resampling to the same dates)

  • Apply erosion

  • Random spatial sampling of the EOPatches

  • Split patches for training/validation

  1. Construct and train the ML model

  • Make the prediction for each patch

  1. Validate the model

  2. Visualise the results

Let’s start!

# Firstly, some necessary imports

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

# Built-in modules
import pickle
import sys
import os
import datetime
import itertools
from aenum import MultiValueEnum

# Basics of Python data handling and visualization
import numpy as np
import geopandas as gpd
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib.colors import ListedColormap, BoundaryNorm
from mpl_toolkits.axes_grid1 import make_axes_locatable
from shapely.geometry import Polygon
from import tqdm

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

# Imports from eo-learn and sentinelhub-py
from eolearn.core import EOTask, EOPatch, LinearWorkflow, FeatureType, OverwritePermission, \
    LoadTask, SaveTask, EOExecutor, ExtractBandsTask, MergeFeatureTask
from import SentinelHubInputTask, VectorImportTask, ExportToTiffTask
from eolearn.mask import AddValidDataMaskTask
from eolearn.geometry import VectorToRasterTask, PointSamplingTask, ErosionTask
from eolearn.features import LinearInterpolationTask, SimpleFilterTask, NormalizedDifferenceIndexTask
from sentinelhub import UtmZoneSplitter, BBox, CRS, DataCollection

Part 1

1. Define the Area-of-Interest (AOI):

  • A geographical shape of Slovenia was taken from Natural Earth database and a buffer of 500 m was applied. The shape is available in repository: example_data/svn_border.geojson

  • Convert it to selected CRS: taken to be the CRS of central UTM tile (UTM_33N)

  • Split it into smaller, manageable, non-overlapping rectangular tiles

  • Run classification on a selected 5x5 area

Be sure that your choice of CRS is the same as the CRS of your reference data.

In the case that you are having problems with empty data being downloaded, try changing the CRS to something that suits the location of the AOI better.

Get country boundary

# Folder where data for running the notebook is stored
DATA_FOLDER = os.path.join('..', '..', 'example_data')
# Locations for collected data and intermediate results
EOPATCH_FOLDER = os.path.join('.', 'eopatches')
EOPATCH_SAMPLES_FOLDER = os.path.join('.', 'eopatches_sampled')
RESULTS_FOLDER = os.path.join('.', 'results')
os.makedirs(EOPATCH_FOLDER, exist_ok=True)
os.makedirs(EOPATCH_SAMPLES_FOLDER, exist_ok=True)
os.makedirs(RESULTS_FOLDER, exist_ok=True)

# Load geojson file
country = gpd.read_file(os.path.join(DATA_FOLDER, 'svn_border.geojson'))
# Add 500m buffer to secure sufficient data near border
country = country.buffer(500)

# Get the country's shape in polygon format
country_shape = country.geometry.values[0]

# Plot country

# Print size
country_width = country_shape.bounds[2] - country_shape.bounds[0]
country_height = country_shape.bounds[3] - country_shape.bounds[1]
print(f'Dimension of the area is {country_width:.0f} x {country_height:.0f} m2')
Dimension of the area is 243184 x 161584 m2

Split to smaller tiles and choose a 5x5 area

The splitting choice depends on the available resources of your computer. An EOPatch with a size of has around 500 x 500 pixels at 10 meter resolution has a size ob about ~1 GB.

# Create a splitter to obtain a list of bboxes with 5km sides
bbox_splitter = UtmZoneSplitter([country_shape],, 5000)

bbox_list = np.array(bbox_splitter.get_bbox_list())
info_list = np.array(bbox_splitter.get_info_list())

# Prepare info of selected EOPatches
geometry = [Polygon(bbox.get_polygon()) for bbox in bbox_list]
idxs = [info['index'] for info in info_list]
idxs_x = [info['index_x'] for info in info_list]
idxs_y = [info['index_y'] for info in info_list]

bbox_gdf = gpd.GeoDataFrame({'index': idxs, 'index_x': idxs_x, 'index_y': idxs_y},
                  , geometry=geometry)

# select a 5x5 area (id of center patch)
ID = 616

# Obtain surrounding 5x5 patches
patchIDs = []
for idx, (bbox, info) in enumerate(zip(bbox_list, info_list)):
    if (abs(info['index_x'] - info_list[ID]['index_x']) <= 2 and
        abs(info['index_y'] - info_list[ID]['index_y']) <= 2):

# Check if final size is 5x5
if len(patchIDs) != 5*5:
    print('Warning! Use a different central patch ID, this one is on the border.')

# Change the order of the patches (useful for plotting)
patchIDs = np.transpose(np.fliplr(np.array(patchIDs).reshape(5, 5))).ravel()

# Save to shapefile
shapefile_name = 'grid_slovenia_500x500.gpkg'
bbox_gdf.to_file(os.path.join(RESULTS_FOLDER, shapefile_name), driver='GPKG')

Visualize the selection

# Display bboxes over country
fig, ax = plt.subplots(figsize=(30, 30))
ax.set_title('Selected 5x5 tiles from Slovenia', fontsize=25)
country.plot(ax=ax, facecolor='w', edgecolor='b', alpha=0.5)
bbox_gdf.plot(ax=ax, facecolor='w', edgecolor='r', alpha=0.5)

for bbox, info in zip(bbox_list, info_list):
    geo = bbox.geometry
    ax.text(geo.centroid.x, geo.centroid.y, info['index'], ha='center', va='center')

# Mark bboxes of selected area
bbox_gdf[bbox_gdf.index.isin(patchIDs)].plot(ax=ax, facecolor='g', edgecolor='r', alpha=0.5)


2. - 4. Fill EOPatches with data:

Now it’s time to create EOPatches and fill them with Sentinel-2 data using Sentinel Hub services. We will add the following data to each EOPatch:

  • L1C custom list of bands [B02, B03, B04, B08, B11, B12], which corresponds to [B, G, R, NIR, SWIR1, SWIR2] wavelengths.

  • SentinelHub’s cloud mask

Additionally, we will add:

  • Calculated NDVI, NDWI, and NDBI information

  • A mask of validity, based on acquired data from Sentinel and cloud coverage. Valid pixel is if:

    1. IS_DATA == True

    2. CLOUD_MASK == 0 (1 indicates cloudy pixels and 255 indicates NO_DATA)

An EOPatch is created and manipulated using EOTasks, which are chained in an EOWorkflow. In this example the final workflow is executed on all patches, which are saved to the specified directory.

Define some needed custom EOTasks

class SentinelHubValidData:
    Combine Sen2Cor's classification map with `IS_DATA` to define a `VALID_DATA_SH` mask
    The SentinelHub's cloud mask is asumed to be found in eopatch.mask['CLM']
    def __call__(self, eopatch):
        return eopatch.mask['IS_DATA'].astype(bool) & np.logical_not(eopatch.mask['CLM'].astype(bool))

class AddValidCountTask(EOTask):
    The task counts number of valid observations in time-series and stores the results in the timeless mask.
    def __init__(self, count_what, feature_name):
        self.what = count_what = feature_name

    def execute(self, eopatch):
        eopatch[(FeatureType.MASK_TIMELESS,] = np.count_nonzero(eopatch.mask[self.what], axis=0)
        return eopatch

Define the workflow tasks

# Add a request for S2 bands.
# Here we also do a simple filter of cloudy scenes (on tile level).
# The s2cloudless masks and probabilities are requested via additional data.
band_names = ['B02', 'B03', 'B04', 'B08', 'B11', 'B12']
add_data = SentinelHubInputTask(
    bands_feature=(FeatureType.DATA, 'BANDS'),
    bands = band_names,
    additional_data=[(FeatureType.MASK, 'dataMask', 'IS_DATA'),
                     (FeatureType.MASK, 'CLM'),
                     (FeatureType.DATA, 'CLP')],

# NDVI: (B08 - B04)/(B08 + B04)
# NDWI: (B03 - B08)/(B03 + B08)
# NDBI: (B11 - B08)/(B11 + B08)
ndvi = NormalizedDifferenceIndexTask((FeatureType.DATA, 'BANDS'), (FeatureType.DATA, 'NDVI'),
                                     [band_names.index('B08'), band_names.index('B04')])
ndwi = NormalizedDifferenceIndexTask((FeatureType.DATA, 'BANDS'), (FeatureType.DATA, 'NDWI'),
                                     [band_names.index('B03'), band_names.index('B08')])
ndbi = NormalizedDifferenceIndexTask((FeatureType.DATA, 'BANDS'), (FeatureType.DATA, 'NDBI'),
                                     [band_names.index('B11'), band_names.index('B08')])

# Validate pixels using SentinelHub's cloud detection mask and region of acquisition
add_sh_validmask = AddValidDataMaskTask(SentinelHubValidData(), 'IS_VALID')

# Count the number of valid observations per pixel using valid data mask
add_valid_count = AddValidCountTask('IS_VALID', 'VALID_COUNT')

# SAVING TO OUTPUT (if needed)
save = SaveTask(EOPATCH_FOLDER, overwrite_permission=OverwritePermission.OVERWRITE_PATCH)

Help! I prefer to calculate cloud masks of my own!

If you wish to calculate s2cloudless masks and probabilities (almost) from scratch, you can do this by using the following two EOTasks instead of the first one above

band_names = ['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B10', 'B11', 'B12']
add_data = SentinelHubInputTask(
    bands_feature=(FeatureType.DATA, 'BANDS'),
    bands = band_names,
    additional_data=[(FeatureType.MASK, 'dataMask', 'IS_DATA')],

add_clm = CloudMaskTask(data_feature='BANDS',
                        mono_features=('CLP', 'CLM'),

Reference map task

For this example, a subset of the country-wide reference for land-use-land-cover is provided. It is available in the form of a geopackage, which contains polygons and their corresponding labels. The labels represent the following 10 classes:

  • lulcid = 0, name = no data

  • lulcid = 1, name = cultivated land

  • lulcid = 2, name = forest

  • lulcid = 3, name = grassland

  • lulcid = 4, name = shrubland

  • lulcid = 5, name = water

  • lulcid = 6, name = wetlands

  • lulcid = 7, name = tundra

  • lulcid = 8, name = artificial surface

  • lulcid = 9, name = bareland

  • lulcid = 10, name = snow and ice

We have defined a land cover enum class for ease of use below.

class LULC(MultiValueEnum):
    """ Enum class containing basic LULC types
    NO_DATA            = 'No Data',            0,  '#ffffff'
    CULTIVATED_LAND    = 'Cultivated Land',    1,  '#ffff00'
    FOREST             = 'Forest',             2,  '#054907'
    GRASSLAND          = 'Grassland',          3,  '#ffa500'
    SHRUBLAND          = 'Shrubland',          4,  '#806000'
    WATER              = 'Water',              5,  '#069af3'
    WETLAND            = 'Wetlands',           6,  '#95d0fc'
    TUNDRA             = 'Tundra',             7,  '#967bb6'
    ARTIFICIAL_SURFACE = 'Artificial Surface', 8,  '#dc143c'
    BARELAND           = 'Bareland',           9,  '#a6a6a6'
    SNOW_AND_ICE       = 'Snow and Ice',       10, '#000000'

    def id(self):
        """ Returns an ID of an enum type

        :return: An ID
        :rtype: int
        return self.values[1]

    def color(self):
        """ Returns class color

        :return: A color in hexadecimal representation
        :rtype: str
        return self.values[2]

def get_bounds_from_ids(ids):
    bounds = []
    for i in range(len(ids)):
        if i < len(ids) - 1:
            if i == 0:
                diff = (ids[i + 1] - ids[i]) / 2
                bounds.append(ids[i] - diff)
            diff = (ids[i + 1] - ids[i]) / 2
            bounds.append(ids[i] + diff)
            diff = (ids[i] - ids[i - 1]) / 2
            bounds.append(ids[i] + diff)
    return bounds

# Reference colormap things
lulc_bounds = get_bounds_from_ids([ for x in LULC])
lulc_cmap = ListedColormap([x.color for x in LULC], name="lulc_cmap")
lulc_norm = BoundaryNorm(lulc_bounds, lulc_cmap.N)

The main point of this task is to create a raster mask from the vector polygons and add it to the eopatch. With this procedure, any kind of a labeled shapefile can be transformed into a raster reference map. This result is achieved with the existing task VectorToRaster from the eolearn.geometry package. All polygons belonging to the each of the classes are separately burned to the raster mask.

Land use data are public in Slovenia, you can use the provided partial dataset for this example, or download the full dataset (if you want to upscale the project) from our bucket. The datasets have already been pre-processed for the purposes of the example.

land_use_ref_path = os.path.join(DATA_FOLDER, 'land_use_10class_reference_slovenia_partial.gpkg')
vector_feature = FeatureType.VECTOR_TIMELESS, 'LULC_REFERENCE'

vector_import_task = VectorImportTask(vector_feature, land_use_ref_path)

rasterization_task = VectorToRasterTask(
    (FeatureType.MASK_TIMELESS, 'LULC'),
    raster_shape=(FeatureType.MASK, 'IS_DATA'),

Define the workflow

All the tasks that were defined so far create and fill the EOPatches. The tasks need to be put in some order and executed one by one. This can be achieved by manually executing the tasks, or more conveniently, defining an EOWorkflow which does this for you.

The following workflow is created and executed:

  1. Create EOPatches with band and cloud data

  2. Calculate and add NDVI, NDWI, NORM

  3. Add mask of valid pixels

  4. Add scalar feature representing the count of valid pixels

  5. Save eopatches

An EOWorkflow can be linear or more complex, but it should be acyclic. Here we will use the linear case of the EOWorkflow, available as LinearWorkflow

Define the workflow

# Define the workflow
workflow = LinearWorkflow(

# Let's visualize it