Labelling information is a crucial step throughout the course of for creating supervised Machine Finding out (ML) fashions. It contains the essential job of assigning associated labels or lessons to completely totally different choices all through the information, corresponding to land cowl class (e.g. vegetation, water our our bodies, metropolis area, and so forth.) or totally different bodily traits of the Earth’s ground. These labels could also be multi-class (e.g., forest, grassland, metropolis), or binary (e.g., water or non-water).
This publish presents Client State of affairs 2 of the AI/ML Enhancement Problem, titled “Alice labels Earth Assertion (EO) information”. It demonstrates how the enhancements being deployed throughout the Geohazards Exploitation Platform (GEP) and Metropolis Thematic Exploitation Platform (U-TEP) will assist prospects labelling EO information.
For this Client State of affairs, an interactive Jupyter Pocket e book is used to data an ML practitioner, corresponding to Alice, by the use of the following steps:
- create information labels, using QGIS Software program program or a Solara / Leafmap software program
- load Labels and Sentinel-2 information using STAC API
- sample Sentinel-2 information with Labels and create a dataframe
- validate the labelled information in direction of the Worldwide Flooring Water (GSW) dataset
- use the dataframe to educate a ML model based on a Random Forest classifier
- perform raster inference on a Sentinel-2 scene to generate a binary water masks
Wise examples and directions are exhibited to point out how this new capabilities may be utilized from a Jupyter Pocket e book.
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The tactic for creating vector (degree or polygon) information layers is illustrated with two examples:
- QGIS Software program program: a loyal profile on the App Hub is configured to the buyer for using QGIS Software program program (additional particulars could also be found on the App Hub online User Manual). The steps to create new Shapefile Layers, add classification kinds for each degree / polygon, and save the output in a geojson format are illustrated with various screenshots.
- Solara / Leafmap software program: an interactive map, constructed on
SolaraandLeafmap, has been built-in throughout the Pocket e book to current the selection to the buyer to manually create and save labels correct from the Pocket e book itself.
After the annotations are created, each from QGIS or from the Solara / Leafmap interactive map, and saved proper right into a .geojson file, the buyer can create the STAC Merchandise of the EO labels, and publish it on the STAC endpoint. That’s executed with the pystac Python library and an interactive variety correct throughout the Pocket e book.
Entry to Labels and EO information was facilitated by the use of the utilisation of the libraries pystac and pystac_client. These libraries enable prospects to work along with a STAC catalog by defining specific query parameters, corresponding to time fluctuate, area of curiosity, and information assortment preferences. Subsequently, solely the STAC Objects that align with the provided requirements are retrieved for the buyer.
Beneath is given a simplified code snippet for implementing STAC information search and for displaying outcomes on an interactive map. An upcoming article, dedicated to the STAC format and information entry may be printed, with additional steering and examples.
# Import libraries
import pystac; from pystac_client import Client
# Entry to STAC Catalog
cat = Client.open("https://ai-extensions-stac.terradue.com", ...)# Define query parameters
start_date = “2023-06-01”
end_date = “2023-06-30”
bbox = [-121.857043 37.853934 -120.608968 38.840424]
cloud_cover = 30
tile = “10SFH”# Search Labels by AOI, start/end date
query_sel = cat.search(
collections=[“ai-extensions-svv-dataset-labels”],
datetime=(start_date, end_date),
bbox=bbox,
)labels = query_sel.item_collection()# Search EO information (Sentinel-2) by AOI, start/end date, cloud cowl and tile amount
query_sel = cat.search(
collections=[“sentinel-2-l2a”],
datetime=(start_date, end_date),
bbox=bbox,
query={"eo:cloud_cover": {"lt": cloud_cover}},
)eo_item = [item for item in query_sel.item_collection() if tile in item.id][0]
As quickly because the Label information is loaded, it is reworked proper right into a dataframe (gdf) using geopandas library. The Python library folium was then used to point out every the Labels and EO information on an interactive map.
import folium; from folium import GeoJson, LayerControl, plugins
map = folium.Map(location=[x, y], tiles="OpenStreetMap", zoom_start=9)# Add Labels to map
map = addPoints2Map(gdf, map)# Add footprint of EO scene
footprint_eo = folium.GeoJson(eo_item.geometry,style_function=lambda x: {...})
footprint_eo.add_to(map)# Visualise map
map
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After loading the data, the Pocket e book continues with the implementation of a function to iteratively sample the EO information in correspondence of each labelled degree. Together with sampling a group of the Sentinel-2 reflectance band (coastal, purple, inexperienced, blue, nir, nir08, nir09, swir16, and swir22), three vegetation indices are moreover calculated (ndvi, ndwi1, and ndwi2). After sampling the EO bands and calculating the vegetation indices, all the data is concatenated proper right into a pandas DataFrame.
import pandas as pd
tmp_gdfs = []
for i, label_item in enumerate(eo_items):
sampled_data = sample_data(label_item=label_item, common_bands=["coastal", "red", "green", "blue", "nir", "nir08", "nir09", "swir16", "swir22"])
tmp_gdfs.append(sampled_data)# Create pandas dataframe
gdf_points = pd.concat(tmp_gdfs)# Save to file
gdf_points.to_pickle(“filename.pkl”)
A comparability in direction of one different, neutral, dataset was carried out to level out a validation methodology of the labelled information. As a validation dataset, we used the Worldwide Flooring Water (GSW) dataset, generated by JRC (Citation: Pekel, Jean-François; Cottam, Andrew; Gorelick, Noel; Belward, Alan (2017): Worldwide Flooring Water Explorer dataset. European Charge, Joint Evaluation Centre (JRC), http://data.europa.eu/89h/jrc-gswe-global-surface-water-explorer-v1).
The comparability was carried out simply by iterating by the use of the generated labels dataframe and by counting the number of elements labelled as “water” which were appropriately categorized as water (i.e. with pixel value larger than 80%) moreover throughout the GSW dataset.
The dataframe was prepared for the supervised ML job by altering it proper right into a binary classification dataset (i.e. “water” and “no-water”) and by eradicating pointless columns. Further and further detailed analysis on the dataframe could also be carried out by the use of Exploratory Information Analysis (EDA). Check out additional data on the currently printed article dedicated to EDA, for additional particulars and steering on this.
The dataset was then break up into put together and verify with the devoted function train_test_split() from the sklearn package deal deal.
from sklearn.model_selection import train_test_split
# columns used as choices all through teaching
feature_cols = ['coastal','red','green','blue','nir','nir08','nir09','swir16','swir22', 'ndvi', 'ndwi1', 'ndwi2']# column determine for label
LABEL_NAME = 'CLASSIFICATION'choices = train_dataset[feature_cols] # cols for choices
label = train_dataset[LABEL_NAME] # col for labels
X_train, X_test, y_train, y_test = train_test_split(
choices, label,
random_state=42,
train_size=0.85,
)
The ML model developed on this Pocket e book was a Random Forest classifier using k-fold cross validation. Random Forest is a robust and versatile supervised ML algorithm that grows and combines various decision timber to create a “forest.” It could be used for every classification and regression points. Okay-Fold Cross-Validation is a technique utilized in ML to guage the effectivity and generalisation potential of a model. The steps involved throughout the Okay-Fold Cross-Validation are:
- break up the dataset into Okay subsets, or “folds”.
- The model is then expert Okay events, each time using Okay-1 folds for teaching, and the remaining fold for validation.
- This course of is repeated Okay events, with each of the Okay folds used exactly as quickly as as a result of the validation information.
- The Okay outcomes from the Okay folds are then averaged to produce a single estimation of model effectivity.
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The ML parameters are outlined and used to educate the model with various straightforward capabilities, provided these are outlined.
hyperparameters = {
'n_estimators': 200,
'criterion':'gini',
'max_depth':None,
'min_samples_split':2,
'min_samples_leaf':1,
'min_weight_fraction_leaf':0.0,
'max_features':'sqrt',
'max_leaf_nodes':None,
'min_impurity_decrease':0.0,
'bootstrap':True,
'oob_score':False,
'n_jobs':-1,
'random_state':42,
'verbose':0,
'warm_start':True,
'class_weight':None,
'ccp_alpha':0.0,
'max_samples':None
}
# define model obj which is printed in utils.py
model = Model(hyperparameters)# teaching model using k-fold cross validation
estimators = model.teaching(X=X_train,Y=y_train,folds=5)
The model is evaluated on unseen information with the following evaluation metrics:
- Accuracy: calculated as a result of the ratio of appropriately predicted conditions to the total number of conditions throughout the dataset
- Recall: additionally known as sensitivity or true constructive cost, recall is a metric that evaluates the pliability of a classification model to appropriately decide all associated conditions from a dataset
- Precision: it evaluates the accuracy of the constructive predictions made by a classification model
- F1-score: it is a metric that mixes precision and recall proper right into a single value. It is notably useful when there’s an uneven class distribution (imbalanced programs) and gives a stability between precision and recall
- Confusion Matrix: it gives an in depth breakdown of the model’s effectivity, highlighting conditions of proper and incorrect predictions.
The code snippet beneath displays how the model could also be evaluated, adopted by the output of the evaluation metrics calculated in the midst of the course of.
# think about model
best_model = model.evaluation(estimators,X_test, y_test)
Totally different strategies to evaluate the ML model are the distribution of the possibility of predicted values, the Receiver Working Attribute (ROC) Curve, and the analysis of the permutation choices significance. All three could also be derived and plotted from all through the Pocket e book with one straightforward line of code.
# Distribution of likelihood of predicted values
ml_helper.distribution_of_predicted_val(best_model, X_train, X_test)
# ROC Curve
ml_helper.roc(best_model,X_test,y_test)# Permutation Significance
ml_helper.p_importance(best_model,X_test,y_test,hyperparameters,MODEL_OUTPUT_DIR)
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Lastly, the simplest ML model could also be saved to a file so that it might be loaded and used ultimately. The one prerequisite for making use of the ML model is for the enter dataset to have the an identical format as a result of the teaching dataset described above.
import joblib
# Save the model to file
model_fname = 'best_rf_model.joblib'
joblib.dump(best_model, model_fname)
Now the buyer can apply the ML model on a Sentinel-2 image to generate a binary water masks output. After loading the EO information and the ML model into the Pocket e book, the ML model is utilized to make predictions over the entire enter EO information. The steps to hold out these operations are confirmed throughout the simplified code snippet beneath.
# Select EO property from the loaded Sentinel-2 scene (eo_item)
fileList = {}
for f in eo_item.get_assets():
if (f in feature_cols) or f == 'scl':
fileList[f] = eo_item.get_assets()[f].href# Load the ML model classifier
model = joblib.load(model_fname)# Make predictions
predictions = ml_helper.readRastersToArray(model, fileList, feature_cols)# Save predictions
df_predict = pd.DataFrame(predictions.ravel(),columns=['predictions'])
df_predict.to_pickle('prediction.pkl')# Create binary masks
predictions = df_predict['predictions']
predictions = predictions.to_numpy().reshape((10980,10980))# Apply sieve operation to remove small choices (in pixels)
my_array_uint8 = predictions.astype(rasterio.uint8)
sieved = sieve(my_array_uint8, threshold=1000, connectivity=8)# Use Scene Classification band to filter out clouds and harmful information
with rasterio.open(fileList['scl']) as scl_src:
scl = scl_src.study(1)
scl = np.the place(~np.isin(scl, [4, 5, 6, 7, 11]), np.nan, scl)
mask_out = np.the place(~np.isnan(scl), sieved, np.nan)# Use Scene Classification band to filter out clouds and harmful information
import matplotlib.pyplot as plt
plt.imshow(mask_out,interpolation='none'); plt.title("Improved end result")
Throughout the decide above, water our our bodies are plotted in yellow and non-water pixels are plotted in darkish blue, and clouds are masked out in white (top-right nook of the image).
This work demonstrates the model new functionalities launched by the AI/ML Enhancement Problem to help a ML practitioner:
- create EO information labels, using QGIS Software program program or a Solara / Leafmap software program
- load Labels and EO information with STAC API
- sample EO information with Labels and create a dataframe
- use the dataframe to educate a Random Forest classifier
- perform raster inference on a specific Sentinel-2 scene to generate a binary water masks.
Useful hyperlinks:
Web page: https://www.terradue.com/portal/
Give attention to dialogue board: https://discuss.terradue.com/t/announcing-the-launch-of-the-ai-ml-enhancement-project-for-gep-and-urban-tep-exploitation-platforms/1188/3
– Parham Membari
– Pedro Pereira goncalves
– Fabrice Brito
– Simone Vaccari
