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Topics covered:
Data normalization and feature extraction
Applying K-Means Clustering
Random Forest Classifier
Creating a CNN with Keras
ARIMA model for time series forecasting
Anomaly detection with Isolation Forest
Geospatial data manipulation with GeoPandas and Folium
Geospatial clustering with K-Means
Spatial linking with GeoPandas
Kriging interpolation
Visualization of geospatial time series data
Visualization of digital elevation models (DEM)
Edge detection on satellite images
Terrain slope calculation
Calculating the terrain orientation
LSTM model for time series prediction
What you will learn:
How to normalize and extract features from geospatial data
Applying K-Means Clustering for Spatial Analysis
Training and evaluating a random forest classifier
Creating and training convolutional neural networks (CNNs) with Keras
Time series forecasting with ARIMA models
Detecting anomalies with Isolation Forest
Editing and visualizing geodata with GeoPandas and Folium
Performing spatial linkages and geospatial clustering
Interpolating spatial data with Kriging
Visualization of geospatial time series data
Edge detection on satellite images
Calculating terrain slope and aspect from DEM data
Predicting time series data with LSTM models
Required libraries:
numpy
Panda
scikit-learning
matplotlib
keras
Statistical models
Geo Pandas
Sheet
Subscribe to
grid
scipy
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The code and data for this tutorial are available in my GitHub repository at: https://github.com/Azad77/ML_Geospatial_Analysis.
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