Integrating Machine Learning Into Gis For Advanced Analytics

Overview of Machine Learning for GIS

Machine learning (ML) refers to the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task. The machine learning models predict by identifying underlying relationships and patterns in large volumes of data.

Some of the most common machine learning algorithms that can be integrated with spatial data analysis in geographic information systems (GIS) include:

  • Random forests – Ensemble learning method that operate by constructing a multitude of decision trees during training
  • Support vector machines (SVM) – Supervised learning models used for classification and regression analysis
  • K-nearest neighbors (KNN) – Non-parametric, lazy learning algorithm used for classification and regression
  • Neural networks – Algorithms modeled loosely after the human brain used for recognizing underlying relationships in data
  • Naive Bayes classification – Set of probabilistic classifiers relying on Bayes’ theorem that assumes strong independence between features

Integrating these machine learning capabilities into GIS workflows provides many benefits for spatial data analysis such as:

  • Revealing deep insights and hidden patterns within geospatial datasets
  • Automating time and labor-intensive manual processes in GIS
  • Improving predictive modeling and future forecasting from geographic data
  • Classifying multi-band satellite or aerial imagery more accurately
  • Assigning geographic features into meaningful categories for analysis

With large volumes of geospatial data being generated continuously, machine learning enables GIS analysts to take spatial analysis to the next level. The next sections will provide practical guidance on applying machine learning to common GIS workflows.

Preprocessing Geospatial Data for ML Models

Real-world geospatial data from sensors and field observations requires preprocessing before further analysis using machine learning algorithms. The three key aspects of preprocessing include:

  • Managing projection systems and coordinate reference systems – Spatial data layers compiled from various sources may have different coordinate reference systems that need alignment to a common projection system before analysis. This ensures geographic alignment.
  • Cleaning and formatting attribute data – Tabular data linked to spatial features needs processing to handle missing values, duplicate entries, formatting inconsistencies or errors that could negatively impact ML model training.
  • Sampling strategies for imbalanced classes – Many geospatial classification tasks lead to imbalanced training data with far more examples for common classes than rare ones. Special sampling techniques like undersampling, oversampling or SMOTE synthesis must be applied to balance training data.

Python’s GeoPandas library provides a range of convenient utilities for handling these preprocessing steps before further ML integration:

  • Coordinate reference system alignment using to_crs()
  • Attribute data cleaning with Pandas utilities such as dropna() and fillna()
  • Spatial data balancing by integration with imbalanced-learn package

Taking care to appropriately preprocess geospatial data ensures higher quality model training and better generalizability.

Training ML Models on Geospatial Data

Supervised machine learning involves training statistical models to make predictions based on labeled examples. For geospatial analysis tasks like land cover mapping, analysts can leverage random forest algorithms to categorize satellite image pixels using spectral signature data as features.

Here is a Python code snippet demonstrating how to train a random forest classifier on hyperspectral satellite image bands to categorize areas into land cover classes like water, vegetation, urban built-up etc.:

# Import packages 
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

# Prepare input and output data
X = sat_image_pixels_df[spectral_bands].values 
y = sat_image_pixels_df['landcover_class'].values

# Split dataset  
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Train model
model = RandomForestClassifier(n_estimators=100), y_train) 

# Evaluate on test data
accuracy = model.score(X_test, y_test)
print("Random forest accuracy:", accuracy)

This trains a random forest model on hyperspectral signatures to predict land cover classes, evaluates classification accuracy, and can be extended to apply predictions across entire satellite images.

Beyond classification, other common geospatial ML tasks include predictive modeling, anomaly detection, object detection in satellite imagery, and more. The trained models can be persisted for integration into GIS workflows.

Applying Trained ML Models in GIS

Once the machine learning models are trained on geospatial datasets, GIS provides a convenient environment to apply these models and visualize the predictions spatially across features and rasters.

For example, here is some sample Python code to take a trained random forest model and generate landcover predictions on a shapefile containing forest inventory polygons, and visualize results in QGIS:

import geopandas 
from sklearn.externals import joblib

# Load forest inventory polygons 
forest_df = geopandas.read_file('forests.shp')

# Load trained model
model = joblib.load(rf_model.pkl)  

# Generate predictions 
forest_df['predictions'] = model.predict(forest_df[features])

# Visualize predictions 
forest_df.plot(column='predictions', legend=True)

The trained model can predict landcover categories like vegetation type for each forest stand. The GeoPandas integration allows spatially visualizing model predictions as a choropleth map within QGIS. This enables further GIS analysis on model outputs.

Similarly, model predictions can be exported as vector layers, rasters, or CSV files to integrate into commercial GIS software packages like ArcGIS, MapInfo, etc. GIS provides a versatile environment for applying ML models spatially.

Advanced Applications of ML in GIS

While geospatial machine learning is often used for common tasks like land cover mapping, more advanced applications leverage deep learning and Big Data analytics to uncover hidden insights. Some examples include:

  • Object detection in aerial/satellite imagery – Deep neural networks can identify and classify various structures, vehicles in high-resolution imagery captured across regions.
  • Predictive modeling of urban growth patterns – ML models can analyze land use change drivers and model future urban expansion risk areas for sustainable planning.
  • Anomaly detection for changed features – Unsupervised learning approaches detect outliers and significant deviations from baseline conditions acrossboth geospatial and temporal dimensions.

Academic research and commercial solutions by geospatial vendors are both advancing ML techniques for spatial data science. GIS provides an intuitive platform to visualize insights and outcomes of advanced geospatial AI in actionable ways for domain experts and decision makers.

Future Outlook for ML in Spatial Analysis

Geospatial machine learning is still an emerging field with much potential and room for growth. Some key areas to watch as future opportunities include:

  • Improving model accuracy and calibration as larger training datasets become available
  • Mitigating unfair bias in training data and modeling pipelines
  • Generalizing solutions across geographic regions
  • Real-time prediction serving for analytics
  • Incorporating 3D data from LiDAR sensors
  • Knowledge graph embeddings for geospatial feature representations

Sustained research across the geospatial and machine learning communities will help address these opportunities and challenges over time. GIS technology and workflows will need to continuously adapt to keep pace with innovations in applying machine learning for spatial data science.

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