Let us Look at Change Detection and Machine Learning.

Author(s): Stephen Chege-Tierra Insights

Originally published on Towards AI.

Let me ask you a question: have you ever visited your old childhood neighborhood and been stunned by the changes it has undergone, it looks unrecognizable. Probably while you were growing up it was an old abandoned street with few buildings and malls, but now it has become a commercial hub buzzing with activities.

This is the case for me; every time I visit my childhood home I am shocked at how it has morphed into a small business district flooded with apartment after apartment, contrary to my upbringing in the’90’s and early 2000s when it was just a simple estate.

Finding insights that propel advancement is more important than simply noting disparities in a world that is constantly changing. Let us delve into machine learning-powered change detection, where innovative algorithms and spatial analysis combine to completely revolutionize how we see and react to our ever-changing surroundings.

This potent combination creates a new learning horizon, delivering unparalleled accuracy and predictive capabilities, from tracking deforestation and urban growth to monitoring infrastructure development and climate change.

Technology has made it possible to detect change virtually anywhere in the world and assess the change to determine which factors are causing it. This is very fascinating especially if you are into urban planning and environmental monitoring.

What is Change Detection For GIS

According to Esri, A process that measures how the attributes of a particular area have changed between two or more periods. Change detection often involves comparing aerial photographs or satellite imagery of the area taken at different times. The process is most frequently associated with environmental monitoring, natural resource management, or measuring urban development.

Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a multifaceted endeavour due to numerous factors, including disparities in image value, noise, registration errors, illumination changes, complex landscapes, and spatial heterogeneity.

Change detection methods in remote sensing and GIS are based on finding discrepancies in two satellite images before and after a certain event. Change detection algorithms for GIS compare the spatial representation of two points in time and measure differences in the variables of interest.

Geospatial and statistical data are analyzed in GIS change detection. Numerous sources can provide statistical data, and satellites, UAVs, and other remote sensing equipment can be used to retrieve geographic data. Thanks to open data availability, satellite change detection is becoming more and more popular these days and is frequently the fastest and least expensive alternative.

Why Using Change detection ML is important for Spatial Analysis.

Enhanced Accuracy: Large volumes of data may be processed by machine learning algorithms, which can also spot minute changes that conventional techniques might overlook. Applications like urban planning, disaster management, and environmental monitoring depend on this accuracy.

Automated Processing: The analysis of sensor data, satellite photos, and other geographical data sources can be done automatically using ML models. As a result, manual analysis takes less time and effort, enabling real-time monitoring and speedier reaction to changes.

Scalability: Large datasets may be handled by ML systems with ease, allowing for detailed monitoring of vast geographic areas. Global projects like monitoring climate change and protecting biodiversity depend on this scalability.

Predictive Capabilities: Machine learning models that have been trained on historical data can predict future inclinations and developments, helping urban researchers and environmentalists. With preemptive preparation and resource distribution, this foresight assistances to minimize the effects of unfavorable changes and maximize favorable developments for earth observation.

Improved Decision-Making: In the modern world, data driven decision-making is essential. The insights gleaned by ML-enhanced change detection offer a strong basis for well-informed decision-making. Planning urban growth, handling emergencies, and managing natural resources all depend on timely and accurate data.

Cost-effectiveness: Utilizing machine learning to automate change detection eliminates the need for labor-intensive field surveys and human labour. Organizations can concentrate on strategic goals and distribute resources more efficiently because of this cost efficiency.

Getting Started with Software Platform ideal for machine learning and Change detection

Google engine- A cloud-based platform for the analysis of environmental data at the planetary scale. GEE offers strong tools for handling and examining big geographic datasets and change detection for machine learning.

– GEE Code Editor: An online, easy-to-use, open-source IDE ideal for writing and executing JavaScript and Python code directly within the GEE environment, with well-detailed documentation for learners who want to try different algorithms

Python and R Integration: Develop bespoke machine learning (ML) models and perform sophisticated analytics using Python and R for change detection. The data science community uses both languages extensively because of their robust libraries and ecosystems, they are both open-source.

– Jupyter Notebooks: For Python-based analysis, make use of Jupyter notebooks, which provide interactive data exploration and visualization.

– RStudio: An integrated R programming environment with coding, debugging, and visualization capabilities.

My take is to use the Google Earth engine and other earth observation platforms to analyze high-quality images that can be analyzed, especially the ones that have been uploaded recently as their images are up to date with the current situation at the ground.

1. Install the necessary libraries:

pip install earthengine-api
pip install geemap

2. Authenticate and Initialize the Earth Engine API:

import ee
import geemap

# Authenticate and initialize the Earth Engine API
ee.Authenticate()
ee.Initialize()

3. Define the Area of Interest and Time Period:

# Define the area of interest (AOI)
aoi = ee.Geometry.Rectangle([73.00, 18.00, 74.00, 19.00])

# Define the time period
start_date = '2020-01-01'
end_date = '2020-12-31'

4. Load and Preprocess the Satellite Data:

# Load Sentinel-2 imagery
collection = ee.ImageCollection('COPERNICUS/S2') \
 .filterBounds(aoi) \
 .filterDate(start_date, end_date) \
 .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20)) \
 .select(['B4', 'B3', 'B2', 'B8']) # Select relevant bands (Red, Green, Blue, NIR)

# Compute median composite
image = collection.median().clip(aoi)

# Add NDVI (Normalized Difference Vegetation Index) as a band
ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI')
image = image.addBands(ndvi)

5.Define Training Data for the Classifier:

# Define training points (latitude, longitude, class)
training_points = ee.FeatureCollection([
 ee.Feature(ee.Geometry.Point([73.5, 18.5]), {'landcover': 0}), # Class 0 (e.g., water)
 ee.Feature(ee.Geometry.Point([73.5, 18.8]), {'landcover': 1}), # Class 1 (e.g., vegetation)
 # Add more training points as needed
])

# Sample the image at the training points
training_data = image.sampleRegions(
 collection=training_points,
 properties=['landcover'],
 scale=10
)

6. Train a Decision Tree Classifier:

# Train a CART (Classification and Regression Trees) classifier
classifier = ee.Classifier.smileCart().train(
 features=training_data,
 classProperty='landcover',
 inputProperties=['B4', 'B3', 'B2', 'NDVI']
)

7.Classify the Image and Detect Changes:

# Classify the image
classified_image = image.classify(classifier)

# Visualize the classification result
map = geemap.Map(center=[18.5, 73.5], zoom=10)
map.addLayer(classified_image, {'min': 0, 'max': 1, 'palette': ['blue', 'green']}, 'Land Cover Classification')
map.addLayerControl()
map

8. Export the Results:

# Export the classified image to Google Drive
export_task = ee.batch.Export.image.toDrive(
 image=classified_image,
 description='LandCoverClassification',
 folder='EarthEngineExports',
 scale=10,
 region=aoi.getInfo()['coordinates']
)
export_task.start()

Conclusion

When machine learning and change detection are combined with spatial analysis, there are unmatched possibilities for comprehending and controlling dynamic settings. To do this, Google Earth Engine (GEE) combined with R and Python offers a stable, expandable, and adaptable platform. Large geographic datasets are processed effectively by GEE’s cloud-based processing capabilities, and the addition of Python and R allows for the use of cutting-edge machine learning algorithms that produce extremely accurate change detection and perceptive analysis.

Automated processes and real-time data processing are made possible by this synergy and are essential for prompt catastrophe management, urban planning, and environmental conservation actions. Models and workflows may be customized thanks to the flexibility and extensibility of R and Python, which makes them an affordable option for a variety of applications.

Next, I will write about best algorithm for change detection and object detection.

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Published via Towards AI