Spatial & Temporal Change Detection

Detect Change. Monitor Growth. Act with Precision.

An AI-driven satellite image analysis system that identifies structural and environmental changes with pixel-level accuracy across large geographic regions.

Pixel-Level

Change Segmentation

Binary change masks output

2+ GB

Annotated Training Dataset

LEVIR-CD supervised learning

Large-Scale

Geographic Coverage

Urban, forest, infrastructure monitoring

SAB's Change Detection System

We built a deep learning-powered satellite change detection system using a Siamese CNN architecture. The solution automatically learns feature differences between bi-temporal satellite images to generate precise pixel-level change maps, enabling reliable and scalable landscape monitoring.

Challenge:

Difficulty in detecting small structural or building-level changes.
High visual similarity between unchanged regions in T1 and T2 images.
Illumination, seasonal, and environmental variations in satellite imagery.
Large high-resolution images leading to high computational cost and long training time.
Requirement of precise pixel-level segmentation instead of simple image classification.
Need for scalable monitoring across large geographic areas.

Solution:

Adopted a deep learning-based Siamese CNN architecture for feature comparison.
Utilized LEVIR-CD large-scale annotated satellite dataset for supervised training.
Learned deep feature representations from before (T1) and after (T2) images.
Applied Binary Cross-Entropy and Dice loss for accurate change segmentation.
Generated binary pixel-level change masks for reliable landscape monitoring.
Enabled a scalable AI pipeline suitable for urban development, deforestation and disaster assessment.

Let's Monitor a Changing World with Intelligent AI

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AI-Based Satellite Image Change Detection using Siamese CNN