Satellite Image Analysis: A Hands-On Workshop
International Workshop on Deep Learning for Satellite Imagery and Earth Observation
About This Course
Vision Transformers for Remote-Sensing Images is a cutting-edge international workshop designed to teach participants how to apply state-of-the-art Vision Transformer architectures to satellite and aerial imagery. With growing applications in climate research, defense, agriculture, and urban planning, transformers are enabling a leap forward in geospatial image analysis.
This hands-on program will introduce the theory of transformers, their adaptation to vision tasks (e.g., ViT, Swin Transformer), and how they outperform traditional CNNs in capturing long-range dependencies and spatial relationships in high-resolution imagery. Participants will work on real datasets (Sentinel, Landsat, DOTA, etc.) using frameworks like PyTorch, Hugging Face, and TIMM.
Aim
To equip participants with the skills to apply Vision Transformers (ViTs) to remote-sensing image analysis, focusing on tasks like land cover classification, object detection, climate pattern recognition, and disaster mapping using advanced deep learning methods.
Workshop Objectives
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Introduce Vision Transformers and their application in satellite image analysis
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Enable hands-on experimentation with publicly available geospatial datasets
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Teach model customization and fine-tuning techniques
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Promote responsible AI usage in environmental and humanitarian applications
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Foster interdisciplinary innovation at the intersection of AI and Earth science
Workshop Structure
Day 1: Transformers vs CNNs in Remote Sensing
Beyond CNNs: Vision Transformers for Scene Classification
🔹 Topics:
- Review of CNN architectures in remote sensing (ResNet, UNet, etc.)
- Introduction to Vision Transformers (ViT): How they work
- Why ViTs are suited for remote-sensing imagery (large context, less inductive bias)
- Comparison: ViT vs CNN in scene classification
🔹 Hands-on/Demo:
- Colab demo using pretrained ViT and CNN for a sample land scene classification task using EuroSAT or BigEarthNet dataset
Day 2: Land-Cover Change Detection Using Transformers
Tracking the Earth: Transformers for Change Detection
🔹 Topics:
- Problem of land-cover change detection (LCCD) and its applications (urbanization, deforestation)
- Architectures adapted for temporal change detection (Siamese ViTs, TimeSFormer)
- Pipeline: Preprocessing → Patch Embedding → Transformer Blocks → Classification head
🔹 Hands-on/Case Study:
- Visual result comparison (before/after images and heatmaps)
Day 3: Fine-Tuning Vision Transformers on Small Labeled Sets
Efficient Learning: Adapting ViTs with Limited Data
🔹 Topics:
- Challenges of training ViTs with small labeled data
- Strategies: Transfer learning, self-supervised learning (DINO, MAE), adapter layers
- Case studies in remote sensing: Agriculture crop mapping, disaster response
🔹 Hands-on:
- Colab demo: Fine-tuning a ViT model on a small custom dataset
Who Should Enrol?
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Geospatial and remote-sensing professionals
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AI/ML engineers and computer vision researchers
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Earth scientists, environmental engineers, and urban planners
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Students and researchers in space science, climate, or deep learning
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Government/NGO professionals working with Earth observation data
Important Dates
Registration Ends
06/02/2025
IST 5 PM
Workshop Dates
06/02/2025 – 06/04/2025
IST 6 PM
Workshop Outcomes
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Understand the fundamentals of Vision Transformers and how they compare to CNNs
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Process and analyze high-resolution satellite imagery using deep learning
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Train and fine-tune ViTs for various geospatial applications
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Build a portfolio project on remote sensing with ViT-based models
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Receive a certificate demonstrating proficiency in AI + remote sensing
Fee Structure
Student Fee
₹1999 | $50
Ph.D. Scholar / Researcher Fee
₹2999 | $60
Academician / Faculty Fee
₹3999 | $70
Industry Professional Fee
₹5999 | $90
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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