Deep Learning / Case study
Satellite Land Classification
CNN and Vision Transformer workflows for classifying satellite terrain in precision-agriculture scenarios.
View repositoryProblem
Classify crops, forests, and water bodies from satellite imagery to support agricultural land-use decisions.
Solution
Built parallel Keras and PyTorch workflows covering image loading, augmentation, CNN development, Vision Transformer fine-tuning, and comparative evaluation.
Architecture
Geospatial images → framework-specific preprocessing and augmentation → CNN / ViT training paths → metric comparison.
Technical implementation
IBM AI Engineering capstone organized into four modules. Models are evaluated using accuracy, precision, recall, F1-score, and AU-ROC.
Key challenges
Compare convolutional and transformer approaches consistently across two deep-learning frameworks.
Lessons learned
The repository documents data-loading trade-offs, framework differences, transfer learning, and comparative model evaluation.