Developed an advanced satellite imagery analysis system that combines traditional algorithms with deep learning techniques to interpret large-scale urban environments from Landsat data.
🚀 Core Contributions
🧠 Advanced Geospatial ML Pipeline
Multi-band Image Processing: Created specialized techniques for importing and preprocessing Landsat's 9-band multi-spectral imagery
Image Stitching Engine: Developed algorithms to seamlessly combine multiple satellite images into comprehensive urban maps
Mask R-CNN Implementation: Designed and trained custom deep neural networks based on Mask R-CNN architecture for multi-band segmentation
Automated Geo-processing: Built end-to-end data pipeline for efficient processing of large-scale satellite datasets
🌎 Environmental Analysis System
Vegetation Assessment: Implemented NDVI (Normalized Difference Vegetation Index) calculations to quantify vegetation health
Land Cover Classification: Created comprehensive classification system for urban environmental features
Spatial Pattern Recognition: Designed algorithms to identify and analyze urban development patterns
Precision Calibration: Implemented metadata-based calibration techniques for enhanced accuracy
🔍 Computer Vision Integration
Keypoint Matching: Applied SURF algorithm for precise alignment and transformation of satellite imagery
Sliding Window Analysis: Implemented efficient techniques for processing high-resolution imagery with limited resources
Hybrid Algorithm Development: Combined Markov Tree and Decision Tree models with deep learning approaches
Custom Feature Extraction: Created specialized techniques for identifying urban features from multi-spectral data