Geospatial Data Scientist | Machine Learning Engineer (MLE) | Remote Sensing Enthusiast I bridge the gap between high-fidelity Remote Sensing science and enterprise-grade software engineering. With a career trajectory defined by delivering high-impact spatial intelligence for global stakeholders like the World Bank aided project and ISRO, I specialize in the orchestration of GeoAI and the deployment of cloud-native spatial architectures. My technical philosophy is rooted in strict Object-Oriented Programming (OOP) and SOLID principles, utilizing Python (GeoPandas, ArcPy, Rasterio) and R (R6 class system) to build modular, production-ready pipelines. I have extensive experience handling multi-modal data streams ranging from SAR and LiDAR to Passive Microwave and Thermal imagery transforming raw pixels into predictive risk models and automated computer vision solutions. I focus on the full lifecycle of spatial data engineering: from designing automated ETL workflows that reduce processing to implementing robust spatial data governance within PostgreSQL/PostGIS environments. On this platform, I share architectural patterns and MLOps strategies for scaling Earth Observation from isolated research scripts to robust, automated spatial intelligence systems.
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