Available for Collaboration

Seyed Amir Muhammad
Qureshi

Geospatial Technologies | GeoAI | Earth Science

About Me

I develop AI methodologies for Earth observation, creating new approaches to satellite imagery synthesis using generative models and conditional generation techniques.

Research Focus

Generative AI for Earth Observation

Developing diffusion models for synthetic satellite imagery generation with the United Nations. Specialized in conditional image synthesis and automated geospatial workflows.

Technical Expertise

AI & Geospatial Stack

Deep Learning: TensorFlow, PyTorch

Generative Models: Diffusion, LoRA, ControlNet

Remote Sensing: Google Earth Engine, SNAP

GIS Development: Full-stack spatial applications

Achievements

Academic & Research

Erasmus Mundus Scholar (3 countries)

Top Graduate, University of Zanjan

2 Publications (first & co-author)

5 Technical Teaching Positions

Currently completing my Erasmus Mundus Master's across Spain, Germany, and Portugal, while collaborating with the United Nations on some pretty cool thesis research involving diffusion models for synthetic satellite imagery. My work explores conditional generation methods and building scalable workflows for processing thousands of satellite images for AI training.

I've published research in Environmental Modelling & Software and Groundwater for Sustainable Development, and built AquMADE, a GIS-based web application that helps with groundwater quality assessment for environmental decision-making. I've also been a teaching assistant in digital image processing, geodesy, and photogrammetry.

My approach? Bringing together cutting-edge generative AI with real-world geospatial applications. From generative models for Earth observation to automated satellite analysis workflows, I'm all about developing methodologies that make sophisticated AI actually useful for tackling environmental challenges.

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