I am

Mirza Waleed

I build AI-native geospatial systems for
Flood intelligence, climate adaptation, geospatial machine learning, and earth observation operations.

Ph.D. Candidate at Hong Kong Baptist University and Google Developer Expert for Earth Engine. I help geospatial teams turn raw data into reproducible AI-assisted workflows that deliver decision-ready insights faster.

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01.

About Me

I'm Mirza Waleed, a Ph.D. Candidate in Computational Geography at Hong Kong Baptist University. My work focuses on AI Productivity for geospatial science: reducing cycle time from raw data to actionable insight without sacrificing rigor.

As a Google Developer Expert for Earth Engine (one of approximately 50 worldwide), I combine cloud geospatial computing with ensemble ML and deep learning, scaling from exploratory notebooks to production-grade research pipelines.

My credibility comes from delivered outcomes: global flood susceptibility products, national land-cover intelligence, and near real-time disaster response frameworks. I translate these into reusable AI-assisted workflows that help institutions ship better science faster.

Mirza Waleed
02.

Experience

Hong Kong Baptist University logo

Ph.D. Fellow

Hong Kong Baptist University
Sep 2022 - Present Hong Kong
  • Conducting doctoral research on scalable flood susceptibility modeling using ensemble machine learning and cloud-based geospatial computing.
  • Developed the Global Flood Susceptibility Map (GFSM v1), a 30-meter resolution dataset recognized as a Web of Science Top 0.1% Highly Cited (Hot) Paper.
  • Published multiple first-author papers in peer-reviewed journals including the International Journal of Disaster Risk Reduction and Environmental Impact Assessment Review.
  • Serving as Teaching Assistant for undergraduate courses in environmental hazards and health geography.
Current Position
Google logo

Google Developer Expert

Google
2023 - Present Global
  • Recognized as one of approximately 50 Google Developer Experts worldwide for Google Earth Engine, based on contributions to the geospatial research and developer community.
  • Delivered workshops and technical talks on cloud-based geospatial analysis, including tutorials on flood mapping, land surface temperature retrieval, and land cover classification using Earth Engine.
  • Mentored researchers and developers on adopting Google Earth Engine for environmental monitoring and disaster risk assessment through open-source projects and educational content.
Current Position
KAUST logo

Visiting Scholar

KAUST
2024 - 2025 Saudi Arabia
  • Developed a self-supervised deep learning pipeline for high-resolution flood mapping, trained on the Shaheen III supercomputer — one of the top 10 most powerful systems globally.
  • Investigated model-data fusion techniques, explainability, and uncertainty quantification to improve flood model transferability across diverse hydro-climatic regions.
  • Collaborated with the Urban Analytics Lab on research integrating remote sensing, computational geography, and environmental risk assessment.
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Freelance GIS Consultant

Fiverr
2019 - 2025 Remote
  • Completed over 40 geospatial consulting projects with a consistent 5/5 client rating, covering remote sensing analysis, Earth Engine development, and spatial data processing.
  • Delivered end-to-end solutions including satellite image classification, flood risk mapping, land cover change detection, and custom GEE application development for international clients.
  • Translated complex geospatial analysis requirements into clear, reproducible workflows tailored to each client's domain and data constraints.
03.

Publications

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Peer-reviewed research published in journals including the International Journal of Disaster Risk Reduction, Environmental Impact Assessment Review, and Science of The Total Environment.

Journal cover for SSRN
Featured
Preprint 2026

BAM: An Automated, Self-Supervised Machine Learning Framework for Near-Real-Time Wildfire Burned Area Mapping using Multi-Source Earth Observation

Mirza Waleed, Muhammad Bilal

SSRN

GitHub PDF DOI
Journal cover for International Journal of Disaster Risk Reduction
Featured
Journal Article 2025

High-resolution flood susceptibility mapping and exposure assessment in Pakistan: An integrated artificial intelligence, machine learning and geospatial framework

Mirza Waleed, Muhammad Sajjad

International Journal of Disaster Risk Reduction

GitHub PDF DOI
Journal cover for Urban Ecosystems
Journal Article 2026

Increased urban thermal discomfort in major Cities of the Arabian Peninsula

Naushin Yasmin, Mirza Waleed, Safi Ullah, Sami G. Al-Ghamdi

Urban Ecosystems

PDF DOI
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04.

Selected Projects

Selected systems and open-source tools that operationalize AI Productivity for geospatial analysis: from flood risk modeling to land cover intelligence and urban climate workflows.

BAM: Self-Supervised Burn Area Mapping

BAM framework for wildfire burned area mapping visualization

BAM is a revolutionary, self-supervised machine learning framework designed for near-real-time mapping of wildfire burned areas globally. It bridges physics-based spectral analysis with data-driven ML refinement to eliminate reliance on manual training data, achieving robust 30-meter resolution mapping that resolves sub-pixel heterogeneity across diverse ecosystems and topographies.

GitHub Paper PDF Live
05.

Posts

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Mirza Waleed receiving the 2025 Spatial Impact Award for excellence in GeoAI
awards
Dec 1, 2025 2 min

Spatial Impact Award 2025

By Atlas.co, the Spatial Impact Award recognizes outstanding projects that leverage geospatial technology to address real-world challenges. In 2025, the award highlights innovative applications in environmental monitoring, disaster management, and sustainable development.

GEE Python Remote Sensing
Top GIS Voices 2024 recognition badge for Mirza Waleed on LinkedIn
awards
Dec 1, 2024 2 min

Top GIS Voices 2024

Selected by Atlas.co as one of the 50 most influential voices in the GIS community. This award recognizes individuals who are actively sharing knowledge, fostering innovation, and driving the geospatial industry forward through community engagement and technical expertise.

GEE Python GeoAI
07. Let's Collaborate

Contact

I am open to collaborations, consulting, and speaking around AI Productivity for geospatial teams, including workflow automation, cloud analytics, and research-to-deployment handoff.

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Email: [email protected]