I am

Mirza Waleed

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

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01.

About Me

I'm Mirza Waleed, a computational geographer who found early on that satellite pixels could carry the weight of saving lives, long before I had a word like "GeoAI" for it. That instinct became a career: building machine learning systems that turn Earth observation data into flood warnings and decision-ready tools.

My core project, GFSM, is a 30-metre global flood susceptibility map trained on tens of millions of samples — alongside NFSEA (near-real-time flood mapping), BAM (self-supervised burned area mapping), and PI-SwinIR (physics-informed terrain refinement). This work has taken me from Google Earth Engine to Shaheen III supercomputing and earned me recognition as a Google Developer Expert in Earth Engine (2023–2026), with findings published across disaster-risk, remote sensing, and environmental journals.

I hold a PhD in Geography (Remote Sensing & GeoAI) from Hong Kong Baptist University, and I'm still chasing the same question that started it all: how do we make climate risk visible before it becomes a disaster?

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.
Fiverr logo

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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18 peer-reviewed journal articles, including two Web of Science Hot Papers, in venues such as Scientific Reports, the International Journal of Disaster Risk Reduction, the International Journal of Applied Earth Observation and Geoinformation, and the Journal of Flood Risk Management.

Journal cover for Scientific Reports
Journal Article 2026

A Cloud-Based Geo-AI Framework for Automated High-Resolution Flood Mapping with Explainable Machine Learning

Z. Cong, Mirza Waleed, S. Ullah, F. Tahir, S. G. Al-Ghamdi

Scientific Reports

DOI
Journal cover for International Journal of Applied Earth Observation and Geoinformation
Journal Article 2026

BAM: A Physics-Informed Self-Supervised Framework for Near-Real-Time Wildfire Burned Area Mapping from Multi-Source Earth Observation

Mirza Waleed, Muhammad Bilal

International Journal of Applied Earth Observation and Geoinformation

GitHub PDF DOI
Journal cover for International Journal of Disaster Risk Reduction
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
View All Publications
04.

Selected Projects

Selected GeoAI systems and open-source tools for flood risk, land-cover intelligence, and urban climate analysis — including GFSM v1, a global flood susceptibility dataset, and BAM, a near-real-time wildfire mapping framework.

Global Flood Susceptibility Map application interface showing worldwide flood risk

Global Flood Susceptibility Map (GFSM v1)

A globally consistent 30-meter flood susceptibility dataset trained on approximately 30 million samples and terabyte-scale multi-source geospatial predictors, built on the Shaheen III supercomputer and Google Earth Engine.

BAM framework for wildfire burned area mapping visualization

BAM: Self-Supervised Burn Area Mapping

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.

High-resolution Flood Susceptibility Map of Pakistan visualization

Flood Risk Analytics for Pakistan

High-resolution (30m) flood susceptibility mapping and population exposure analysis for Pakistan, integrating ensemble machine learning with geospatial big data to support national disaster risk reduction.

View the full archive
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 research collaborations, consulting, and speaking on GeoAI, flood risk and disaster intelligence, and cloud-native Earth observation — from academic partnerships to applied geospatial projects.

Say Hello Connect
Email: waleedgeo@outlook.com