A comprehensive list of projects I've worked on, from GeoAI applications to educational materials.
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 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 (30m) flood susceptibility mapping and population exposure analysis for Pakistan, integrating ensemble machine learning with geospatial big data to support national disaster risk reduction.
Nationwide study quantifying the impact of urbanization-driven land cover changes on terrestrial carbon storage in Pakistan from 1990 to 2020, using remote sensing and machine learning.
| Year | Project | Links |
|---|---|---|
| 2026 | Global Flood Susceptibility Map (GFSM v1)
Featured
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. GeoAI ML | |
| 2026 | BAM: Self-Supervised Burn Area Mapping
Featured
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. GEE Python | |
| 2024 | Flood Risk Analytics for Pakistan
Featured
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. GEE GeoAI | |
| 2025 | High-resolution Flood Susceptibility Mapping in Pakistan
Floods GeoAI | |
| 2024 | Open-Sourced Google Earth Engine Projects Repository
GEE JavaScript | |
| 2024 | Urbanization Effects on Terrestrial Carbon Storage in Pakistan
Geo-BigData Land Cover Change | |
| 2023 | 30 Day Map Challenge 2023 Journey
ArcGIS Pro QGIS | |
| 2023 | LST, Urban Heat Island Effect, and UTFVI Analysis using Google Earth Engine and Landsat
GEE LST | |
| 2023 | Land Cover & Carbon Storage Change Assessment Nationwide study quantifying the impact of urbanization-driven land cover changes on terrestrial carbon storage in Pakistan from 1990 to 2020, using remote sensing and machine learning. GEE GeoAI | |
| 2023 | Mastering Machine Learning based Land Use Classification with Python
Python ML |