PHIL
Center For Artificial Intelligence Research (CAIR)
PHIL
Center For Artificial Intelligence Research (CAIR)
Mortality Triangulation
Building a clearer picture of how — and how many — people die across Pakistan
When death records are incomplete, whole populations of deaths — and their causes — go uncounted, and health policy is left guessing. Mortality Triangulation reconciles independent, imperfect data sources — civil registration, surveys and census, and verbal autopsy — into the best available estimate of all-cause and cause-specific mortality.
- Civil registration · Surveys · Census · Verbal autopsy
- All-cause & cause-specific mortality estimates
- Built where death records are incomplete
Join the Mortality Triangulation team — help count the uncounted.
muhammad.abubakar@bnu.edu.pk
LEADERSHIP.
Bridging artificial intelligence, health, and policy.
Hafiz Muhammad Abubakar
Faculty, SCIT · BNU
Research associate at PHIL working across remote sensing and applied AI for health.
Mr. Muhammad Ali
Faculty, SCIT · BNU
Builds and maintains the ML infrastructure and deployment pipelines behind PHIL projects.
OUR TEAM.
The researchers and students driving PHIL's work across health, environment, and society.
Hasnain Ahmad
Crop Mapping at Scale
Raahim Arbaz
Geo-AI Agents
Qossain Awais
Flood Mapping
Hania Ata
Spiking Neural Network for Pollution Source Detection
Mehkaan Khan
Environmental Indicators for GBD
Hasnain Ahmad
Crop Mapping at Scale
Raahim Arbaz
Geo-AI Agents
Qossain Awais
Flood Mapping
Hania Ata
Spiking Neural Network for Pollution Source Detection
Mehkaan Khan
Environmental Indicators for GBD
Hasnain Ahmad
Crop Mapping at Scale
Raahim Arbaz
Geo-AI Agents
Qossain Awais
Flood Mapping
Hania Ata
Spiking Neural Network for Pollution Source Detection
Mehkaan Khan
Environmental Indicators for GBD
Hasnain Ahmad
Crop Mapping at Scale
Raahim Arbaz
Geo-AI Agents
Qossain Awais
Flood Mapping
Hania Ata
Spiking Neural Network for Pollution Source Detection
Mehkaan Khan
Environmental Indicators for GBD
Hasnain Ahmad
Crop Mapping at Scale
Raahim Arbaz
Geo-AI Agents
Qossain Awais
Flood Mapping
Hania Ata
Spiking Neural Network for Pollution Source Detection
Mehkaan Khan
Environmental Indicators for GBD
Hasnain Ahmad
Crop Mapping at Scale
Raahim Arbaz
Geo-AI Agents
Qossain Awais
Flood Mapping
Hania Ata
Spiking Neural Network for Pollution Source Detection
Mehkaan Khan
Environmental Indicators for GBD
ABOUT.
AI for People and Health
The Planetary Health Intelligence at Center for Artificial Intelligence Research (PHIL) at Beaconhouse National University is a research lab advancing artificial intelligence through collaborative, interdisciplinary research. PHIL addresses critical global challenges — in health, environment, and society — through innovative, responsible AI solutions, combining Earth-observation, computer vision, and predictive modelling to deliver real-world impact.
Our Research Areas
Geo-AI
Geospatial analysis and environmental monitoring using AI.
Vision-AI
Computer vision techniques and image-processing algorithms.
Predict-AI
Predictive models to analyse and forecast behaviours and trends.
ML-Efficiency
Designing efficient, scalable machine-learning algorithms.
AI-Policy
Ensuring ethical AI deployment and societal benefit.
PROJECTS.
Explore our
PHIL
research projects
- Geo-AI
- Vision-AI
- Predict-AI
- ML-Efficiency
- AI-Policy
Mortality Triangulation
Poverty Mapping
Pollution Sources Detection
Deforestation Trends
EO & Global Burden of Disease
Urban Green Spaces
Trash Detection
PUBLICATIONS.
Peer-reviewed research from the Planetary Health Intelligence Lab — advancing AI for people, health, and the planet.
Remote Sensing of Urban Structure and Vegetation Effects on Atmospheric Pollutants
Hafiz Muhammad Abubakar, Hasnain Ahmad, Furqan Arshad, Zain Ali, Usman Nazir, Sara Khalid · IEEE IGARSS 2026 — International Geoscience and Remote Sensing Symposium · Washington, D.C.
Continuous Flood Nowcasting in South Asia: A Multi-Sensor Ensemble Remote Sensing Framework for Flood Extent
Usman Nazir, Disha Gomathinayagam, Muhammad Kamran, Sara Khalid · arXiv preprint arXiv:2605.10950
AlphaEarth Satellite Embeddings for Modelling Climate Sensitive Diseases Towards Global Health Resilience
Usman Nazir, I-Han Cheng, Sara Khalid · arXiv preprint arXiv:2605.10949
Evaluation of plant based indoor air purification in urban environments in Pakistan
Hafiz Muhammad Abubakar, Moeed Yusuf, Mariam Saghir, Hasnain Ahmad, Qossain Awais, Mehkaan Khan, Furqan Arshad, Zain Ali, Sara Khalid, Usman Nazir · Available at SSRN 6581360
Detecting Brick Kiln Infrastructure at Scale: Graph, Foundation, and Remote Sensing Models for Satellite Imagery Data
Usman Nazir, Xidong Chen, Hafiz Muhammad Abubakar, Hadia Abu Bakar, Raahim Arbaz, Fezan Rasool, Bin Chen, Sara Khalid · arXiv preprint arXiv:2602.13350
Foundation Models for Mapping Emission Sources and Acute Respiratory Infection (ARI) Hotspots
Usman Nazir, Sara Khalid · NeurIPS 2025 · Tackling Climate Change with Machine Learning Workshop
Predicting malaria outbreaks using earth observation measurements and spatiotemporal deep learning modelling: a South Asian case study from 2000 to 2017
Usman Nazir, Muhammad Talha Quddoos, Momin Uppal, Sara Khalid · The Lancet Planetary Health · Vol. 8 · p. S17 · Elsevier
Mapping Air Pollution Sources with Sequential Transformer Chaining: A Case Study in South Asia
Usman Nazir, Hafiz Muhammad Abubakar, Raahim Arbaz, Hasnain Ahmad, Mubasher Nazir · NeurIPS · Tackling Climate Change with Machine Learning (6th)
2022 Flood Impact in Pakistan: Remote Sensing Assessment of Agricultural and Urban Damage
Hafiz Muhammad Abubakar, Arbaz Khan, Aqs Younas, Zia Tahseen, Aqeel Arshad, Murtaza Taj, Usman Nazir · Proceedings of the AAAI Symposium Series · Vol. 4, Issue 1 · pp. 405–410
Towards a spatio-temporal deep learning approach to predict malaria outbreaks using earth observation measurements in South Asia
Usman Nazir, Ahzam Ejaz, Muhammad Talha Quddoos, Momin Uppal, Sara Khalid · NeurIPS 2023 · Tackling Climate Change with Machine Learning Workshop
Spatio-Temporal driven Attention Graph Neural Network with Block Adjacency matrix (STAG-NN-BA) for Remote Land-use Change Detection
Usman Nazir, Wadood Islam, Sara Khalid, Murtaza Taj · AAAI Symposium Series 2023
Kiln-Net: A Gated Neural Network for Detection of Brick Kilns in South Asia
Usman Nazir, Usman Khalid Mian, Muhammad Usman Sohail, Murtaza Taj, Momin Uppal · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS)
Tiny-Inception-ResNet-v2: Using Deep Learning for Eliminating Bonded Labors of Brick Kilns in South Asia
Usman Nazir, Numan Khurshid, Muhammad Ahmed Bhimra, Murtaza Taj · CVPR 2019 Workshop
CONTACT.
LET'S BUILD
AI FOR PEOPLE
AND HEALTH.
For collaborations, research partnerships, and more information