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5 papers
Synergistic Neural Forecasting of Air Pollution with Stochastic Sampling
Yohan Abeysinghe, Muhammad Akhtar Munir, Sanoojan Baliah +4
Air pollution remains a leading global health and environmental risk, particularly in regions vulnerable to episodic air pollution spikes due to wildfires, urban haze and dust stor…
TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation
Muhammad Sohail Danish, Muhammad Akhtar Munir, Syed Roshaan Ali Shah +5
Modern Earth observation (EO) increasingly leverages deep learning to harness the scale and diversity of satellite imagery across sensors and regions. While recent foundation model…
AirCast: Improving Air Pollution Forecasting Through Multi-Variable Data Alignment
Vishal Nedungadi, Muhammad Akhtar Munir, Marc Rußwurm +5
Air pollution remains a leading global health risk, exacerbated by rapid industrialization and urbanization, contributing significantly to morbidity and mortality rates. In this pa…
EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues
Sagar Soni, Akshay Dudhane, Hiyam Debary +8
Automated analysis of vast Earth observation data via interactive Vision-Language Models (VLMs) can unlock new opportunities for environmental monitoring, disaster response, and {r…
GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial Tasks
Muhammad Sohail Danish, Muhammad Akhtar Munir, Syed Roshaan Ali Shah +5
While numerous recent benchmarks focus on evaluating generic Vision-Language Models (VLMs), they do not effectively address the specific challenges of geospatial applications. Gene…