3 citations · 7 across the 11 of their papers we have counts for
11 papers
TinyNina: A Resource-Efficient Edge-AI Framework for Sustainable Air Quality Monitoring via Intra-Image Satellite Super-Resolution
Prasanjit Dey, Zachary Yahn, Bianca Schoen-Phelan +1
Nitrogen dioxide (NO) is a primary atmospheric pollutant and a significant contributor to respiratory morbidity and urban climate-related challenges. While satellite platforms…
NeuroDDAF: Neural Dynamic Diffusion-Advection Fields with Evidential Fusion for Air Quality Forecasting
Prasanjit Dey, Soumyabrata Dev, Angela Meyer +1
Accurate air quality forecasting is crucial for protecting public health and guiding environmental policy, yet it remains challenging due to nonlinear spatiotemporal dynamics, wind…
PollutionNet: A Vision Transformer Framework for Climatological Assessment of NO and SO Using Satellite-Ground Data Fusion
Prasanjit Dey, Soumyabrata Dev, Bianca Schoen-Phelan
Accurate assessment of atmospheric nitrogen dioxide (NO) and sulfur dioxide (SO) is essential for understanding climate-air quality interactions, supporting environmental p…
Meteorology-Driven GPT4AP: A Multi-Task Forecasting LLM for Atmospheric Air Pollution in Data-Scarce Settings
Prasanjit Dey, Soumyabrata Dev, Bianca Schoen-Phelan
Accurate forecasting of air pollution is important for environmental monitoring and policy support, yet data-driven models often suffer from limited generalization in regions with…
One-for-All: A Lightweight Stabilized and Parameter-Efficient Pre-trained LLM for Time Series Forecasting
Prasanjit Dey, Soumyabrata Dev, Bianca Schoen-Phelan
We address the challenge of adapting pre-trained Large Language Models (LLMs) for multivariate time-series analysis, where their deployment is often hindered by prohibitive computa…
TrafficKAN-GCN: Graph Convolutional-based Kolmogorov-Arnold Network for Traffic Flow Optimization
Jiayi Zhang, Yiming Zhang, Yuan Zheng +5
Urban traffic optimization is critical for improving transportation efficiency and alleviating congestion, particularly in large-scale dynamic networks. Traditional methods, such a…
Segregation and Context Aggregation Network for Real-time Cloud Segmentation
Yijie Li, Hewei Wang, Jiayi Zhang +5
Cloud segmentation from intensity images is a pivotal task in atmospheric science and computer vision, aiding weather forecasting and climate analysis. Ground-based sky/cloud segme…
RAINER: A Robust Ensemble Learning Grid Search-Tuned Framework for Rainfall Patterns Prediction
Zhenqi Li, Junhao Zhong, Hewei Wang +6
Rainfall prediction remains a persistent challenge due to the highly nonlinear and complex nature of meteorological data. Existing approaches lack systematic utilization of grid se…
CP2M: Clustered-Patch-Mixed Mosaic Augmentation for Aerial Image Segmentation
Yijie Li, Hewei Wang, Jinfeng Xu +4
Remote sensing image segmentation is pivotal for earth observation, underpinning applications such as environmental monitoring and urban planning. Due to the limited annotation dat…
DDUNet: Dual Dynamic U-Net for Highly-Efficient Cloud Segmentation
Yijie Li, Hewei Wang, Jinfeng Xu +4
Cloud segmentation amounts to separating cloud pixels from non-cloud pixels in an image. Current deep learning methods for cloud segmentation suffer from three issues. (a) Constrai…
UCloudNet: A Residual U-Net with Deep Supervision for Cloud Image Segmentation
Yijie Li, Hewei Wang, Shaofan Wang +3
Recent advancements in meteorology involve the use of ground-based sky cameras for cloud observation. Analyzing images from these cameras helps in calculating cloud coverage and un…