Search Results for author:
Found 24 papers, 8 papers with code
Cloud Optical Thickness Retrievals Using Angle Invariant Attention Based Deep Learning Models
Satellite radiance measurements enable global COT retrieval, but challenges like 3D cloud effects, viewing angles, and atmospheric interference must be addressed to ensure accurate estimation.
DeepTopoNet: A Framework for Subglacial Topography Estimation on the Greenland Ice Sheets
Understanding Greenland's subglacial topography is critical for projecting the future mass loss of the ice sheet and its contribution to global sea-level rise.
BACON: A fully explainable AI model with graded logic for decision making problems
As machine learning models and autonomous agents are increasingly deployed in high-stakes, real-world domains such as healthcare, security, finance, and robotics, the need for transparent and trustworthy explanations has become critical.
Enhancing Satellite Object Localization with Dilated Convolutions and Attention-aided Spatial Pooling
Object localization in satellite imagery is particularly challenging due to the high variability of objects, low spatial resolution, and interference from noise and dominant features such as clouds and city lights.
Joint Retrieval of Cloud properties using Attention-based Deep Learning Models
Accurate cloud property retrieval is vital for understanding cloud behavior and its impact on climate, including applications in weather forecasting, climate modeling, and estimating Earth's radiation balance.
Integrating Frequency-Domain Representations with Low-Rank Adaptation in Vision-Language Models
Situational awareness applications rely heavily on real-time processing of visual and textual data to provide actionable insights.
Building Machine Learning Challenges for Anomaly Detection in Science
We present the different datasets along with a scheme to make machine learning challenges around the three datasets findable, accessible, interoperable, and reusable (FAIR).
Hybrid Ensemble Deep Graph Temporal Clustering for Spatiotemporal Data
Classifying subsets based on spatial and temporal features is crucial to the analysis of spatiotemporal data given the inherent spatial and temporal variability.
Investigating Causal Cues: Strengthening Spoofed Audio Detection with Human-Discernible Linguistic Features
Several types of spoofed audio, such as mimicry, replay attacks, and deepfakes, have created societal challenges to information integrity.
gWaveNet: Classification of Gravity Waves from Noisy Satellite Data using Custom Kernel Integrated Deep Learning Method
Atmospheric gravity waves occur in the Earths atmosphere caused by an interplay between gravity and buoyancy forces.
Estimating Direct and Indirect Causal Effects of Spatiotemporal Interventions in Presence of Spatial Interference
We then propose our deep learning based potential outcome model for spatiotemporal causal inference.
YOLO based Ocean Eddy Localization with AWS SageMaker
Ocean eddies play a significant role both on the sea surface and beneath it, contributing to the sustainability of marine life dependent on oceanic behaviors.
Causality for Earth Science -- A Review on Time-series and Spatiotemporal Causality Methods
This survey paper covers the breadth and depth of time-series and spatiotemporal causality methods, and their applications in Earth Science.
TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data
The growing availability and importance of time series data across various domains, including environmental science, epidemiology, and economics, has led to an increasing need for time-series causal discovery methods that can identify the intricate relationships in the non-stationary, non-linear, and often noisy real world data.
MT-HCCAR: Multi-Task Deep Learning with Hierarchical Classification and Attention-based Regression for Cloud Property Retrieval
In response, this paper introduces MT-HCCAR, an end-to-end deep learning model employing multi-task learning to simultaneously tackle cloud masking, cloud phase retrieval (classification tasks), and COT prediction (a regression task).
Multi-graph Spatio-temporal Graph Convolutional Network for Traffic Flow Prediction
In our method, data normalization strategy is used to deal with data imbalance, due to long-tail distribution of traffic flow at network-wide toll stations.
MT-IceNet -- A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting
Arctic amplification has altered the climate patterns both regionally and globally, resulting in more frequent and more intense extreme weather events in the past few decades.
Deep Spatiotemporal Clustering: A Temporal Clustering Approach for Multi-dimensional Climate Data
Concentrating on joint deep representation learning of spatial and temporal features, we propose Deep Spatiotemporal Clustering (DSC), a novel algorithm for the temporal clustering of high-dimensional spatiotemporal data using an unsupervised deep learning method.
Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference
Through experiments on synthetic and observational data, we show how our research can substantially improve the ability to quantify leading causes of Arctic sea ice melt, further paving paths for causal inference in observational Earth science.
An Edge-Cloud Integrated Framework for Flexible and Dynamic Stream Analytics
In this paper, we study how to best leverage edge and cloud resources to achieve better accuracy and latency for stream analytics using a type of RNN model called long short-term memory (LSTM).
Reproducible and Portable Big Data Analytics in the Cloud
To tackle these problems, we leverage serverless computing and containerization techniques for automated scalable execution and reproducibility, and utilize the adapter design pattern to enable application portability and reproducibility across different clouds.
Sea Ice Forecasting using Attention-based Ensemble LSTM
Accurately forecasting Arctic sea ice from subseasonal to seasonal scales has been a major scientific effort with fundamental challenges at play.
Scalable and Hybrid Ensemble-Based Causality Discovery
Causality discovery mines cause-effect relationships among different variables of a system and has been widely used in many disciplines including climatology and neuroscience.
A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification
A self-organizing map (SOM) is a type of competitive artificial neural network, which projects the high-dimensional input space of the training samples into a low-dimensional space with the topology relations preserved.
