Paahuni Khandelwal

Orcid: 0000-0002-9026-5242

According to our database1, Paahuni Khandelwal authored at least 11 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
DeepSoil: A Science-guided Framework for Generating High Precision Soil Moisture Maps by Reconciling Measurement Profiles Across In-situ and Remote Sensing Data.
Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems, 2024

2023
Deep Learning based Approach for Fast, Effective Visualization of Voluminous Gridded Spatial Observations.
Proceedings of the 23rd IEEE/ACM International Symposium on Cluster, 2023

Enabling Fast, Effective Visualization of Voluminous Gridded Spatial Datasets.
Proceedings of the 23rd IEEE/ACM International Symposium on Cluster, 2023

DISCERN: Leveraging Knowledge Distillation to Generate High Resolution Soil Moisture Estimation from Coarse Satellite Data.
Proceedings of the IEEE International Conference on Big Data, 2023

ARGUS: Rapid Wildfire Tracking Using Satellite Data Collections.
Proceedings of the 16th IEEE International Conference on Cloud Computing, 2023

2022
Attention-based convolutional capsules for evapotranspiration estimation at scale.
Environ. Model. Softw., 2022

CloudNet: A Deep Learning Approach for Mitigating Occlusions in Landsat-8 Imagery using Data Coalescence.
Proceedings of the 18th IEEE International Conference on e-Science, 2022

2021
Mind the Gap: Generating Imputations for Satellite Data Collections at Myriad Spatiotemporal Scopes.
Proceedings of the 21st IEEE/ACM International Symposium on Cluster, 2021

2020
Small is Beautiful: Distributed Orchestration of Spatial Deep Learning Workloads.
Proceedings of the 13th IEEE/ACM International Conference on Utility and Cloud Computing, 2020

Lightweight, Embeddings Based Storage and Model Construction Over Satellite Data Collections.
Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

2019
STASH : Fast Hierarchical Aggregation Queries for Effective Visual Spatiotemporal Explorations.
Proceedings of the 2019 IEEE International Conference on Cluster Computing, 2019


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