Swetava Ganguli

Orcid: 0000-0002-7392-4867

According to our database1, Swetava Ganguli authored at least 14 papers between 2017 and 2024.

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

Timeline

Legend:

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In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Temporal Embeddings: Scalable Self-Supervised Temporal Representation Learning from Spatiotemporal Data for Multimodal Computer Vision.
CoRR, 2024

2023
Self-Supervised Temporal Analysis of Spatiotemporal Data.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2023

SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets.
Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, 2023

2022
Scalable Self-Supervised Representation Learning from Spatiotemporal Motion Trajectories for Multimodal Computer Vision.
CoRR, 2022

Reachability Embeddings: Scalable Self-Supervised Representation Learning from Mobility Trajectories for Multimodal Geospatial Computer Vision.
Proceedings of the 23rd IEEE International Conference on Mobile Data Management, 2022

2021
Reachability Embeddings: Scalable Self-Supervised Representation Learning from Markovian Trajectories for Geospatial Computer Vision.
CoRR, 2021

Conditional Generation of Synthetic Geospatial Images from Pixel-level and Feature-level Inputs.
CoRR, 2021

Trinity: A No-Code AI platform for complex spatial datasets.
Proceedings of the GeoAI@SIGSPATIAL 2021: Proceedings of the 4th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, 2021

2020
VAE-Info-cGAN: generating synthetic images by combining pixel-level and feature-level geospatial conditional inputs.
Proceedings of the IWCTS@SIGSPATIAL 2020: Proceedings of the 13th ACM SIGSPATIAL International Workshop on Computational Transportation Science, 2020

2019
Semi-Supervised Multitask Learning on Multispectral Satellite Images Using Wasserstein Generative Adversarial Networks (GANs) for Predicting Poverty.
CoRR, 2019

Predicting US State-Level Agricultural Sentiment as a Measure of Food Security with Tweets from Farming Communities.
CoRR, 2019

GeoGAN: A Conditional GAN with Reconstruction and Style Loss to Generate Standard Layer of Maps from Satellite Images.
CoRR, 2019

Predicting Food Security Outcomes Using Convolutional Neural Networks (CNNs) for Satellite Tasking.
CoRR, 2019

2017
Machine Learning for Better Models for Predicting Bond Prices.
CoRR, 2017


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