21 citations · 92 across the 15 of their papers we have counts for
15 papers
Temporal Embeddings: Scalable Self-Supervised Temporal Representation Learning from Spatiotemporal Data for Multimodal Computer Vision
Yi Cao, Swetava Ganguli, Vipul Pandey
There exists a correlation between geospatial activity temporal patterns and type of land use. A novel self-supervised approach is proposed to stratify landscape based on mobility…
SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets
Daria Reshetova, Swetava Ganguli, C. V. Krishnakumar Iyer +1
We propose a Self-supervised Anomaly Detection technique, called SeMAnD, to detect geometric anomalies in Multimodal geospatial datasets. Geospatial data comprises of acquired and…
Self-Supervised Temporal Analysis of Spatiotemporal Data
Yi Cao, Swetava Ganguli, Vipul Pandey
There exists a correlation between geospatial activity temporal patterns and type of land use. A novel self-supervised approach is proposed to stratify landscape based on mobility…
Scalable Self-Supervised Representation Learning from Spatiotemporal Motion Trajectories for Multimodal Computer Vision
Swetava Ganguli, C. V. Krishnakumar Iyer, Vipul Pandey
Self-supervised representation learning techniques utilize large datasets without semantic annotations to learn meaningful, universal features that can be conveniently transferred…
Reachability Embeddings: Scalable Self-Supervised Representation Learning from Mobility Trajectories for Multimodal Geospatial Computer Vision
Swetava Ganguli, C. V. Krishnakumar Iyer, Vipul Pandey
Self-supervised representation learning techniques utilize large datasets without semantic annotations to learn meaningful, universal features that can be conveniently transferred…
Conditional Generation of Synthetic Geospatial Images from Pixel-level and Feature-level Inputs
Xuerong Xiao, Swetava Ganguli, Vipul Pandey
Training robust supervised deep learning models for many geospatial applications of computer vision is difficult due to dearth of class-balanced and diverse training data. Converse…
Trinity: A No-Code AI platform for complex spatial datasets
C. V. Krishnakumar Iyer, Feili Hou, Henry Wang +4
We present a no-code Artificial Intelligence (AI) platform called Trinity with the main design goal of enabling both machine learning researchers and non-technical geospatial domai…
VAE-Info-cGAN: Generating Synthetic Images by Combining Pixel-level and Feature-level Geospatial Conditional Inputs
Xuerong Xiao, Swetava Ganguli, Vipul Pandey
Training robust supervised deep learning models for many geospatial applications of computer vision is difficult due to dearth of class-balanced and diverse training data. Converse…
Drag of a Heated Sphere at Low Reynolds Numbers in the Presence of Buoyancy
Swetava Ganguli, Sanjiva K. Lele
Fully resolved simulations are used to quantify the effects of heat transfer in the presence of buoyancy on the drag of a spatially fixed heated spherical particle at low Reynolds…
Linear Stability Analysis of a Normal Shock Train in a Constant Area Isolator of a Hypersonic ScramJet
Swetava Ganguli
Recent experiments in hypersonic vehicle technology have had some successful outcomes (ex. NASA X-43, Boeing X-51) but also have seen some unexpected failures (DARPA Hypersonic Tes…
Semi-Supervised Multitask Learning on Multispectral Satellite Images Using Wasserstein Generative Adversarial Networks (GANs) for Predicting Poverty
Anthony Perez, Swetava Ganguli, Stefano Ermon +3
Obtaining reliable data describing local poverty metrics at a granularity that is informative to policy-makers requires expensive and logistically difficult surveys, particularly i…
Predicting US State-Level Agricultural Sentiment as a Measure of Food Security with Tweets from Farming Communities
Jared Dunnmon, Swetava Ganguli, Darren Hau +1
The ability to obtain accurate food security metrics in developing areas where relevant data can be sparse is critically important for policy makers tasked with implementing food a…
GeoGAN: A Conditional GAN with Reconstruction and Style Loss to Generate Standard Layer of Maps from Satellite Images
Swetava Ganguli, Pedro Garzon, Noa Glaser
Automatically generating maps from satellite images is an important task. There is a body of literature which tries to address this challenge. We created a more expansive survey of…
Predicting Food Security Outcomes Using Convolutional Neural Networks (CNNs) for Satellite Tasking
Swetava Ganguli, Jared Dunnmon, Darren Hau
Obtaining reliable data describing local Food Security Metrics (FSM) at a granularity that is informative to policy-makers requires expensive and logistically difficult surveys, pa…
Machine Learning for Better Models for Predicting Bond Prices
Swetava Ganguli, Jared Dunnmon
Bond prices are a reflection of extremely complex market interactions and policies, making prediction of future prices difficult. This task becomes even more challenging due to the…