9 citations · 28 across the 8 of their papers we have counts for
8 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…