9 citations · 28 across the 8 of their papers we have counts for
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cs.AI2023★ 3 cited
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…
cs.AI2023
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…