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SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets

Autor: Reshetova, Daria, Ganguli, Swetava, Iyer, C. V. Krishnakumar, Pandey, Vipul

We propose a Self-supervised Anomaly Detection technique, called SeMAnD, to detect geometric anomalies in Multimodal geospatial datasets. Geospatial data comprises of acquired and derived heterogeneous data modalities that we transform to semanticall

Externí odkaz: http://arxiv.org/abs/2309.15245

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Scalable Self-Supervised Representation Learning from Spatiotemporal Motion Trajectories for Multimodal Computer Vision

Autor: Ganguli, Swetava, Iyer, C. V. Krishnakumar, Pandey, Vipul

Self-supervised representation learning techniques utilize large datasets without semantic annotations to learn meaningful, universal features that can be conveniently transferred to solve a wide variety of downstream supervised tasks. In this work,

Externí odkaz: http://arxiv.org/abs/2210.03289

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Reachability Embeddings: Scalable Self-Supervised Representation Learning from Mobility Trajectories for Multimodal Geospatial Computer Vision

Autor: Ganguli, Swetava, Iyer, C. V. Krishnakumar, Pandey, Vipul

Externí odkaz: http://arxiv.org/abs/2110.12521

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Trinity: A No-Code AI platform for complex spatial datasets

Autor: Iyer, C. V. Krishnakumar, Hou, Feili, Wang, Henry, Wang, Yonghong, Oh, Kay, Ganguli, Swetava, Pandey, Vipul

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 domain experts to experiment with domain-specific signals and datasets for

Externí odkaz: http://arxiv.org/abs/2106.11756

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