SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets

no code implementations • 26 Sep 2023 • Daria Reshetova, Swetava Ganguli, C. V. Krishnakumar Iyer, Vipul Pandey

We propose a Self-supervised Anomaly Detection technique, called SeMAnD, to detect geometric anomalies in Multimodal geospatial datasets.

Anomaly Classification Data Augmentation +3

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

no code implementations • 7 Oct 2022 • Swetava Ganguli, C. V. Krishnakumar Iyer, Vipul Pandey

In this work, we propose a self-supervised method for learning representations of geographic locations from unlabeled GPS trajectories to solve downstream geospatial computer vision tasks.

Representation Learning Semantic Segmentation

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

no code implementations • 24 Oct 2021 • Swetava Ganguli, C. V. Krishnakumar Iyer, Vipul Pandey

In this paper, we propose a self-supervised method for learning representations of geographic locations from unlabeled GPS trajectories to solve downstream geospatial computer vision tasks.

Representation Learning Semantic Segmentation

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

no code implementations • 21 Jun 2021 • C. V. Krishnakumar Iyer, Feili Hou, Henry Wang, Yonghong Wang, Kay Oh, Swetava Ganguli, Vipul Pandey

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 solving a variety of complex problems on their own.

Feature Engineering Semantic Segmentation