Reachability Embeddings: Scalable Self-Supervised Representation Learning from Mobility Trajectories for Multimodal Geospatial Computer Vision-Reference-Cited by-同舟云学术

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

Published:2022-06 Issue: Volume: Page:
ISSN:
Container-title:2022 23rd IEEE International Conference on Mobile Data Management (MDM)
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Author:

Ganguli Swetava¹,Iyer C. V. Krishnakumar¹,Pandey Vipul¹

Affiliation:

1. Apple,Cupertino,CA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9861039/9861093/09861124.pdf?arnumber=9861124

Reference53 articles.

1. Deep Neural Networks for Learning Graph Representations

2. Semantic Proximity Search on Heterogeneous Graph by Proximity Embedding

3. Apple is rebuilding Maps from the ground up;panzarino;TechCrunch,2018

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2. SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets;Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems;2023-11-13

3. Self-Supervised Temporal Analysis of Spatiotemporal Data;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16

4. Quantitative reachability analysis of generalized possibilistic decision processes;Journal of Intelligent & Fuzzy Systems;2023-05-04

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