Summary of Self-supervised Temporal Analysis Of Spatiotemporal Data, by Yi Cao and Swetava Ganguli and Vipul Pandey
Self-Supervised Temporal Analysis of Spatiotemporal Data
by Yi Cao, Swetava Ganguli, Vipul Pandey
First submitted to arxiv on: 25 Apr 2023
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel self-supervised approach is proposed to stratify landscape based on mobility activity time series, exploiting the correlation between geospatial activity temporal patterns and type of land use. By transforming the time series signal into frequency domain and compressing it with a contractive autoencoder, cyclic temporal patterns are preserved, allowing for task-agnostic temporal embeddings that can be converted into image-like channels. These representations are semantically meaningful and effective across different tasks such as classifying residential areas and commercial areas. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to group landscapes based on how people move around over time. By looking at patterns in when people are moving, researchers can figure out what kind of land use is happening. They do this by changing the way they look at time series data and then using it for tasks like recognizing different types of areas. |
Keywords
* Artificial intelligence * Autoencoder * Self supervised * Time series
