Self-Supervised Temporal Analysis of Spatiotemporal Data
- URL: http://arxiv.org/abs/2304.13143v1
- Date: Tue, 25 Apr 2023 20:34:38 GMT
- Title: Self-Supervised Temporal Analysis of Spatiotemporal Data
- Authors: Yi Cao and Swetava Ganguli and Vipul Pandey
- Abstract summary: 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 activity time series.
Experiments show that temporal embeddings are semantically meaningful representations of time series data and are effective across different tasks.
- Score: 2.2720298829059966
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: 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 activity time series. First, the time series signal
is transformed to the frequency domain and then compressed into task-agnostic
temporal embeddings by a contractive autoencoder, which preserves cyclic
temporal patterns observed in time series. The pixel-wise embeddings are
converted to image-like channels that can be used for task-based, multimodal
modeling of downstream geospatial tasks using deep semantic segmentation.
Experiments show that temporal embeddings are semantically meaningful
representations of time series data and are effective across different tasks
such as classifying residential area and commercial areas.
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