# Self-Supervised Temporal Analysis of Spatiotemporal Data

Source: [Self-Supervised Temporal Analysis of Spatiotemporal Data](<https://www.sota2.com/research/paper/68fa6f231e209131cea43fab>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T01:14:37.532Z

Authors: Yi Cao, Swetava Ganguli, Vipul Pandey

Published: 2023-04-25T20:34:38.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2304.13143>)

[PDF](<https://arxiv.org/pdf/2304.13143.pdf>)