DeepAI AI Chat
Log In Sign Up

Self-Supervised Temporal Analysis of Spatiotemporal Data

by   Yi Cao, et al.
Apple Inc.

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.


page 2

page 3

page 4


Self-supervision of wearable sensors time-series data for influenza detection

Self-supervision may boost model performance in downstream tasks. Howeve...

Pixel-wise Agricultural Image Time Series Classification: Comparisons and a Deformable Prototype-based Approach

Improvements in Earth observation by satellites allow for imagery of eve...

Are uGLAD? Time will tell!

We frequently encounter multiple series that are temporally correlated i...

DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation

Earth observation is a fundamental tool for monitoring the evolution of ...