Self-Supervised Temporal Analysis of Spatiotemporal Data
This work addresses the challenge of land use classification from geospatial data, offering an incremental improvement through a novel self-supervised approach.
The paper tackled the problem of analyzing spatiotemporal data by proposing a self-supervised method to stratify landscapes based on mobility activity time series, resulting in temporal embeddings that are semantically meaningful and effective for tasks like classifying residential and commercial areas.
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.