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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, multi-modal 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 and commercial areas.

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