  Self-supervised representation learning techniques utilize large datasets
without semantic annotations to learn meaningful, universal features that can
be conveniently transferred to solve a wide variety of downstream supervised
tasks. In this work, we propose a self-supervised method for learning
representations of geographic locations from unlabeled GPS trajectories to
solve downstream geospatial computer vision tasks. Tiles resulting from a
raster representation of the earth's surface are modeled as nodes on a graph or
pixels of an image. GPS trajectories are modeled as allowed Markovian paths on
these nodes. A scalable and distributed algorithm is presented to compute
image-like representations, called reachability summaries, of the spatial
connectivity patterns between tiles and their neighbors implied by the observed
Markovian paths. A convolutional, contractive autoencoder is trained to learn
compressed representations, called reachability embeddings, of reachability
summaries for every tile. Reachability embeddings serve as task-agnostic,
feature representations of geographic locations. Using reachability embeddings
as pixel representations for five different downstream geospatial tasks, cast
as supervised semantic segmentation problems, we quantitatively demonstrate
that reachability embeddings are semantically meaningful representations and
result in 4-23% gain in performance, as measured using area under the
precision-recall curve (AUPRC) metric, when compared to baseline models that
use pixel representations that do not account for the spatial connectivity
between tiles. Reachability embeddings transform sequential, spatiotemporal
mobility data into semantically meaningful tensor representations that can be
combined with other sources of imagery and are designed to facilitate
multimodal learning in geospatial computer vision.
