  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 paper, 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 tensors, 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, while using upto 67% less trajectory data, 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
image-like tensor representations that can be combined with other sources of
imagery and are designed to facilitate multimodal learning in geospatial
computer vision.
