Scalable Label-efficient Footpath Network Generation Using Remote
  Sensing Data and Self-supervised Learning
Xinye Wanyan Sachith Seneviratne Kerry Nice Jason Thompson Marcus White Nano Langenheim Mark Stevenson
Footpath mapping, modeling, and analysis can provide important geospatial
insights to many fields of study, including transport, health, environment and
urban planning. The availability of robust Geographic Information System (GIS)
layers can benefit the management of infrastructure inventories, especially at
local government level with urban planners responsible for the deployment and
maintenance of such infrastructure. However, many cities still lack real-time
information on the location, connectivity, and width of footpaths, and/or
employ costly and manual survey means to gather this information. This work
designs and implements an automatic pipeline for generating footpath networks
based on remote sensing images using machine learning models. The annotation of
segmentation tasks, especially labeling remote sensing images with specialized
requirements, is very expensive, so we aim to introduce a pipeline requiring
less labeled data. Considering supervised methods require large amounts of
training data, we use a self-supervised method for feature representation
learning to reduce annotation requirements. Then the pre-trained model is used
as the encoder of the U-Net for footpath segmentation. Based on the generated
masks, the footpath polygons are extracted and converted to footpath networks
which can be loaded and visualized by geographic information systems
conveniently. Validation results indicate considerable consistency when
compared to manually collected GIS layers. The footpath network generation
pipeline proposed in this work is low-cost and extensible, and it can be
applied where remote sensing images are available. Github:
https://github.com/WennyXY/FootpathSeg.