  Road segmentation in challenging domains, such as night, snow or rain, is a
difficult task. Most current approaches boost performance using fine-tuning,
domain adaptation, style transfer, or by referencing previously acquired
imagery. These approaches share one or more of three significant limitations: a
reliance on large amounts of annotated training data that can be costly to
obtain, both anticipation of and training data from the type of environmental
conditions expected at inference time, and/or imagery captured from a previous
visit to the location. In this research, we remove these restrictions by
improving road segmentation based on similar places. We use Visual Place
Recognition (VPR) to find similar but geographically distinct places, and fuse
segmentations for query images and these similar place priors using a Bayesian
approach and novel segmentation quality metric. Ablation studies show the need
to re-evaluate notions of VPR utility for this task. We demonstrate the system
achieving state-of-the-art road segmentation performance across multiple
challenging condition scenarios including night time and snow, without
requiring any prior training or previous access to the same geographical
locations. Furthermore, we show that this method is network agnostic, improves
multiple baseline techniques and is competitive against methods specialised for
road prediction.
