  Contrastive self-supervised learning is widely employed in visual recognition
for geographic image data (remote or proximal sensing), but because of
landscape heterogeneity, models can show disparate performance across spatial
units. In this work, we consider fairness risks in such contrastive
pre-training; we show learnt representations present large performance gaps
across selected sensitive groups: urban and rural areas for satellite images
and city GDP level for street view images on downstream semantic segmentation.
We propose fair dense representations with contrastive learning (FairDCL) to
address the issue, a multi-level latent space de-biasing objective, using a
novel dense sensitive attribute encoding technique to constrain spurious local
information disparately distributes across groups. The method achieves improved
downstream task fairness and outperforms state-of-the-art methods for the
absence of a fairness-accuracy trade-off. Image embedding evaluation and
ablation studies further demonstrate effectiveness of FairDCL. As fairness in
geographic imagery is a nascent topic without existing state-of-the-art data or
results, our work motivates researchers to consider fairness metrics in such
applications, especially reinforced by our results showing no accuracy
degradation. Our code is available at:
https://anonymous.4open.science/r/FairDCL-1283
