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

翻译：对比自监督学习广泛用于地理图像数据（遥感或近感）的视觉识别中，但由于景观异质性，模型在不同空间单元上的性能可能表现出差异。本文研究了此类对比预训练中的公平性风险；我们发现，所学表征在下游语义分割任务中，针对所选敏感群体（卫星图像的城市与农村区域、街景图像的城市GDP水平）存在显著性能差距。我们提出基于对比学习的公平密集表征（FairDCL）方法解决该问题，这是一种多层级潜在空间去偏目标，通过新颖的密集敏感属性编码技术，约束跨群体分布的虚假局部信息差异。该方法提升了下游任务的公平性，并在无公平-精度权衡的情况下优于现有最优方法。图像嵌入评估与消融研究进一步证实了FairDCL的有效性。由于地理影像公平性是一个尚无现有最优数据或结果的新兴课题，我们的工作激励研究人员在此类应用中考虑公平性指标——尤其基于我们未出现精度下降的结果。代码已开源：https://anonymous.4open.science/r/FairDCL-1283