  The increasing availability of high-resolution satellite imagery has enabled
the use of machine learning to support land-cover measurement and inform
policy-making. However, labelling satellite images is expensive and is
available for only some locations. This prompts the use of transfer learning to
adapt models from data-rich locations to others. Given the potential for
high-impact applications of satellite imagery across geographies, a systematic
assessment of transfer learning implications is warranted. In this work, we
consider the task of land-cover segmentation and study the fairness
implications of transferring models across locations. We leverage a large
satellite image segmentation benchmark with 5987 images from 18 districts (9
urban and 9 rural). Via fairness metrics we quantify disparities in model
performance along two axes -- across urban-rural locations and across
land-cover classes. Findings show that state-of-the-art models have better
overall accuracy in rural areas compared to urban areas, through unsupervised
domain adaptation methods transfer learning better to urban versus rural areas
and enlarge fairness gaps. In analysis of reasons for these findings, we show
that raw satellite images are overall more dissimilar between source and target
districts for rural than for urban locations. This work highlights the need to
conduct fairness analysis for satellite imagery segmentation models and
motivates the development of methods for fair transfer learning in order not to
introduce disparities between places, particularly urban and rural locations.
