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{miaozhng, hs3673, lcc9673, rumi.chunara }@nyu.edu |
Abstract |
The increasing availability of high-resolution satellite |
imagery has enabled the use of machine learning to support |
land-cover measurement and inform policy-making. How- |
ever, 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 satel- |
lite 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 lo- |
cations. 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 ru- |
ral areas compared to urban areas, through unsupervised |
domain adaptation methods transfer learning better to ur- |
ban versus rural areas and enlarge fairness gaps. In analy- |
sis of reasons for these findings, we show that raw satellite |
images are overall more dissimilar between source and tar- |
get districts for rural than for urban locations. This work |
highlights the need to conduct fairness analysis for satel- |
lite imagery segmentation models and motivates the devel- |
opment of methods for fair transfer learning in order not |
to introduce disparities between places, particularly urban |
and rural locations. |
1. Introduction |
Satellite imagery is becoming readily available with |
around 1030 active satellites that are dedicated to earth ob- |
servation [36]. Out of the different spectra of imagery avail- |
able from such satellites, visible spectrum imagery is partic- |
ularly relevant for many applications based on the extremelyhigh resolution and according ability to resolve specific ob- |
jects of interest [5]. Consequently, satellite images com- |
bined with semantic segmentation, the task of clustering |
parts of an image together which belong to the same ob- |
ject class, can be used to detect objects ranging from natu- |
ral features (water bodies, forests) to human land-use types |
(buildings, roads). The extracted information is being ap- |
plied in a wide range of settings including urban planning |
[34], modelling disease spread [1], aiding disaster relief |
efforts [16, 57], and detecting and mapping environmental |
phenomena [24,55]. However, because segmentation mod- |
els employ supervised learning, availability of ground truth |
data is a major bottleneck for their training. Annotation for |
the segmentation task is particularly labor intensive as it re- |
quires fine-grained labels at the level of pixels which results |
in incomplete or noisy ground truth data [45]. In such sit- |
uations, generalizing existing models to non-annotated data |
bytransfer learning is a widely applied solution [43, 48]. |
Transfer learning uses knowledge learnt from the same |
or related tasks to improve learning on the task at hand (see |
Pan and Yang [39] for a survey). We will focus on a type of |
transfer learning setting called domain adaptation , where |
we have a single task but the train and test domains may |
differ. The key challenge here is the discrepancy in data |
distributions between domains. In the case of satellite im- |
agery, the discrepancies commonly result from transferring |
models to new geographies where the landscapes are dis- |
similar to where the model was trained. For example, Islam |
[22] finds that a well-trained seagrass detection model from |
satellite images fails when tested at other locations with dif- |
ferent seagrass density. To mitigate the degrading effects of |
domain discrepancies on segmentation accuracy, previous |
work has re-designed network architectures [28], loss func- |
tions [18,50], and batch normalization methods [38] to im- |
prove model generalization. Other approaches include us- |
ing labels at a coarser granularity for the target domain (e.g. |
image-level labels) as weak supervision [21] and learning |
latent representations shared between source and target do- |
2916 |
mains to help in adaptation [26, 29]. |
Simultaneously, while machine learning approaches |
have been used to improve prediction in a variety of tasks, |
recent studies have highlighted concerns towards model |
fairness, exhibited by performance disparities across sen- |
sitive groups based on geography, demographics, and eco- |
nomic indicators [31,58]. A push for model fairness aligns |
with the ideal of equity defined by World Health Organiza- |
tion as “Equity is the absence of unfair, avoidable or reme- |
diable differences among groups of people, whether those |
groups are defined socially, economically, demographically, |
or geographically or by other dimensions of inequality (e.g. |
sex, gender, ethnicity, disability, or sexual orientation).” |
Real-world examples have demonstrated the harmful effects |
of unfair machine learning models, such as facial recogni- |
tion software that performs worse on darker women [4] and |
advertisement systems that deliver economic opportunity- |
related ads less often to women than men [25]. Indeed, |
discriminatory issues persist even in state-of-the-art learn- |
ing methods [32]. Expanding types of data used in ma- |
chine learning tasks, such as satellite imagery, enables in- |
creased use in a wide range of daily-life applications and |
ever-increasing social impacts. Accordingly, broader as- |
pects and viewpoints of performance, such as fairness, need |
to be ascertained in multiple machine learning subareas. |
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