text stringlengths 0 820 |
|---|
In this work we study the fairness impacts of transfer |
learning with satellite imagery . To accomplish this goal, |
we test multiple semantic segmentation models across dif- |
ferent geographies. We then assess if such models made |
fair predictions on both the source and the target data. We |
focus on differences between urban and rural areas (i.e. ur- |
ban/rural categorization is the sensitive attribute) due to per- |
sistent and striking disparities between urban and rural ar- |
eas, especially for poor populations [2, 3]. The unfairness |
criterion in this work is based on differences in error rates |
across protected groups where the error rate is computed |
using Intersection-over-Union (IoU), a standard segmenta- |
tion metric. We also examine model performance disparity |
across different land-cover classes. Results show that ex- |
isting domain adaptation methods do not maintain fairness |
properties on transfer, either across protected groups or fea- |
ture classes. This work serves as a valuable demonstration |
of fairness being an critical issue in transfer learning using |
a large freely-available satellite imagery dataset. |
Important takeaways are as follows. |
• Studied models have better overall accuracy (via mean |
IoU over the 7 classes) on rural districts as compared to |
urban districts. |
• For common unsupervised domain adaptation methods, |
transfer accuracy is improved, but at the cost of fairness; |
the performance gap between rural and urban group is |
enlarged indicating the need to design new methods thattransfer well for both the groups. |
• Investigating reasons for the above findings, we find that |
images from rural districts differ more across locations |
than those of urban districts. |
2. Related Work |
Before discussing prior work on transfer learning for |
satellite images, we describe some of the alternative ways |
to address label scarcity and their shortcomings. Lastly, we |
summarize work in the nascent area of fair transfer learning. |
Approaches to tackle annotation burden for satellite |
images Given the difficulty in labelling data for semantic |
segmentation of satellite images, Schmitt, et al. [45] de- |
veloped weakly-supervised learning methods, where noisy, |
limited, or imprecise data sources are used to provide su- |
pervision signal. Previous work has leveraged the spatial |
context to develop unsupervised losses which, for exam- |
ple, penalize nearby pixels with different predicted labels |
[35, 50]. In another approach, Castillo-Navarro, et al. [6] |
proposed auxiliary losses based on self-supervised image |
reconstruction to improve the performance on the main task |
of image segmentation. To improve efficiency of label-use, |
Wang et al. [53] transferred classification models trained |
with image-level labels to image segmentation tasks and |
achieved high accuracy. While these approaches demon- |
strate successful combination of labeled and unlabeled im- |
ages, they assume that the images are from the same domain |
(or distribution). However, the assumption of a consistent |
domain is not realistic for problems involving satellite im- |
ages which are often from different geographies. Thus, such |
approaches are not straight-forwardly applicable in our set- |
ting. |
Transfer learning for satellite images Transferability |
of satellite image segmentation models across different ge- |
ographic locations has been studied in Ghorbanzadeh et |
al.[14]. Using train and test sets across 3 different ge- |
ographies (Taiwan, China, and Japan), they show consis- |
tent decrease in evaluation scores upon transfer. Previous |
work has incorporated domain adaptation methods to deal |
with the challenge. For instance, Tran et al. [49] pro- |
posed a two-stage transfer learning structure which gen- |
erated pseudo-ground truth segmentation labels for target |
data. Algorithms to improve the quality of such pseudo la- |
bels were studied in [33,59]. Data augmentation is another |
strategy for domain adaptation. Ji et al. augments images |
to simulate perturbations due to atmospheric radiation and |
demonstrate improved generalization of CNN-based mod- |
els [23]. These studies show the promise of adapting mod- |
els to data from different locations. But, the transfer is only |
evaluated based on overall accuracy for the domain, such as |
using Intersection-over-Union (IoU) to measure the over- |
lap between predicted segmentation maps and ground-truth |
2917 |
masks. Past work does not study fine-grained measures of |
model performance on transfer, like how is the performance |
for different subgroups in the domain (based on sensitive |
attributes or land-use types) impacted. The risk that dis- |
crepancy between domains in transfer learning may impact |
subgroups unfairly remains unexplored. |
Fairness in transfer learning Following the work in fair |
machine learning literature [32], we will narrowly classify |
the study of performance differences between subgroups |
asmodel fairness analysis. Compared to fairness analysis |
within the same domain, little work has studied transfer of |
fair models across domains. The two objectives–improving |
transfer accuracy and maintaining fairness–can be at odds |
with each other [47, 56]. Schumann et al. [46] formalized |
the problem of fair transfer learning which sets the learn- |
ing objective to improve accuracy as well as fairness in the |
target domain. Multiple approaches to fair transfer learning |
have been proposed [8, 27, 30, 37, 41, 42, 47] that make var- |
ious assumptions on how the domains differ and what data |
is available. Even when labels are not available for the tar- |
get domain, like in our setting, methods typically make the |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.