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as training. For the network trained without any adapta- |
tion, the mean IoU accuracy difference between rural and |
urban images on the target domain is around 64% higher |
than the difference reported in the single-domain task. Sim- |
ilarly, all other fairness metrics show much higher differ- |
ences between groups on transferring to the target domain. |
Notably, when applying UDA methods, CBST and IAST, |
transfer accuracy was improved, but at the cost of fairness |
damage. These methods further enlarged the performance |
gap between rural and urban groups measured in all four |
metrics. These findings indicate a need for new domain |
adaptation methods that tackle the challenge of fair trans- |
fer learning. |
One of the possible reasons why the network can bet- |
ter adapt to urban images than rural images is the unequal |
domain discrepancy. To estimate how similar the source |
and target images are in the rural and urban groups, we use |
two metrics – Proxy-A-distance (PAD) [12] and Maximum |
mean discrepancy (MMD) [15]. Both measure the dissim- |
ilarity between data distributions of different domains. We |
randomly sample 100 images from each domain at a time |
and ran 30 trials to compute the two measures. The mean |
and standard deviation of distance across the trials are re- |
ported in Table 4. We observe that the raw satellite imagesGroup Source Target PAD MMD |
Urban Gulou Yuhuatai 0.26 0.207 |
Qinhuai Jintan (±0.12) (±0.0235) |
Qixia |
Jinghan |
Rural Pukou Liuhe 0.64 0.262 |
Gaochun Huangpi (±0.21) (±0.0437) |
Lishui |
Jingxia |
Table 4. Shift in raw image distribution. Measurement of |
source-target domain distance using two metrics – Proxy-A- |
distance (PAD) and Maximum mean discrepancy (MMD). The |
implementation is based on the online codebase in [51]. Rural |
images shift more than urban images. |
Figure 4. Shifts in class distribution. Class distribution in terms |
of proportion of pixels per class is plotted for urban images from |
source domain (S-urban), urban images from target domain (T- |
urban), rural images from source domain (S-rural), and rural im- |
ages from target domain (T-rural). Class distribution is substan- |
tially different for all subsets. |
are overall more dissimilar between source and target dis- |
tricts for rural than for urban, which is a likely cause of |
the unequal transfer learning performance between the two |
groups. Figure 1 illustrates example images from both do- |
mains and the segmentation predictions our network made |
on the target. We see that Buildings across source and tar- |
get districts are of similar shape and arrangement for urban, |
but they are disordered and dissimilar for rural. Accord- |
ingly, the model segmentation map shows that the model |
segments urban buildings well but fails to detect most of |
the rural buildings. This observation indicates the impor- |
tance of checking class differences besides overall differ- |
ences across the whole image. |
Along these lines, we define pixel-wise class distribu- |
tion as the proportion of pixels belonging to each class. We |
assess shifts in the class distribution between source and |
target for both rural and urban images. Class distributions |
are shown in Figure 4. For urban locations, the Water and |
2922 |
Agriculture classes have the largest shifts from source to |
target. For rural, the Background and Forest classes have |
the largest shifts. Indeed there are large class shifts between |
source and target for both the urban and rural data. For |
example, the class distribution of urban target data seems |
more rural-like with an increased proportion in the Agricul- |
ture class. This emphasizes the internal variation in rural |
and urban categories. Moreover, as our data consists of im- |
ages from just one set of locations, data from different loca- |
tions are needed for more generalizable conclusions. How- |
ever, based on the selection of classes examined in our data |
which are common land-use classes globally, some results |
(such as in Table 4 indicate common threads that can be |
applicable to rural-urban disparities in general. |
In the second transfer learning task, the networks show |
unfairness on rural to urban domain transfer. Differences |
between rural and urban scenes provide explanation for |
why almost all classes lost accuracy on the target do- |
main. Some classes show opposite transfer performance |
in the two sub-tasks of Task C. The Road class lost only |
4% accuracy on Rural →Urban transfer but lost 41% ac- |
curacy on Urban →Rural transfer. The Water class shows |
similar patterns, whereas the Forest class lost 53% accu- |
racy on Rural →Urban transfer but gains 33% accuracy on |
Urban→Rural transfer. These observations indicate that for |
some classes, the features learnt by the networks from rural |
scenes can be easily adapted to interpret urban scenes, and |
some classes have the opposite case. From this perspective, |
features of different classes can have very different general- |
ization ability, which will cause the transfer performance to |
be highly unequal across classes. This feature-specific char- |
acteristic may be leveraged in the design of future transfer |
learning methods. |
7. Conclusion |
Transfer learning models for semantic segmentation are |
often evaluated based on overall accuracy metrics. Here, |
we expand the scope of their evaluation by conducting a |
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