text stringlengths 0 820 |
|---|
(S-overall) and target (T-overall), urban data from the source (S- |
urban) and target (T-urban), rural data from the source (S-rural) |
and target (T-rural) is plotted when transferring models across dis- |
tricts. No adaptation is the source-only method, CBST and IAST |
are UDA methods. While overall accuracy drops on transfer, UDA |
methods have smaller accuracy drop. |
2920 |
Sub-task Method Mean Class-std Worst Sorted 30% (bottom, top) |
No adaptation 0.437 0.108 0.301 (0.322, 0.566) |
Rural→Urban CBST 0.469 0.123 0.326 (0.332 , 0.617) |
IAST 0.443 0.175 0.205 (0.211, 0.638 ) |
No adaptation 0.426 0.108 0.226 (0.271, 0.531) |
Urban→Rural CBST 0.467 0.129 0.228 (0.283, 0.599 ) |
IAST 0.454 0.120 0.229 (0.307 , 0.592) |
Table 3. Task C: Evaluation of urban-rural transfer. Three methods (No adaptation, CBST, IAST) are trained on rural districts and |
evaluated on urban districts, and vice versa. Results with the most improvements are marked in bold. UDA methods improve Mean IoU |
compared to No adaptation but increase standard deviation of IoUs across classes. |
from top 30% classes than urban results, but higher class- |
wise standard deviation. Overall, we observe rural-urban |
disparities in all four metrics. |
For Task B on transfer learning across districts, we sum- |
marize the results in Figure 2 and Table 2. Figure 2 visual- |
izes the mean IoU metric for both source and target districts. |
Based on the first two bar plots (dark and light orange) for |
No adapation, CBST, and IAST, we conclude that UDA |
methods improve overall segmentation accuracy on the tar- |
get domain (T-overall) compared to No adaptation (a de- |
crease of 22% and 24% vs that of 26%). Similar trend is ob- |
served for each of the source-target pairs for rural and urban |
separately. However, the performance gap between the ru- |
ral and urban data from target (T-rural and T-urban) remains |
large. For instance, from Table 2 we observe that CBST ob- |
tains mean IoU of 0.523 on urban area which is better than |
the ”No adaptation” 0.486, and IAST obtains 0.376 on rural |
area better than the ”No adaptation” 0.364. However, the |
networks remain unfair across rural-urban groups after the |
transfer (large values in the rural −urban columns). UDA |
methods further lower fairness: CBST increases the differ- |
ence of mean IoU between urban and rural by 22% (-0.122 |
to -0.149), increases the difference of standard deviation by |
69% (0.065 to 0.11), and increases the difference of worst- |
performing class’ IoU by 89% (-0.183 to -0.345). |
Next, we examine Task C on transfer learning from ur- |
ban domain to rural domain and vice versa. Results are |
summarized in Table 3. For both the transfer directions, |
the two UDA methods improve the overall accuracy, shown |
as higher mean IoU, higher IoU on the worst-preforming |
class, and higher mean IoU on bottom and top 30% classes. |
However, compared to ”No adaptation”, UDA methods dis- |
perse model performance across the classes, measured by |
higher standard deviation. For example, CBST increases |
Class-std from 0.108 to 0.123 on rural to urban transfer and |
from 0.108 to 0.129 on urban to rural transfer. Looking |
more closely into the CBST method which obtains the best |
overall transfer accuracy (0.469 and 0.467), its performance |
on each class is visualized in Figure 3. We observe that |
the IoU changes for each class upon transfer are highly un- |
Figure 3. Task C: Mean IoU upon transfer across rural-urban. |
Mean IoU for 7 landscape classes on source and target domain |
when transferring from rural area to urban area, and from ur- |
ban area to rural area, with the UDA method CBST. Performance |
changes vary substantially by class. |
equal. For example, in transferring from Rural →Urban, the |
network retains accuracy on the Water and Road classes, but |
lost significant accuracy ( 53%) on the Forest class. In trans- |
ferring from Urban →Rural, accuracy drops significantly on |
Road, Water, and Barren classes, but increases by 33% on |
the Forest class. |
2921 |
6. Discussion |
For the locations included in this study, segmentation re- |
sults showed a disparity in performance between rural and |
urban areas. Though the two groups obtain similar accu- |
racy on the respective bottom 30% performing land-cover |
classes, rural areas obtain better accuracy on the top 30% |
performing classes. Moreover, performance distribution |
across classes are different between rural and urban im- |
ages. Specifically, the segmentation model detects Forest |
and Agriculture classes well in their rural form, and detects |
Road and Water classes well in their urban form (detailed |
results are reported in Table 5 in the Appendix). Due to ur- |
banization, rural and urban areas have clear landscape dif- |
ferences. For example, roads are typically wider in the ur- |
ban scenes and narrower in rural scenes and water takes on |
larger shapes like lakes in urban scenes, and smaller shapes |
like ditches in rural scenes [52]. This may explain why the |
networks show advantages in urban images on Road and |
Water classes. Moreover, agricultural land covers large area |
and is continuously distributed in rural scenes. The per- |
centage of pixels with Agriculture and Forest elements is |
also higher [52]. This can facilitate learning on these two |
classes in rural areas as compared to urban areas. |
We considered two practical transfer learning tasks with |
satellite images and assess network fairness while trans- |
ferring across geographical locations, and across rural and |
urban areas. Broadly, we observed that when transferring |
across districts, networks made more unfair predictions on |
data from the new domain than data from the same domain |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.