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covariate shift assumption which says that the labeling rule |
remains the same between the domains and only the fea- |
ture distribution changes. In this setting, Coston et al. [8] |
propose a method for fair transfer even in cases where sen- |
sitive attributes are absent from one of the domains. Other |
approaches do not require access to target domain data al- |
together and instead either make causal assumptions on the |
discrepancy [47] or hypothesize a set of target domains and |
optimize against them [10, 30]. Finally, Szab ´oet al. [19] |
conduct a fairness evaluation of segmentation methods as- |
suming a single domain. |
However, none of the existing works study fair transfer |
learning for semantic segmentation models. The task differs |
considerably from the above settings as the input data is |
high-dimensional, and the model output and loss function |
for segmentation are different. We take the first step in this |
direction by demonstrating the need for such methods via a |
thorough empirical study on a relevant application. |
3. Dataset |
We use the publicly available, high spatial resolution |
land-cover dataset called LoveDA [52] in this study. Com- |
pared to other popular satellite image datasets, such as |
Zurich Summer [50], DeepGlobe [9], and DSTL [20], the |
recently released LoveDA has more annotated images and |
includes images from diverse locations. The dataset con- |
sists of 5987 images of size 1024 ×1024 and spatial reso- |
lution 0.3m. The images are collected from 18 adminis- |
trative districts from three cities in China, namely Nanjing, |
Changzhou, and Wuhan. Out of these, there are 9 urban dis- |
tricts and 9 rural districts, categorized based on their popu- |
lation density and level of economic development. We use |
Figure 1. Sample images from urban and rural scenes. For each |
scene, one image from source domain districts, one from target |
domain districts, and the network’s segmentation predictions (Seg- |
map) for the 7 land-cover classes on that target image are shown. |
satellite images from the 12 districts for which ground-truth |
masks are available: Gulou, Qinhuai, Qixia, Yuhuatai, Jin- |
tan, and Jianghan (urban); Pukou, Lishui, Liuhe, Huangpi, |
Gaochun, and Jiangxia (rural). The remaining 6 districts |
are not availanble as they are held out for the benchmark |
challenge. The dataset contains segmentation masks, which |
are pixel-level labels, for 7 land-cover classes: Background, |
Building, Road, Water, Barren, Forest, and Agricultural. |
The Background class consists of any pixel not belonging |
to the other classes. Statistics of the dataset, given in Figure |
3 in [52], show that the pixel counts across the 7 classes are |
imbalanced. Further, distribution of classes and of building |
scales differ between urban and rural scenes. Thus the rural |
and urban groups of images, which we use in our fairness |
analysis, have different characteristics. |
4. Methods |
We study three tasks, namely semantic segmentation |
within the same domain, across districts, and across rural- |
urban areas. Next, we describe the setup for each task. |
2918 |
4.1. Task A - Semantic segmentation |
Our task is to train multi-class semantic segmentation |
models for detecting the 7 land-cover classes from a given |
image. Sample images and predicted segmentation maps |
are shown in Figure 1. Same as the models studied in |
the LoveDA study [52], we use two commonly used deep |
learning-based segmentation methods – U-Net [44] and |
DeepLabV3+ [7] network, both with pre-trained ResNet50 |
[17] as the backbone model for the encoder [13]. |
4.1.1 Training-testing details |
Images from the 12 districts with labeled data are shuffled |
and split into training ( ≈80%) and testing sets ( ≈20%). |
Training set has 3148 images with a mix of 1377 urban and |
1771 rural images, and the rest of the images comprise the |
testing set with a mix of 368 urban images and 473 rural |
images. Images are augmented during training by mirroring |
and rotation. Dimension of the input image to the network is |
512×512×3 where 3 indicates the RGB bands. The output |
dimension of the network is 512 ×512×7, where 7 repre- |
sents the probability of each pixel belonging to each land- |
cover class. We use cross-entropy (CE) loss, and stochastic |
gradient descent (SGD) as the optimizer with a momentum |
of 0.9 and a weight decay of 10−4. The batch size is set to |
16 and the total training iterations are 15000, during which |
the learning rate is decayed using a polynomial learning rate |
scheduler implemented in PyTorch [40]. |
4.1.2 Evaluation metrics |
We test the models on either the whole test set or the ur- |
ban and rural subsets in the test set separately, and evaluate |
model performance using the following metrics: |
Accuracy metrics: Intersection-over-Union (IoU), also |
called Jaccard index, is used to measure segmentation ac- |
curacy which is a common method to evaluate the quality |
of image segmentation [11, 54]. IoU for a class is defined |
as the intersection of class-wise ground-truth masks and the |
predicted segmentation divided by their union, |
IoU :=TP |
TP+FP+FN, |
where TP,FP, andFN are pixel-wise true positives, false |
positives, and false negatives. We report IoU score of the |
model on each land-cover class as well as mean over class- |
wise IoU (referred to as Mean ) over the 7 classes. |
Fairness metrics: Besides looking at IoU on the rural |
and urban subsets and comparing the two values, we de- |
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