  One billion people worldwide are estimated to be living in slums, and
documenting and analyzing these regions is a challenging task. As compared to
regular slums; the small, scattered and temporary nature of temporary slums
makes data collection and labeling tedious and time-consuming. To tackle this
challenging problem of temporary slums detection, we present a semi-supervised
deep learning segmentation-based approach; with the strategy to detect initial
seed images in the zero-labeled data settings. A small set of seed samples (32
in our case) are automatically discovered by analyzing the temporal changes,
which are manually labeled to train a segmentation and representation learning
module. The segmentation module gathers high dimensional image representations,
and the representation learning module transforms image representations into
embedding vectors. After that, a scoring module uses the embedding vectors to
sample images from a large pool of unlabeled images and generates pseudo-labels
for the sampled images. These sampled images with their pseudo-labels are added
to the training set to update the segmentation and representation learning
modules iteratively. To analyze the effectiveness of our technique, we
construct a large geographically marked dataset of temporary slums. This
dataset constitutes more than 200 potential temporary slum locations (2.28
square kilometers) found by sieving sixty-eight thousand images from 12
metropolitan cities of Pakistan covering 8000 square kilometers. Furthermore,
our proposed method outperforms several competitive semi-supervised semantic
segmentation baselines on a similar setting. The code and the dataset will be
made publicly available.
