  This paper proposes to employ a Inception-ResNet inspired deep learning
architecture called Tiny-Inception-ResNet-v2 to eliminate bonded labor by
identifying brick kilns within "Brick-Kiln-Belt" of South Asia. The framework
is developed by training a network on the satellite imagery consisting of 11
different classes of South Asian region. The dataset developed during the
process includes the geo-referenced images of brick kilns, houses, roads,
tennis courts, farms, sparse trees, dense trees, orchards, parking lots, parks
and barren lands. The dataset is made publicly available for further research.
Our proposed network architecture with very fewer learning parameters
outperforms all state-of-the-art architectures employed for recognition of
brick kilns. Our proposed solution would enable regional monitoring and
evaluation mechanisms for the Sustainable Development Goals.
