Tiny-Inception-ResNet-v2: Using Deep Learning for Eliminating Bonded Labors of Brick Kilns in South Asia

TL;DR

This paper introduces Tiny-Inception-ResNet-v2, a deep learning model that accurately identifies brick kilns in satellite images to help eliminate bonded labor in South Asia.

Contribution

The paper presents a novel lightweight deep learning architecture tailored for brick kiln recognition, outperforming existing models with fewer parameters.

Findings

Outperforms state-of-the-art architectures in recognition accuracy.

Develops a publicly available geo-referenced dataset of South Asian regions.

Enables regional monitoring for sustainable development goals.

Abstract

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.