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

arXiv: https://arxiv.org/abs/1907.05552 · HTML: https://mlsanity.com/paper/1907.05552/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2019.

## Built on by (most cited first, 3 of 3)

- [Spatio-Temporal driven Attention Graph Neural Network with Block Adjacency matrix (STAG-NN-BA) for Remote Land-use Change Detection](https://mlsanity.com/paper/2303.14322/) (2023)
- [Space to Policy: Scalable Brick Kiln Detection and Automatic Compliance Monitoring with Geospatial Data](https://mlsanity.com/paper/2412.04065/) (2024)
- [Detecting Brick Kiln Infrastructure at Scale: Graph, Foundation, and Remote Sensing Models for Satellite Imagery Data](https://mlsanity.com/paper/2602.13350/) (2026)

## Abstract (arXiv)

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.