Gated-Attention Feature-Fusion Based Framework for Poverty Prediction

TL;DR

This paper introduces a novel CNN architecture with a Gated-Attention Feature-Fusion Module that enhances poverty prediction accuracy from satellite images, outperforming existing methods significantly.

Contribution

The paper presents a new CNN extension with GAFM for better feature integration, improving poverty estimation accuracy from satellite imagery.

Findings

Achieved 75% R2 score in poverty prediction

Outperformed existing methods significantly

Enhanced feature focus improves model performance

Abstract

This research paper addresses the significant challenge of accurately estimating poverty levels using deep learning, particularly in developing regions where traditional methods like household surveys are often costly, infrequent, and quickly become outdated. To address these issues, we propose a state-of-the-art Convolutional Neural Network (CNN) architecture, extending the ResNet50 model by incorporating a Gated-Attention Feature-Fusion Module (GAFM). Our architecture is designed to improve the model's ability to capture and combine both global and local features from satellite images, leading to more accurate poverty estimates. The model achieves a 75% R2 score, significantly outperforming existing leading methods in poverty mapping. This improvement is due to the model's capacity to focus on and refine the most relevant features, filtering out unnecessary data, which makes it a powerful tool for remote sensing and poverty estimation.