Gated-Attention Feature-Fusion Based Framework for Poverty Prediction
This provides a more accurate tool for poverty mapping in developing regions, though it is incremental as it builds on existing ResNet50 models.
The paper tackled the challenge of accurately estimating poverty levels in developing regions by proposing a CNN architecture with a Gated-Attention Feature-Fusion Module, achieving a 75% R2 score and outperforming existing methods.
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in developing regions where traditional methods like household surveys are often costly, infrequent, and quickly become outdated.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.