用卫星图预测贫困，融合注意力机制提升准确率
Gated-Attention Feature-Fusion Based Framework for Poverty Prediction
- 在ResNet50基础上加入门控注意力特征融合模块
- 在真实数据上达到75%的R2得分，优于现有方法
- 适合做遥感与贫困评估的研究者参考
本研究针对发展地区贫困水平估算难题，提出一种基于门控注意力特征融合模块（GAFM）的新型卷积神经网络架构，以改进深度学习模型对卫星图像中全局与局部特征的捕捉和融合能力。该方法通过聚焦关键特征并过滤冗余信息，显著提升贫困预测精度，在真实数据集上实现75%的决定系数（R²），明显优于现有领先方法。该模型为遥感与贫困评估提供了高效、可扩展的技术支持。
原文摘要 · Abstract (English)
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in developing regions where traditional methods like household surveys are often costly, infrequent, and quickly become outdated.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.
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