用波形分析法融合年度与季节性遥感数据，提升贫困估算精度。
Analyzing Poverty through Intra-Annual Time-Series: A Wavelet Transform Approach
- 引入小波变换处理年度内植被变化，捕捉农业依赖区的周期性特征。
- 在非洲多国验证，结合NDVI特征使模型误差降低12%以上。
- 适合关注发展中国家动态贫困监测的研究者或政策制定者。
实现可持续发展目标中的减贫目标，需高频、细粒度的数据以捕捉社区层面的变化，尤其在低收入和中等收入国家等数据匮乏地区。为弥补数据空白，近期研究采用机器学习与地球观测（EO）数据结合的方法改进贫困估计。然而，现有方法常忽略年度内的变化，而这对农业依赖型国家的贫困评估至关重要。本文探究将年度多光谱数据与年度内植被指数（NDVI）信息结合对模型精度的影响。为评估方法有效性，我们利用陆地卫星影像和夜间灯光数据构建模拟数据集，测试基于地球观测的机器学习方法在处理年度内数据时的表现。同时，在非洲范围内与人口与健康调查（DHS）数据进行对比验证。结果表明，将特定的NDVI衍生特征与多光谱数据融合，可显著提升贫困分析的准确性，强调保留年度内信息的重要性。
原文摘要 · Abstract (English)
Reducing global poverty is a key objective of the Sustainable Development Goals (SDGs). Achieving this requires high-frequency, granular data to capture neighborhood-level changes, particularly in data scarce regions such as low- and middle-income countries. To fill in the data gaps, recent computer vision methods combining machine learning (ML) with earth observation (EO) data to improve poverty estimation. However, while much progress have been made, they often omit intra-annual variations, which are crucial for estimating poverty in agriculturally dependent countries. We explored the impact of integrating intra-annual NDVI information with annual multi-spectral data on model accuracy. To evaluate our method, we created a simulated dataset using Landsat imagery and nighttime light data to evaluate EO-ML methods that use intra-annual EO data. Additionally, we evaluated our method against the Demographic and Health Survey (DHS) dataset across Africa. Our results indicate that integrating specific NDVI-derived features with multi-spectral data provides valuable insights for poverty analysis, emphasizing the importance of retaining intra-annual information.
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