贫困威胁人类发展，尤其对发展中国家而言，消除贫困已成为联合国可持续发展目标（SDGs）中最重要目标之一。本研究通过时间序列多源地理空间数据与深度学习模型，探讨2016年至2019年中国减贫进展。减贫效率（PRE）通过2016年与2019年脱贫概率（即非贫困人口概率）的差异进行衡量。研究结果表明，中国各地区贫困概率总体呈下降趋势（PRE = 0.264），说明该时期减贫进展显著。胡焕庸线（胡线）呈现出东南与西北地区脱贫率分布不均的地理格局。2016年至2019年间，中国脱贫率重心向东北方向移动105.786公里，脱贫率标准差椭圆偏离胡线3度，表明高脱贫率区域在2016至2019年间更集中于胡线以东。研究结果表明，未来政府减贫政策应关注贫困地区基础设施建设，并适当提高贫困地区人口密度。本研究填补了多尺度减贫研究的空白，为政府减贫政策制定提供了有益启示。

Poverty threatens human development especially for developing countries, so ending poverty has become one of the most important United Nations Sustainable Development Goals (SDGs). This study aims to explore China’s progress in poverty reduction from 2016 to 2019 through time-series multi-source geospatial data and a deep learning model. The poverty reduction efficiency (PRE) is measured by the difference in the out-of-poverty rates (which measures the probability of being not poor) of 2016 and 2019. The study shows that the probability of poverty in all regions of China has shown an overall decreasing trend (PRE = 0.264), which indicates that the progress in poverty reduction during this period is significant. The Hu Huanyong Line (Hu Line) shows an uneven geographical pattern of out-of-poverty rate between Southeast and Northwest China. From 2016 to 2019, the centroid of China’s out-of-poverty rate moved 105.786 km to the northeast while the standard deviation ellipse of the out-of-poverty rate moved 3 degrees away from the Hu Line, indicating that the regions with high out-of-poverty rates are more concentrated on the east side of the Hu Line from 2016 to 2019. The results imply that the government’s future poverty reduction policies should pay attention to the infrastructure construction in poor areas and appropriately increase the population density in poor areas. This study fills the gap in the research on poverty reduction under multiple scales and provides useful implications for the government’s poverty reduction policy.