Combining Residual Neural Networks and Feature Pyramid Networks to Estimate Poverty Using Multisource Remote Sensing Data

Reliable poverty data are critical for regional economic analysis and policy making, especially considering that economic inequality and sustainable development are widespread social concerns. This article proposes a multitask learning model combining deep residual neural networks and feature pyramid networks to estimate poverty level from multiple sources including the night-time light data, Landsat 8 imagery, and spectral index data. We first train the multitask learning model using the multisource data in Chongqing, China and then estimate the representative economic indicators in the study area. The model is evaluated with the Pearson correlation coefficient of the actual and estimated economic indicators. The result shows that the proposed model outperforms other models with the Pearson correlation coefficient up to 0.87 in the annual estimates of economic indicators between 2013 and 2017. As all the data used in this article are publicly available, the proposed model can be used to estimate the economic indicators in other regions as well.