… We propose a two step approach to predict poverty in a rural region from satellite imagery.
First, we engineer a multi-task fully convolutional deep network for simultaneously predicting …

… evidence that multi-task learning improves generalization of machine learning models “by …
Combining satellite imagery and machine learning to predict poverty. Science, 2016. [22] …

… poverty estimation model development scenarios using machine learning and deep learning
… First, the model is constructed using multisource satellite imagery and geospatial point of …

… satellite imagery and machine learning to predict poverty. … multitask learning on
multispectral satellite images using … networks (gans) for predicting poverty. arXiv preprint arXiv:…

… In this work we apply a machine learning approach similar to the one developed by Jean et
al. [16] for predicting economic outcomes in 5 African countries from satellite imagery, to the …

… for predicting poverty in rural regions of India from satellite … Initially, they train a multi-task
fully convolution model to predict … source of drinking water – from satellite imagery. Using only …

… how they relate to the poverty, machine learning, and satellite imagery nexus. Our inclusion
… cover poverty/wealth prediction, using survey data as the basis for the ground truth poverty/…

… to the poverty, machine learning and satellite imagery nexus. Our … elements of explainable
machine learning (transparency, … Multi-task deep learning for predicting poverty from satellite …

… This work aims to use deep learning and transfer learning (ResNet50 and VGG16) to train
the model on satellite images … Multi-task deep learning for predicting poverty from satellite …

… a two-step approach for predicting poverty using multispectral satellite imagery. Initially, they
… Krishnan NC (2018) Multi-task deep learning for predicting poverty from satellite images. In: …