- Weakly Supervised Deep Learning for Segmentation of Remote Sensing Imagery REMOTE SENSING 2020; 12 (2)
Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018
View details for Web of Science ID 000461823305035
Combining satellite imagery and machine learning to predict poverty.
2016; 353 (6301): 790-794
Reliable data on economic livelihoods remain scarce in the developing world, hampering efforts to study these outcomes and to design policies that improve them. Here we demonstrate an accurate, inexpensive, and scalable method for estimating consumption expenditure and asset wealth from high-resolution satellite imagery. Using survey and satellite data from five African countries--Nigeria, Tanzania, Uganda, Malawi, and Rwanda--we show how a convolutional neural network can be trained to identify image features that can explain up to 75% of the variation in local-level economic outcomes. Our method, which requires only publicly available data, could transform efforts to track and target poverty in developing countries. It also demonstrates how powerful machine learning techniques can be applied in a setting with limited training data, suggesting broad potential application across many scientific domains.
View details for DOI 10.1126/science.aaf7894
View details for PubMedID 27540167
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping
ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE. 2016: 3929–35
View details for Web of Science ID 000485474203133