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Mapping the world population one building at a time
Obtaining high accuracy in estimation of population distribution in rural areas remains a very challenging task due to the simultaneous requirements of sufficient sensitivity and resolution to detect very sparse populations through remote sensing as well as reliable performance at a global scale.
Building Detection from Satellite Images on a Global Scale
Our work is some of the first to create population density maps from building detection on a large scale.
Feedback Neural Network for Weakly Supervised Geo-Semantic Segmentation
Learning from weakly-supervised data is one of the main challenges in machine learning and computer vision, especially for tasks such as image semantic segmentation where labeling is extremely expensive and subjective.
Sentiment in New York City: A High Resolution Spatial and Temporal View
Measuring public sentiment is a key task for researchers and policymakers alike.
