Using GIS and Machine Learning to Classify Residential Status of Urban Buildings in Low and Middle Income Settings

Comparisons of two global built area land cover datasets in methods to disaggregate human population in eleven countries from the global South

(2019) Forrest R. Stevens et al.   International Journal of Digital Earth

Recognition of building group patterns in topographic maps based on graph partitioning and random forest

(2018) Xianjin He et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

Spatially disaggregated population estimates in the absence of national population and housing census data

(2018) N. A. Wardrop et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Predicting residential structures from open source remotely enumerated data using machine learning

(2018) Hugh J. W. Sturrock et al.   PLoS One

Classification of Urban Building Type from High Spatial Resolution Remote Sensing Imagery Using Extended MRS and Soft BP Network

(2017) Junfei Xie et al.   IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Examining the correlates and drivers of human population distributions across low- and middle-income countries

(2017) Jeremiah J. Nieves et al.   Journal of the Royal Society Interface

High resolution global gridded data for use in population studies

(2017) Christopher T. Lloyd et al.   Scientific Data

Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing

(2017) Alemayehu Midekisa et al.   PLoS One

Building type classification using spatial and landscape attributes derived from LiDAR remote sensing data

(2014) Zhenyu Lu et al.   LANDSCAPE AND URBAN PLANNING

Assessment of the NASA–USGS Global Land Survey (GLS) datasets

(2013) Garik Gutman et al.   REMOTE SENSING OF ENVIRONMENT

pROC: an open-source package for R and S+ to analyze and compare ROC curves

(2011) Xavier Robin et al.   BMC BIOINFORMATICS