Combining satellite imagery and machine learning to predict poverty

Slides:



Advertisements
Similar presentations
Figure B1. Income levels associated with poverty lines. Note. Vertical bares indicate bias-corrected Hall confidence intervals. Data. German Sample Survey.
Advertisements

Analyzing satellite data in Stata
Michael Xie, Neal Jean, Stefano Ermon
Key Terms Symbology Categorical attributes Style Layer file.
From: Motion processing with two eyes in three dimensions
Volume 10, Issue 6, Pages (June 2018)
The Duffy-null state is associated with a survival advantage in leukopenic HIV-infected persons of African ancestry by Hemant Kulkarni, Vincent C. Marconi,
Volume 60, Issue 5, Pages (December 2008)
One-Dimensional Dynamics of Attention and Decision Making in LIP
Modulation of Neuronal Interactions Through Neuronal Synchronization
Kenway Louie, Matthew A. Wilson  Neuron 
Machine Learning Techniques for Predicting Crop Photosynthetic Capacity from Leaf Reflectance Spectra  David Heckmann, Urte Schlüter, Andreas P.M. Weber 
by Daniela Vallentin, Georg Kosche, Dina Lipkind, and Michael A. Long
Retinal Representation of the Elementary Visual Signal
Integration of omic networks in a developmental atlas of maize
Marshall Burke, PhD, Sam Heft-Neal, PhD, Dr Eran Bendavid, MD 
Ecological selectivity of the emerging mass extinction in the oceans
Vincent B. McGinty, Antonio Rangel, William T. Newsome  Neuron 
Hongbo Yu, Brandon J. Farley, Dezhe Z. Jin, Mriganka Sur  Neuron 
Volume 111, Issue 2, Pages (July 2016)
Hedging Your Bets by Learning Reward Correlations in the Human Brain
CA3 Retrieves Coherent Representations from Degraded Input: Direct Evidence for CA3 Pattern Completion and Dentate Gyrus Pattern Separation  Joshua P.
A Switching Observer for Human Perceptual Estimation
by A. Dutton, A. E. Carlson, A. J. Long, G. A. Milne, P. U. Clark, R
Yang Liu, Perry Palmedo, Qing Ye, Bonnie Berger, Jian Peng 
Volume 36, Issue 4, Pages (November 2002)
Target-Specific Precision of CRISPR-Mediated Genome Editing
Cortical Mechanisms of Smooth Eye Movements Revealed by Dynamic Covariations of Neural and Behavioral Responses  David Schoppik, Katherine I. Nagel, Stephen.
James A. Strother, Aljoscha Nern, Michael B. Reiser  Current Biology 
Neural Correlates of Visual Working Memory
University of Washington, Autumn 2018
Fig. 2. Best model fits. Best model fits. Illustration of the best model fits for the (A) basic, (B) continuous, and (C) cluster models. See Table 1 and.
Dynamic Coding for Cognitive Control in Prefrontal Cortex
Topographical effects of cell line, mitotic phase, and inhibitors
Predicting poverty and wealth from mobile phone metadata
Singular Behavior of Slow Dynamics of Single Excitable Cells
Sensory Population Decoding for Visually Guided Movements
The Spatiotemporal Organization of the Striatum Encodes Action Space
Peter A. Savage, Mark M. Davis  Immunity 
A Switching Observer for Human Perceptual Estimation
Benjamin Scholl, Daniel E. Wilson, David Fitzpatrick  Neuron 
Origin and Function of Tuning Diversity in Macaque Visual Cortex
A Scalable Population Code for Time in the Striatum
Volume 89, Issue 6, Pages (March 2016)
Fig. 3. H3N2 incidence forecasts based on the cluster model for the Unites States. H3N2 incidence forecasts based on the cluster model for the Unites States.
by Asaf Inbal, Jean Paul Ampuero, and Robert W. Clayton
Xiaomo Chen, Marc Zirnsak, Tirin Moore  Cell Reports 
Volume 76, Issue 4, Pages (November 2012)
by Benjamin Vernot, Serena Tucci, Janet Kelso, Joshua G
Benjamin Scholl, Daniel E. Wilson, David Fitzpatrick  Neuron 
Ethan S. Bromberg-Martin, Okihide Hikosaka  Neuron 
Encoding of Stimulus Probability in Macaque Inferior Temporal Cortex
Tuning to Natural Stimulus Dynamics in Primary Auditory Cortex
by Khaled Nasr, Pooja Viswanathan, and Andreas Nieder
Volume 24, Issue 8, Pages e6 (August 2018)
Visual selection: Neurons that make up their minds
Source-based CP BCI robotic arm control.
Prediction of income with the Big Five PTs and cognitive ability
Albert K. Lee, Matthew A. Wilson  Neuron 
Modeling Endoplasmic Reticulum Network Maintenance in a Plant Cell
Fig. 4 Visualization of the 20 occipital lobe models, trained to predict EmoNet categories from brain responses to emotional images. Visualization of the.
Feature computation and classification of grating pitch.
Reward associations do not explain transitive inference performance in monkeys by Greg Jensen, Yelda Alkan, Vincent P. Ferrera, and Herbert S. Terrace.
Fig. 3 ET dynamics on the control and treatment watersheds during the pretreatment and treatment periods. ET dynamics on the control and treatment watersheds.
Valerio Mante, Vincent Bonin, Matteo Carandini  Neuron 
Fig. 2 Visualization of features.
Relationships between species richness and temperature or latitude
Urban scaling and the regional divide
Evolution-informed forecasting of seasonal influenza A (H3N2)‏
Correlation between journal impact factor and percentage of papers with image duplication. Correlation between journal impact factor and percentage of.
Presentation transcript:

Combining satellite imagery and machine learning to predict poverty by Neal Jean, Marshall Burke, Michael Xie, W. Matthew Davis, David B. Lobell, and Stefano Ermon Science Volume 353(6301):790-794 August 19, 2016 Published by AAAS

Fig. 1 Poverty data gaps. Poverty data gaps. (A) Number of nationally representative consumption surveys occurring in each African country between 2000 and 2010. (B) Same as (A), for DHS surveys measuring assets. (C to F) Relationship between per capita consumption expenditure (measured in U.S. dollars) and nightlight intensity at the cluster level for four African countries, based on household surveys. Nationally representative share of households at each point in the consumption distribution is shown beneath each panel in gray. Vertical red lines show the official international extreme poverty line ($1.90 per person per day), and black lines are fits to the data with corresponding 95% confidence intervals in light blue. Neal Jean et al. Science 2016;353:790-794 Published by AAAS

Fig. 2 Visualization of features. Visualization of features. By column: Four different convolutional filters (which identify, from left to right, features corresponding to urban areas, nonurban areas, water, and roads) in the convolutional neural network model used for extracting features. Each filter “highlights” the parts of the image that activate it, shown in pink. By row: Original daytime satellite images from Google Static Maps, filter activation maps, and overlay of activation maps onto original images Neal Jean et al. Science 2016;353:790-794 Published by AAAS

Fig. 3 Predicted cluster-level consumption from transfer learning approach (y axis) compared to survey-measured consumption (x axis). Predicted cluster-level consumption from transfer learning approach (y axis) compared to survey-measured consumption (x axis). Results are shown for Nigeria (A), Tanzania (B), Uganda (C), and Malawi (D). Predictions and reported r2 values in each panel are from five-fold cross-validation. Black line is the best fit line, and red line is international poverty line of $1.90 per person per day. Both axes are shown in logarithmic scale. Countries are ordered by population size. Neal Jean et al. Science 2016;353:790-794 Published by AAAS

Fig. 4 Evaluation of model performance. Evaluation of model performance. (A) Performance of transfer learning model relative to nightlights for estimating consumption, using pooled observations across the four LSMS countries. Trials were run separately for increasing percentages of the available clusters (e.g., x-axis value of 40 indicates that all clusters below 40th percentile in consumption were included). Vertical red lines indicate various multiples of the international poverty line. Image features reduced to 100 dimensions using principal component analysis. (B) Same as (A), but for assets. (C) Comparison of r2 of models trained on correctly assigned images in each country (vertical lines) to the distribution of r2 values obtained from trials in which the model was trained on randomly shuffled images (1000 trials per country). (D) Same as (C), but for assets. Cross-validated r2 values are reported in all panels. Neal Jean et al. Science 2016;353:790-794 Published by AAAS

Fig. 5 Cross-border model generalization. Cross-border model generalization. (A) Cross-validated r2 values for consumption predictions for models trained in one country and applied in other countries. Countries on x axis indicate where model was trained, countries on y axis where model was evaluated. Reported r2 values are averaged over 100 folds (10 trials, 10 folds each). (B) Same as in (A), but for assets. Neal Jean et al. Science 2016;353:790-794 Published by AAAS