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Accurate local-level poverty measurement is an essential task for governments and humanitarian organizations to track the progress towards improving livelihoods and distribute scarce resources.
Estimating wealth effects without expenditure data—or tears: an application to educational enrollments in states of india
Deon Filmer and Lant H Pritchett · 2001
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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A large-scale hierarchical multi-view rgb-d object dataset
Kevin Lai, Liefeng Bo, Xiaofeng Ren, and Dieter Fox · 2011
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Uganda national panel survey 2011/2012
Uganda Bureau of Statistics · 2012
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Sustainable development goals
United Nations · 2015
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Predicting poverty and wealth from mobile phone metadata
Joshua Blumenstock, Gabriel Cadamuro, and Robert On · 2015
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Combining satellite imagery and machine learning to predict poverty
Neal Jean, Marshall Burke, Michael Xie, W Matthew Davis, David B Lobell, and Stefano Ermon · 2016
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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
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Beyond skip connections: Top-down modulation for object detection
Abhinav Shrivastava, Rahul Sukthankar, Jitendra Malik, and Abhinav Gupta · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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The view from above: Applications of satellite data in economics
Dave Donaldson and Adam Storeygard · 2016
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How much will a data revolution in development cost?
Morten Jerven · 2017
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Poverty prediction with public landsat 7 satellite imagery and machine learning
Anthony Perez, Christopher Yeh, George Azzari, Marshall Burke, David Lobell, and Stefano Ermon · 2017
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Using deep learning and google street view to estimate the demographic makeup of neighborhoods across the united states
Timnit Gebru, Jonathan Krause, Yilun Wang, Duyun Chen, Jia Deng, Erez Lieberman Aiden, and Li Fei-Fei · 2017
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Poverty from space: Using high-resolution satellite imagery for estimating economic well-being, 2017
Ryan Engstrom, Jonathan Hersh, and David Newhouse · 2017
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xview: Objects in context in overhead imagery
Darius Lam, Richard Kuzma, Kevin McGee, Samuel Dooley, Michael Laielli, Matthew Klaric, Yaroslav Bulatov, and Brendan McCord · 2018
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Tracking in aerial hyperspectral videos using deep kernelized correlation filters
Burak Uzkent, Aneesh Rangnekar, and Matthew J Hoffman · 2018
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Yolov3: An incremental improvement
Joseph Redmon and Ali Farhadi · 2018
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Consistent individualized feature attribution for tree ensembles
Scott M Lundberg, Gabriel G Erion, and Su-In Lee · 2018
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Predicting economic development using geolocated wikipedia articles
Evan Sheehan, Chenlin Meng, Matthew Tan, Burak Uzkent, Neal Jean, Marshall Burke, David Lobell, and Stefano Ermon · 2019
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Cheng-Yang Fu, Wei Liu, Ananth Ranga, Ambrish Tyagi, and Alexander C Berg · 2017
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Focal loss for dense object detection
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár · 2017
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Feature pyramid networks for object detection
Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie · 2017
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Towards a rigorous science of interpretable machine learning
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Definitions, methods, and applications in interpretable machine learning
W James Murdoch, Chandan Singh, Karl Kumbier, Reza Abbasi-Asl, and Bin Yu · 2019
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Learning to interpret satellite images using wikipedia
Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang2 Marshall Burke, David Lobell, and Stefano Ermon · 2019
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Learning to interpret satellite images in global scale using wikipedia
Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang, Marshall Burke, David Lobell, and Stefano Ermon · 2019
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Efficient object detection in large images using deep reinforcement learning
Burak Uzkent, Christopher Yeh, and Stefano Ermon · 2019
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