17 citations · 60 across the 18 of their papers we have counts for
7 papers · 1 filter
Efficient Conditional Pre-training for Transfer Learning
Shuvam Chakraborty, Burak Uzkent, Kumar Ayush +3
Almost all the state-of-the-art neural networks for computer vision tasks are trained by (1) pre-training on a large-scale dataset and (2) finetuning on the target dataset. This st…
Geography-Aware Self-Supervised Learning
Kumar Ayush, Burak Uzkent, Chenlin Meng +4
Contrastive learning methods have significantly narrowed the gap between supervised and unsupervised learning on computer vision tasks. In this paper, we explore their application…
Predicting Livelihood Indicators from Community-Generated Street-Level Imagery
Jihyeon Lee, Dylan Grosz, Burak Uzkent +4
Major decisions from governments and other large organizations rely on measurements of the populace's well-being, but making such measurements at a broad scale is expensive and thu…
Efficient Poverty Mapping using Deep Reinforcement Learning
Kumar Ayush, Burak Uzkent, Kumar Tanmay +3
The combination of high-resolution satellite imagery and machine learning have proven useful in many sustainability-related tasks, including poverty prediction, infrastructure meas…
Farmland Parcel Delineation Using Spatio-temporal Convolutional Networks
Han Lin Aung, Burak Uzkent, Marshall Burke +2
Farm parcel delineation provides cadastral data that is important in developing and managing climate change policies. Specifically, farm parcel delineation informs applications in…
Learning When and Where to Zoom with Deep Reinforcement Learning
Burak Uzkent, Stefano Ermon
While high resolution images contain semantically more useful information than their lower resolution counterparts, processing them is computationally more expensive, and in some a…
Generating Interpretable Poverty Maps using Object Detection in Satellite Images
Kumar Ayush, Burak Uzkent, Marshall Burke +2
Accurate local-level poverty measurement is an essential task for governments and humanitarian organizations to track the progress towards improving livelihoods and distribute scar…