123 citations · 529 across the 19 of their papers we have counts for
21 papers
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery
Yezhen Cong, Samar Khanna, Chenlin Meng +6
Unsupervised pre-training methods for large vision models have shown to enhance performance on downstream supervised tasks. Developing similar techniques for satellite imagery pres…
Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution
Yutong He, William Zhang, Chenlin Meng +3
Automated tracking of urban development in areas where construction information is not available became possible with recent advancements in machine learning and remote sensing. Un…
IS-COUNT: Large-scale Object Counting from Satellite Images with Covariate-based Importance Sampling
Chenlin Meng, Enci Liu, Willie Neiswanger +4
Object detection in high-resolution satellite imagery is emerging as a scalable alternative to on-the-ground survey data collection in many environmental and socioeconomic monitori…
Using Satellite Imagery and Machine Learning to Estimate the Livelihood Impact of Electricity Access
Nathan Ratledge, Gabe Cadamuro, Brandon de la Cuesta +2
In many regions of the world, sparse data on key economic outcomes inhibits the development, targeting, and evaluation of public policy. We demonstrate how advancements in satellit…
Wildfire smoke plume segmentation using geostationary satellite imagery
Jeff Wen, Marshall Burke
Wildfires have increased in frequency and severity over the past two decades, especially in the Western United States. Beyond physical infrastructure damage caused by these wildfir…
Using Localized Twitter Activity for Red Tide Impact Assessment
A. Skripnikov, N. Wagner, J. Shafer +3
Red tide blooms of the dinoflagellate Karenia brevis (K. brevis) produce toxic coastal conditions that can impact marine organisms and human health, while also affecting local econ…
Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis
Yutong He, Dingjie Wang, Nicholas Lai +5
High-resolution satellite imagery has proven useful for a broad range of tasks, including measurement of global human population, local economic livelihoods, and biodiversity, amon…
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…
Using satellite imagery to understand and promote sustainable development
Marshall Burke, Anne Driscoll, David B. Lobell +1
Accurate and comprehensive measurements of a range of sustainable development outcomes are fundamental inputs into both research and policy. We synthesize the growing literature th…
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…
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…
Predicting Economic Development using Geolocated Wikipedia Articles
Evan Sheehan, Chenlin Meng, Matthew Tan +5
Progress on the UN Sustainable Development Goals (SDGs) is hampered by a persistent lack of data regarding key social, environmental, and economic indicators, particularly in devel…
Mapping Missing Population in Rural India: A Deep Learning Approach with Satellite Imagery
Wenjie Hu, Jay Harshadbhai Patel, Zoe-Alanah Robert +6
Millions of people worldwide are absent from their country's census. Accurate, current, and granular population metrics are critical to improving government allocation of resources…
Learning to Interpret Satellite Images in Global Scale Using Wikipedia
Burak Uzkent, Evan Sheehan, Chenlin Meng +4
Despite recent progress in computer vision, finegrained interpretation of satellite images remains challenging because of a lack of labeled training data. To overcome this limitati…
Semi-Supervised Multitask Learning on Multispectral Satellite Images Using Wasserstein Generative Adversarial Networks (GANs) for Predicting Poverty
Anthony Perez, Swetava Ganguli, Stefano Ermon +3
Obtaining reliable data describing local poverty metrics at a granularity that is informative to policy-makers requires expensive and logistically difficult surveys, particularly i…
Learning to Interpret Satellite Images Using Wikipedia
Evan Sheehan, Burak Uzkent, Chenlin Meng +4
Despite recent progress in computer vision, fine-grained interpretation of satellite images remains challenging because of a lack of labeled training data. To overcome this limitat…
Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learning
Barak Oshri, Annie Hu, Peter Adelson +6
The UN Sustainable Development Goals allude to the importance of infrastructure quality in three of its seventeen goals. However, monitoring infrastructure quality in developing re…
Poverty Prediction with Public Landsat 7 Satellite Imagery and Machine Learning
Anthony Perez, Christopher Yeh, George Azzari +3
Obtaining detailed and reliable data about local economic livelihoods in developing countries is expensive, and data are consequently scarce. Previous work has shown that it is pos…
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping
Michael Xie, Neal Jean, Marshall Burke +2
The lack of reliable data in developing countries is a major obstacle to sustainable development, food security, and disaster relief. Poverty data, for example, is typically scarce…