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HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing
Jonathan Xu, Amna Elmustafa, Liya Weldegebriel +5
Small farms contribute to a large share of the productive land in developing countries. In regions such as sub-Saharan Africa, where 80\% of farms are small (under 2 ha in size), t…
Building Coverage Estimation with Low-resolution Remote Sensing Imagery
Enci Liu, Chenlin Meng, Matthew Kolodner +5
Building coverage statistics provide crucial insights into the urbanization, infrastructure, and poverty level of a region, facilitating efforts towards alleviating poverty, buildi…
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…
Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision
Sherrie Wang, Francois Waldner, David B. Lobell
Crop field boundaries aid in mapping crop types, predicting yields, and delivering field-scale analytics to farmers. Recent years have seen the successful application of deep learn…
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…
Early- and in-season crop type mapping without current-year ground truth: generating labels from historical information via a topology-based approach
Chenxi Lin, Liheng Zhong, Xiao-Peng Song +3
Land cover classification in remote sensing is often faced with the challenge of limited ground truth. Incorporating historical information has the potential to significantly lower…
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…
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…
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…
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…
Tile2Vec: Unsupervised representation learning for spatially distributed data
Neal Jean, Sherrie Wang, Anshul Samar +3
Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and com…
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…