182 citations · 239 across the 12 of their papers we have counts for
13 papers
OAM-TCD: A globally diverse dataset of high-resolution tree cover maps
Josh Veitch-Michaelis, Andrew Cottam, Daniella Schweizer +5
Accurately quantifying tree cover is an important metric for ecosystem monitoring and for assessing progress in restored sites. Recent works have shown that deep learning-based seg…
GEO-Bench: Toward Foundation Models for Earth Monitoring
Alexandre Lacoste, Nils Lehmann, Pau Rodriguez +14
Recent progress in self-supervision has shown that pre-training large neural networks on vast amounts of unsupervised data can lead to substantial increases in generalization to do…
A Note on "Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms"
Jiachen T. Wang, Ruoxi Jia
Data valuation is a growing research field that studies the influence of individual data points for machine learning (ML) models. Data Shapley, inspired by cooperative game theory…
Data Debugging with Shapley Importance over End-to-End Machine Learning Pipelines
Bojan Karlaš, David Dao, Matteo Interlandi +4
Developing modern machine learning (ML) applications is data-centric, of which one fundamental challenge is to understand the influence of data quality to ML training -- "Which tra…
ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery
Gyri Reiersen, David Dao, Björn Lütjens +5
Forest biomass is a key influence for future climate, and the world urgently needs highly scalable financing schemes, such as carbon offsetting certifications, to protect and resto…
RumbleML: program the lakehouse with JSONiq
Ghislain Fourny, David Dao, Can Berker Cikis +2
Lakehouse systems have reached in the past few years unprecedented size and heterogeneity and have been embraced by many industry players. However, they are often difficult to use…