55 citations · 152 across the 18 of their papers we have counts for
21 papers
Benchmarking Generative Models for Near-Surface Data Assimilation on Real Station Observations
Ruizhe Huang, Qidong Yang, Jonathan Giezendanner +1
Weather reanalysis products rely on computationally intensive numerical weather predictions followed by data assimilation that corrects the forecast toward observations. Recent adv…
SolarBench: A global solar energy nowcasting benchmark
Yuhao Nie, Stephen Campbell, Quentin Paletta +18
As the share of solar power grows, nowcasting weather-driven solar variability becomes critical for reliable energy system operation. State-of-the-art approaches increasingly apply…
Partial recovery of meter-scale surface weather
Jonathan Giezendanner, Qidong Yang, Eric Schmitt +9
Near-surface weather varies over tens to hundreds of meters, yet remains unresolved in analyses and forecasts. We test whether this variation can be inferred without resolving atmo…
Conformal Prediction Sets for Instance Segmentation
Kerri Lu, Dan M. Kluger, Stephen Bates +1
Current instance segmentation models achieve high performance on average predictions, but lack principled uncertainty quantification: their outputs are not calibrated, and there is…
Conformal Prediction for Generative Models via Adaptive Cluster-Based Density Estimation
Qidong Yang, Qianyu Julie Zhu, Jonathan Giezendanner +3
Conditional generative models map input variables to complex, high-dimensional distributions, enabling realistic sample generation in a diverse set of domains. A critical challenge…
Observation-driven correction of numerical weather prediction for marine winds
Matteo Peduto, Qidong Yang, Jonathan Giezendanner +2
Accurate marine wind forecasts are essential for safe navigation, ship routing, and energy operations, yet they remain challenging because observations over the ocean are sparse, h…
Masked Mineral Modeling: Continent-Scale Mineral Prospecting via Geospatial Infilling
Sujay Nair, Evan Coleman, Sherrie Wang +1
Minerals play a critical role in the advanced energy technologies necessary for decarbonization, but characterizing mineral deposits hidden underground remains costly and challengi…
Invariant Features for Global Crop Type Classification
Xin-Yi Tong, Sherrie Wang
Accurate global crop type mapping supports agricultural monitoring and food security, yet remains limited by the scarcity of labeled data in many regions. A key challenge is enabli…
Prediction-Powered Inference with Imputed Covariates and Nonuniform Sampling
Dan M. Kluger, Kerri Lu, Tijana Zrnic +2
Machine learning models are increasingly used to produce predictions that serve as input data in subsequent statistical analyses. For example, computer vision predictions of econom…
Local Off-Grid Weather Forecasting with Multi-Modal Earth Observation Data
Qidong Yang, Jonathan Giezendanner, Daniel Salles Civitarese +8
Urgent applications like wildfire management and renewable energy generation require precise, localized weather forecasts near the Earth's surface. However, forecasts produced by m…
Regression coefficient estimation from remote sensing maps
Kerri Lu, Dan M. Kluger, Stephen Bates +1
Regressions are commonly used in environmental science and economics to identify causal or associative relationships between variables. In these settings, remote sensing-derived ma…
Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data
Chenhui Zhang, Sherrie Wang
Large Vision-Language Models (VLMs) have demonstrated impressive performance on complex tasks involving visual input with natural language instructions. However, it remains unclear…
Taking it further: leveraging pseudo labels for field delineation across label-scarce smallholder regions
Philippe Rufin, Sherrie Wang, Sá Nogueira Lisboa +3
Transfer learning allows for resource-efficient geographic transfer of pre-trained field delineation models. However, the scarcity of labeled data for complex and dynamic smallhold…
Combining Deep Learning and Street View Imagery to Map Smallholder Crop Types
Jordi Laguarta Soler, Thomas Friedel, Sherrie Wang
Accurate crop type maps are an essential source of information for monitoring yield progress at scale, projecting global crop production, and planning effective policies. To date,…
Annual field-scale maps of tall and short crops at the global scale using GEDI and Sentinel-2
Stefania Di Tommaso, Sherrie Wang, Vivek Vajipey +3
Crop type maps are critical for tracking agricultural land use and estimating crop production. Remote sensing has proven an efficient and reliable tool for creating these maps in r…
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…
SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning
Christopher Yeh, Chenlin Meng, Sherrie Wang +7
Progress toward the United Nations Sustainable Development Goals (SDGs) has been hindered by a lack of data on key environmental and socioeconomic indicators, which historically ha…
Combining GEDI and Sentinel-2 for wall-to-wall mapping of tall and short crops
Stefania Di Tommaso, Sherrie Wang, David B. Lobell
High resolution crop type maps are an important tool for improving food security, and remote sensing is increasingly used to create such maps in regions that possess ground truth l…
Two Shifts for Crop Mapping: Leveraging Aggregate Crop Statistics to Improve Satellite-based Maps in New Regions
Dan M. Kluger, Sherrie Wang, David B. Lobell
Crop type mapping at the field level is critical for a variety of applications in agricultural monitoring, and satellite imagery is becoming an increasingly abundant and useful raw…
Meta-Learning for Few-Shot Land Cover Classification
Marc Rußwurm, Sherrie Wang, Marco Körner +1
The representations of the Earth's surface vary from one geographic region to another. For instance, the appearance of urban areas differs between continents, and seasonality influ…
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…