Sherrie Wang

We are hiring PhD students and postdocs to start in 2024-25! Please visit our lab website for more details.

I am PI of the Earth Intelligence Lab at MIT, where our research uses novel data and computational algorithms to monitor our planet and enable sustainable development. Our focus is on improving agricultural management and mitigating climate change, especially in low- or middle-income regions of the world. To this end, we frequently use satellite imagery, crowdsourced data, LiDAR, and other spatial data. Due to the scarcity of ground truth data in these regions and the noisiness of real-world data in general, our methodological work is geared toward developing machine learning methods that work well with these constraints.

Prior to MIT, I was a Ciriacy-Wantrup Postdoctoral Fellow at UC Berkeley, hosted by Solomon Hsiang and the Global Policy Lab. In 2021, I obtained my PhD in Computational and Mathematical Engineering from Stanford University, where I was advised by David Lobell and benefited from mentors at the Center on Food Security and the Environment and the Sustainability and AI Lab.

You can find a copy of my CV here.

news

May 11, 2024	I gave a keynote at the ICLR ML4RS Workshop. Our paper on benchmarking vision-language models on Earth observation data, led by PhD student Chenhui Zhang, received the Best Paper Award at the workshop!
Feb 15, 2024	Our lab’s work mapping crops in Thailand, led by PhD student Jordi Laguarta, was featured on MIT News and MIT’s front page!
Jan 25, 2024	We mapped, for the first time, which water bodies are regulated by the Clean Water Act in the US – the paper is now published in Science.

selected publications

Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data

Chenhui Zhang, and Sherrie Wang

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2024

Bib HTML

@article{zhang2024good,
  author = {Zhang, Chenhui and Wang, Sherrie},
  title = {Good at captioning, bad at counting: Benchmarking GPT-4V on Earth observation data},
  journal = {2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  year = {2024},
}

Combining deep learning and street view imagery to map smallholder crop types

Jordi Laguarta, Thomas Friedel, and Sherrie Wang

Proceedings of the AAAI Conference on Artificial Intelligence, 2024

Bib HTML

@article{laguarta2024combining,
  author = {Laguarta, Jordi and Friedel, Thomas and Wang, Sherrie},
  title = {Combining deep learning and street view imagery to map smallholder crop types},
  journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
  year = {2024},
}

Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates

Simon Greenhill, Hannah Druckenmiller*, Sherrie Wang*, and 6 more authors

Science, 2024

Bib HTML

@article{greenhill2024machine,
  author = {Greenhill, Simon and Druckenmiller*, Hannah and Wang*, Sherrie and Keiser, David and Girotto, Manuela and Moore, Jason and Yamaguchi, Nobuhiro and Todeschini, Alberto and Shapiro, Joseph},
  title = {Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates},
  journal = {Science},
  year = {2024},
}

Current benefits of wildfire smoke for yields in the US Midwest may dissipate by 2050

A. Patrick Behrer, and Sherrie Wang

Environmental Research Letters, 2024

Bib

@article{behrer2024current,
  author = {Behrer, A. Patrick and Wang, Sherrie},
  title = {Current benefits of wildfire smoke for yields in the {US Midwest} may dissipate by 2050},
  journal = {Environmental Research Letters},
  year = {2024},
}

Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision

Sherrie Wang, Francois Waldner, and David B. Lobell

Remote Sensing, 2022

Bib HTML

@article{wang2022unlocking,
  title = {Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision},
  author = {Wang, Sherrie and Waldner, Francois and Lobell, David B.},
  journal = {Remote Sensing},
  volume = {14},
  number = {22},
  year = {2022},
}

SustainBench: Benchmarks for monitoring the Sustainable Development Goals with machine learning

Christopher Yeh*, Chenlin Meng*, Sherrie Wang*, and 7 more authors

NeurIPS Datasets and Benchmarks Track, 2021

Bib HTML Website

@article{yeh2021sustainbench,
  title = {SustainBench: Benchmarks for monitoring the Sustainable Development Goals with machine learning},
  author = {Yeh*, Christopher and Meng*, Chenlin and Wang*, Sherrie and Driscoll, Anne and Rozi, Erik and Liu, Patrick and Lee, Jihyeon and Burke, Marshall and Lobell, David B. and Ermon, Stefano},
  journal = {NeurIPS Datasets and Benchmarks Track},
  year = {2021},
}

Combining GEDI and Sentinel-2 for wall-to-wall mapping of tall and short crops

Stefania Di Tommaso, Sherrie Wang, and David B. Lobell

Environmental Research Letters, 2021

Bib HTML Blog

@article{ditommaso2021combining,
  title = {Combining GEDI and Sentinel-2 for wall-to-wall mapping of tall and short crops},
  author = {Di Tommaso, Stefania and Wang, Sherrie and Lobell, David B.},
  journal = {Environmental Research Letters},
  volume = {16},
  number = {12},
  pages = {125002},
  year = {2021},
}

Mapping crop types in southeast India with smartphone crowdsourcing and deep learning

Sherrie Wang, Stefania Di Tommaso, Joey Faulkner, and 4 more authors

Remote Sensing, 2020

Bib HTML Blog

@article{wang2020mapping2,
  title = {Mapping crop types in southeast India with smartphone crowdsourcing and deep learning},
  author = {Wang, Sherrie and Di Tommaso, Stefania and Faulkner, Joey and Friedel, Thomas and Kennepohl, Alexander and Strey, Rob and Lobell, David B.},
  issn = {2072-4292},
  journal = {Remote Sensing},
  number = {18},
  volume = {12},
  year = {2020},
}

Meta-learning for few-shot land cover classification

Marc Rußwurm*, Sherrie Wang*, Marco Körner, and 1 more author

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020

Bib HTML

@article{russwurm2020meta,
  title = {Meta-learning for few-shot land cover classification},
  author = {Ru{\ss}wurm*, Marc and Wang*, Sherrie and K{\"o}rner, Marco and Lobell, David B.},
  journal = {2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  pages = {788-796},
  year = {2020},
}

Tile2Vec: Unsupervised representation learning for spatially distributed data

Neal Jean, Sherrie Wang, Anshul Samar, and 3 more authors

Proceedings of the AAAI Conference on Artificial Intelligence, 2019

Bib HTML Blog Code

@article{jean2019tile2vec,
  title = {Tile2Vec: Unsupervised representation learning for spatially distributed data},
  author = {Jean, Neal and Wang, Sherrie and Samar, Anshul and Azzari, George and Lobell, David and Ermon, Stefano},
  journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume = {33},
  number = {01},
  pages = {3967--3974},
  year = {2019},
}