no code implementations • 19 Dec 2022 • Stefania Di Tommaso, Sherrie Wang, Vivek Vajipey, Noel Gorelick, Rob Strey, David B. Lobell
In the current study, we leverage GEDI to develop wall-to-wall maps of short vs tall crops on a global scale at 10 m resolution for 2019-2021.
no code implementations • 17 Jul 2022 • Yezhen Cong, Samar Khanna, Chenlin Meng, Patrick Liu, Erik Rozi, Yutong He, Marshall Burke, David B. Lobell, Stefano Ermon
Unsupervised pre-training methods for large vision models have shown to enhance performance on downstream supervised tasks.
no code implementations • 4 Apr 2022 • Yutong He, William Zhang, Chenlin Meng, Marshall Burke, David B. Lobell, Stefano Ermon
Automated tracking of urban development in areas where construction information is not available became possible with recent advancements in machine learning and remote sensing.
no code implementations • 13 Jan 2022 • Sherrie Wang, Francois Waldner, David B. Lobell
Our best model uses 1. 5m resolution Airbus SPOT imagery as input, pre-trains a state-of-the-art neural network on France field boundaries, and fine-tunes on India labels to achieve a median Intersection over Union (IoU) of 0. 86 in India.
1 code implementation • 8 Nov 2021 • Christopher Yeh, Chenlin Meng, Sherrie Wang, Anne Driscoll, Erik Rozi, Patrick Liu, Jihyeon Lee, Marshall Burke, David B. Lobell, Stefano Ermon
Our goals for SustainBench are to (1) lower the barriers to entry for the machine learning community to contribute to measuring and achieving the SDGs; (2) provide standard benchmarks for evaluating machine learning models on tasks across a variety of SDGs; and (3) encourage the development of novel machine learning methods where improved model performance facilitates progress towards the SDGs.
no code implementations • 19 Oct 2021 • Chenxi Lin, Liheng Zhong, Xiao-Peng Song, Jinwei Dong, David B. Lobell, Zhenong Jin
Land cover classification in remote sensing is often faced with the challenge of limited ground truth.
no code implementations • 10 Sep 2021 • 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 labels for model training.
no code implementations • 2 Sep 2021 • Dan M. Kluger, Sherrie Wang, David B. Lobell
Still, in many regions crop type mapping with satellite data remains constrained by a scarcity of field-level crop labels for training supervised classification models.
1 code implementation • NeurIPS 2021 • Yutong He, Dingjie Wang, Nicholas Lai, William Zhang, Chenlin Meng, Marshall Burke, David B. Lobell, Stefano Ermon
High-resolution satellite imagery has proven useful for a broad range of tasks, including measurement of global human population, local economic livelihoods, and biodiversity, among many others.
no code implementations • 20 Jul 2020 • Ariel Ortiz-Bobea, Toby R. Ault, Carlos M. Carrillo, Robert G. Chambers, David B. Lobell
Agricultural research has fostered productivity growth, but the historical influence of anthropogenic climate change on that growth has not been quantified.