| Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques S Wang, G Azzari, DB Lobell Remote sensing of environment 222, 303-317, 2019 | 80 | 2019 |
| Tile2vec: Unsupervised representation learning for spatially distributed data N Jean, S Wang, A Samar, G Azzari, D Lobell, S Ermon Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 3967-3974, 2019 | 59* | 2019 |
| Weakly supervised deep learning for segmentation of remote sensing imagery S Wang, W Chen, SM Xie, G Azzari, DB Lobell Remote Sensing 12 (2), 207, 2020 | 47 | 2020 |
| A mixture-of-modelers approach to forecasting NCAA tournament outcomes LH Yuan, A Liu, A Yeh, A Kaufman, A Reece, P Bull, A Franks, S Wang, ... Journal of Quantitative Analysis in Sports 11 (1), 13-27, 2015 | 27 | 2015 |
| Mapping crop types in southeast india with smartphone crowdsourcing and deep learning S Wang, S Di Tommaso, J Faulkner, T Friedel, A Kennepohl, R Strey, ... Remote Sensing 12 (18), 2957, 2020 | 12 | 2020 |
| Meta-learning for few-shot land cover classification M Rußwurm, S Wang, M Korner, D Lobell Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 12 | 2020 |
| Satellites reveal a small positive yield effect from conservation tillage across the US Corn Belt JM Deines, S Wang, DB Lobell Environmental Research Letters 14 (12), 124038, 2019 | 11 | 2019 |
| Mapping twenty years of corn and soybean across the US Midwest using the Landsat archive S Wang, S Di Tommaso, JM Deines, DB Lobell Scientific Data 7 (1), 1-14, 2020 | 8 | 2020 |
| Facial affect detection using convolutional neural networks S Wang Stanford University, 2016 | 5 | 2016 |
| Mapping Crop Types in India with Crowdsourced Data and Deep Learning S Wang, S Di Tommaso, J Faulkner, T Friedel, A Kennepohl, R Strey, ... AGU Fall Meeting Abstracts 2019, IN42A-03, 2019 | 1 | 2019 |
| Two shifts for crop mapping: Leveraging aggregate crop statistics to improve satellite-based maps in new regions DM Kluger, S Wang, DB Lobell Remote Sensing of Environment 262, 112488, 2021 | | 2021 |
| Deep learning for label-scarce remote sensing applications S Wang, DB Lobell AGU Fall Meeting Abstracts 2020, IN009-01, 2020 | | 2020 |
| Toward Global-Scale Field Boundary Delineation Using Deep Learning E Rostami, S Wang, S Di Tommaso, DB Lobell AGU Fall Meeting Abstracts 2020, IN028-11, 2020 | | 2020 |
| Investigating the Yield Impacts of Conservation Tillage in the US Corn Belt using Landsat JM Deines, S Wang, DB Lobell AGU Fall Meeting Abstracts 2019, GC21B-05, 2019 | | 2019 |
| Weakly Supervised Deep Learning for Segmentation of Cropland in Remote Sensing Imagery S Wang, W Chen, G Azzari, DB Lobell AGU Fall Meeting Abstracts 2018, IN14A-03, 2018 | | 2018 |
| Mapping the yields of major crops in Sub-Saharan African smallholder farming system Z Jin, G Azzari, S Wang, DB Lobell AGU Fall Meeting Abstracts 2018, GC51H-0890, 2018 | | 2018 |
| Toward global crop type mapping using a hybrid machine learning approach and multi-sensor imagery S Wang, S Le Bras, G Azzari, DB Lobell AGU Fall Meeting Abstracts 2017, B51C-1819, 2017 | | 2017 |
David LobellStanford UniversityVerified email at stanford.edu
