| 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 | 359 | 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 | 249 | 2020 |
| 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 | 246 | 2019 |
| Daily Local-Level Estimates of Ambient Wildfire Smoke PM2.5 for the Contiguous US ML Childs, J Li, J Wen, S Heft-Neal, A Driscoll, S Wang, CF Gould, M Qiu, ... Environmental Science & Technology 56 (19), 13607-13621, 2022 | 123 | 2022 |
| 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), 307, 2020 | 109 | 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 | 109 | 2020 |
| 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 | 86 | 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 | 83 | 2019 |
| SustainBench: Benchmarks for Monitoring the Sustainable Development Goals with Machine Learning C Yeh*, C Meng*, S Wang*, A Driscoll, E Rozi, P Liu, J Lee, M Burke, ... Thirty-fifth Conference on Neural Information Processing Systems, Datasets …, 2021 | 59 | 2021 |
| 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 | 42 | 2021 |
| Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision S Wang, F Waldner, DB Lobell Remote Sensing 14 (22), 5738, 2022 | 36 | 2022 |
| 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 | 36 | 2015 |
| Combining GEDI and Sentinel-2 for wall-to-wall mapping of tall and short crops S Di Tommaso, S Wang, DB Lobell Environmental Research Letters 16 (12), 125002, 2021 | 35 | 2021 |
| Good at captioning, bad at counting: Benchmarking gpt-4v on earth observation data C Zhang, S Wang arXiv preprint arXiv:2401.17600, 2024 | 17 | 2024 |
| Machine learning predicts which rivers, streams, and wetlands the Clean Water Act regulates S Greenhill, H Druckenmiller*, S Wang*, DA Keiser, M Girotto, JK Moore, ... Science 383 (6681), 406-412, 2024 | 16 | 2024 |
| Mapping Sugarcane in Central India with Smartphone Crowdsourcing JY Lee, S Wang, AJ Figueroa, R Strey, DB Lobell, RL Naylor, SM Gorelick Remote Sensing 14 (3), 703, 2022 | 14 | 2022 |
| Meta-Learning For Few-Shot Time Series Classification S Wang*, M Rußwurm*, M Körner, DB Lobell IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium …, 2020 | 12 | 2020 |
| Current benefits of wildfire smoke for yields in the US midwest may dissipate by 2050 AP Behrer, S Wang Environmental Research Letters 19 (8), 084010, 2024 | 9* | 2024 |
| Annual field-scale maps of tall and short crops at the global scale using GEDI and Sentinel-2 S Di Tommaso, S Wang, V Vajipey, N Gorelick, R Strey, DB Lobell Remote Sensing 15 (17), 4123, 2023 | 9 | 2023 |
| Facial affect detection using convolutional neural networks S Wang Stanford University, 2016 | 7 | 2016 |
David LobellStanford UniversityVerified email at stanford.edu
