Machine Learning for Remote Sensing:
Agriculture and Food Security
CVPR Tutorial: June 20, 2022 1:30-5:30 pm
In person: Room 243-245 | Virtual: join through CVPR virtual website
This tutorial will cover fundamental topics of machine learning for remote sensing applications
in agriculture and food security, focusing on the African context. You can find the full list
of tutorials on the CVPR 2022 website. You must be registered for the conference to
attend the tutorial, which is being held in person.
Schedule
All times are in local time for New Orleans.
| Time |
Topic |
Instructor |
| 1:30-2:10pm |
Remote sensing data and nuances [slides, video] |
Kerner |
| 2:10-2:30pm |
Overview of Earth observations for agriculture [slides, video] |
Nakalembe |
| 2:30-3:00pm |
Use cases and example methods [slides, video] |
Kerner |
| 3:00-3:30pm |
Break |
|
| 3:30-4:00pm |
Labeled datasets for agriculture [slides, video] |
Nakalembe |
| 4:00-5:30pm |
OpenMapFlow hands-on demo [slides, video] |
Zvonkov |
|
Instructors |
|
 |
 |
 |
Dr. Hannah Kerner
(University of Maryland) |
Dr. Catherine Nakalembe
(University of Maryland) |
Ivan Zvonkov
(University of Maryland) |
Additional Resources
- OpenMapFlow Github repository: https://github.com/nasaharvest/openmapflow
- Introduction to Earth Observation (curated list of useful resources)
- NASA Harvest Github organization: https://github.com/nasaharvest
- NASA Harvest website: https://nasaharvest.org
- Radiant Earth ML Hub: https://mlhub.earth/
- Google Earth Engine: https://earthengine.google.com/
- Kerner, H. R., Tseng, G., Becker-Reshef, I., Barker, B., Munshell, B., Paliyam, M., and Hosseini, M. (2020). Rapid Response Crop Maps in Data Sparse Regions. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining Workshops, link.
- Rustowicz, R., Cheong, R., Wang, L., Ermon, S., Burke, M., and Lobell, D. (2019). Semantic segmentation of crop type in africa: A novel dataset and analysis of deep learning methods. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (pp. 75-82), link.
- Tseng, G., Kerner, H., and Rolnick, D. (2022). TIML: Task-Informed Meta-Learning for Agriculture. arXiv preprint arXiv:2202.02124, link.
- Wang, S., Waldner, F., and Lobell, D. B. (2022). Unlocking large-scale crop field delineation in smallholder farming systems with transfer learning and weak supervision. arXiv preprint arXiv:2201.04771, link.
- You, J., Li, X., Low, M., Lobell, D., and Ermon, S. (2017). Deep Gaussian Process for crop yield prediction based on remote sensing data. In Thirty-First AAAI Conference on Artificial Intelligence, link.
- Kerner, H. R., Rebbapragada, U., Wagstaff, K. L., Lu, S., Dubayah, B., Huff, E., Raman, V., and Kulshrestha, S. (2022). Domain-Agnostic Outlier Ranking Algorithms—A Configurable Pipeline for Facilitating Outlier Detection in Scientific Datasets. Frontiers in Astronomy and Space Sciences, 9, 867947, link.
- Tusubira, J. F., Akera, B., Nsumba, S., Nakatumba-Nabende, J., and Mwebaze, E. (2020). Scoring root necrosis in cassava using semantic segmentation. In CVPR Workshops 2020, link.
