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<!DOCTYPE html>
<html lang="en">
<head>
<title>Scalable Cropland Mapping Workshop</title>
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<body>
<!-- Header -->
<div id="navbar">
<a href="https://nasaharvest.github.io/">
<div id="logo">
<img src="assets/logo.png"/>
<div id="logotext">
<h1>NASA Harvest</h1>
<h3>Machine Learning</h3>
</div>
</div>
</a>
<div class="menu">
<a href="https://nasaharvest.github.io/">About</a>
<a href="https://nasaharvest.github.io/#sessions">Sessions</a>
<a href="https://nasaharvest.github.io/#profiles">Team</a>
</div>
</div>
<!-- Main -->
<div id="content50">
<h1>Scalable Cropland Mapping Workshop </h1>
<p style="line-height: 1.6;">
Monday August 22, 2022 - Thursday August 25, 2022
<br>In person: Room 301 at 4600 River Road, Riverdale Park, MD
<br>Virtual: join through <a href="https://umd.zoom.us/my/izvonkov">https://umd.zoom.us/my/izvonkov</a>
<br>Public Google Doc: <a href="https://docs.google.com/document/d/1Kp6MphER1G5tdLYeAzl4n19S10TweIxiYT64rXsjKm4/edit?usp=sharing">link</a>
<br>Public Google Drive: <a href="https://drive.google.com/drive/folders/155Rx-QBSJG5rUZqJ_3R2kN9pa6YPyteu?usp=sharing">link</a>
</p>
<img src="assets/data-to-map.png" width="90%"/>
<p>
This workshop will do a deep dive into scalable cropland mapping with deep learning.
Fundamental topics in machine learning, satellite time series data, labeling, and scalable systems will be covered.
Every participant will generate their own project for cropland mapping with deep learning.
</p>
<h2>Schedule</h2>
<p>All times are in local time for Maryland.</p>
<!--Tables created using https://tableconvert.com/excel-to-html -->
<table class="schedule">
<tr>
<td></td>
<td>Monday August 22, Day 1/4 - Scalable Cropland Mapping</td>
<td></td>
</tr>
<tr>
<td>Time</td>
<td>Activity</td>
<td>Instructor</td>
</tr>
<tr>
<td>13:00 - 13:40</td>
<td>Intro to workshop</td>
<td>Ivan</td>
</tr>
<tr>
<td>13:40 - 15:00</td>
<td>OpenMapFlow preview</td>
<td>Ivan</td>
</tr>
<tr>
<td>15:00 - 15:30</td>
<td>Tea break</td>
<td></td>
</tr>
<tr>
<td>15:30 - 17:00</td>
<td>Labeling in CollectEarthOnline</td>
<td>Ivan</td>
</tr>
</table>
<br>
<table class="schedule">
<tr>
<td></td>
<td>Tuesday August 23, Day 2/4 - Scalable Cropland Mapping</td>
<td></td>
</tr>
<tr>
<td>Time</td>
<td>Activity</td>
<td>Instructor</td>
</tr>
<tr>
<td>9:00 - 9:30</td>
<td>Labeling in CollectEarthOnline</td>
<td>Ivan</td>
</tr>
<tr>
<td>9:30 - 10:00</td>
<td>ML models/intro</td>
<td>Gabi</td>
</tr>
<tr>
<td>10:00 - 10:30</td>
<td>Labeling in CollectEarthOnline</td>
<td>Ivan</td>
</tr>
<tr>
<td>10:30 - 11:00</td>
<td>Tea break</td>
<td></td>
</tr>
<tr>
<td>11:00 - 11:30</td>
<td>Label Consensus</td>
<td>Hannah</td>
</tr>
<tr>
<td>11:30 - 12:00</td>
<td>ML for Earth Observation data</td>
<td>Hannah</td>
</tr>
<tr>
<td>12:30 - 13:30</td>
<td>ML for Earth Observation data discussion</td>
<td>Ivan, Gabi</td>
</tr>
<tr>
<td>13:30 - 15:00</td>
<td>Lunch</td>
<td></td>
</tr>
<tr>
<td>15:00 - 15:30</td>
<td>Machine learning systems</td>
<td>Ivan</td>
</tr>
<tr>
<td>15:30 - 17:00</td>
<td>Generating a new OpenMapFlow project</td>
<td>Ivan</td>
</tr>
</table>
<br>
<table class="schedule">
<tr>
<td></td>
<td>Wednesday August 23, Day 3/4 - Scalable Cropland Mapping</td>
<td></td>
</tr>
<tr>
<td>Time</td>
<td>Activity</td>
<td>Instructor</td>
</tr>
<tr>
<td>09:00 - 09:30</td>
<td>Generating ML ready data</td>
<td>Ivan</td>
</tr>
<tr>
<td>09:30 - 10:30</td>
<td>ML models for EO data</td>
<td>Gabi</td>
</tr>
<tr>
<td>10:30 - 11:00</td>
<td>Tea break</td>
<td></td>
</tr>
<tr>
<td>11:00 - 12:30</td>
<td>Generating ML ready data revisited</td>
<td>Ivan</td>
</tr>
<tr>
<td>12:30 - 14:00</td>
<td>Lunch</td>
<td></td>
</tr>
<tr>
<td>14:00 - 15:00</td>
<td>Training a model</td>
<td>Ivan</td>
</tr>
<tr>
<td>15:00 - 16:00</td>
<td>Making a map</td>
<td>Ivan</td>
</tr>
<tr>
<td>16:00 - 17:00</td>
<td>Discussion / Tea break (outside)</td>
<td></td>
</tr>
</table>
<br>
<table class="schedule">
<tr>
<td></td>
<td>Thursday August 24, Day 4/4 - Scalable Cropland Mapping</td>
<td></td>
</tr>
<tr>
<td>Time</td>
<td>Activity</td>
<td>Instructor</td>
</tr>
<tr>
<td>9:00 - 10:00</td>
<td>Improving Map Discussion</td>
<td>Ivan</td>
</tr>
<tr>
<td>10:00 - 10:30</td>
<td>Improving Map In Practice</td>
<td>Ivan</td>
</tr>
<tr>
<td>10:30 - 11:00</td>
<td>CropHarvest</td>
<td>Gabi</td>
</tr>
<tr>
<td>11:00 - 11:30</td>
<td>Tea break</td>
<td></td>
</tr>
<tr>
<td>11:30 - 12:00</td>
<td>Harvest's crop type mapping with TIML</td>
<td>Gabi</td>
</tr>
<tr>
<td>12:00 - 13:00</td>
<td>Conclusion and discussion</td>
<td>Ivan</td>
</tr>
</table>
<table id="profiles">
<tr>
<th colspan="4"><h2>Instructors</h2></th>
</tr>
<tr>
<td><img src="profiles/Zvonkov.jpg" class="profile"></td>
<td><img src="profiles/Tseng.jpeg" class="profile"/></td>
<td><img src="profiles/Kerner.png" class="profile"/></td>
<td><img src="profiles/Nakalembe.jpeg" class="profile"/></td>
</tr>
<tr>
<td>Ivan Zvonkov<br>(University of Maryland)</td>
<td>Gabriel Tseng<br>(McGill<br>University)</td>
<td>Dr. Hannah Kerner<br>(Arizona State University)</td>
<td>Dr. Catherine Nakalembe<br>(University of Maryland)</td>
</tr>
</table>
<h2>Additional Resources</h2>
<ul>
<li>NASA Harvest Github organization: <a href="https://github.com/nasaharvest">https://github.com/nasaharvest</a> </li>
<li>OpenMapFlow Github repository: <a href="https://github.com/nasaharvest/openmapflow">https://github.com/nasaharvest/openmapflow</a></li>
<li>Example W&B Project: <a href="https://wandb.ai/nasa-harvest/crop-mask-example">https://wandb.ai/nasa-harvest/crop-mask-example</a></li>
<li>CropHarvest Github repository <a href="https://github.com/nasaharvest/cropharvest"/>https://github.com/nasaharvest/cropharves</a></li>
<li><a href="https://docs.google.com/document/d/1UYxjAyhIkgTUiOCvRwsWo-JBV9y0jmHluC0zWqU5M-Q/edit"> Introduction to Earth Observation</a> (curated list of useful resources)</li>
<li>NASA Harvest website: <a href="https://nasaharvest.org/">https://nasaharvest.org</a></li>
<li>Radiant Earth ML Hub: <a href="https://mlhub.earth/">https://mlhub.earth/</a></li>
<li>Google Earth Engine: <a href="https://earthengine.google.com/">https://earthengine.google.com/</a></li>
<li>Rußwurm M., Körner M., Self-attention for raw optical Satellite Time Series Classification (2019), <a href="https://arxiv.org/pdf/1910.10536.pdf">link</a></li>
<li>Stehman, S. V., & Foody, G. M. (2019). Key issues in rigorous accuracy assessment of land cover products. Remote Sensing of Environment, 231, 111199.</li>
<li>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, <a href="https://arxiv.org/abs/2006.16866">link</a>.</li>
<li>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), <a href="https://openaccess.thecvf.com/content_CVPRW_2019/papers/cv4gc/Rustowicz_Semantic_Segmentation_of_Crop_Type_in_Africa_A_Novel_Dataset_CVPRW_2019_paper.pdf">link</a>.</li>
<li>Tseng, G., Kerner, H., and Rolnick, D. (2022). TIML: Task-Informed Meta-Learning for Agriculture. arXiv preprint arXiv:2202.02124, <a href="https://arxiv.org/pdf/2202.02124.pdf">link</a>.</li>
<li>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, <a href="https://arxiv.org/abs/2201.04771">link</a>. </li>
<li>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, <a href="https://cs.stanford.edu/~ermon/papers/cropyield_AAAI17.pdf">link</a>.</li>
<li>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, <a href="https://www.frontiersin.org/articles/10.3389/fspas.2022.867947/full">link</a>.</li>
<li>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, <a href="https://arxiv.org/pdf/2005.03367.pdf">link</a>.</li>
</ul>
<table id="sponsors">
<tr><th colspan="2"><h2>Supported by</h2></th></tr>
<tr>
<td colspan="2"><img src="assets/Harvest_Header_Color_Horizontal_Text.png" style="max-height: 7em; width: auto"/></td>
</tr>
<tr>
<td><img src="assets/servir.png" style="max-height: 7em;"/></td>
<td><img src="assets/swissre.png" style="max-height: 5em"/></td>
</tr>
<tr>
</tr>
</table>
</div>
</body>
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