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<!doctype html>
<html lang="en">
<head>
<title>GeoAI & ML</title>
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<div class="align-middle mx-auto course-title">GEOG 490: GeoAI and Machine Learning</div>
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<main role="main" class="container">
<div class="jumbotron no-background">
<div>
<h3><u>Course Description</u></h3>
<div>
<p>
The availability of high-resolution geographic data, recent progress in artificial intelligence techniques, and high-performance computing techniques are contributing to the emerging field of GeoAI that includes the detections of terrain features and densely-distributed building footprints, information extraction from scanned historical maps, semantic classification, novel methods for spatial interpolation, advances in traffic forecasting, improving geographic information retrieval, constructing advanced geographic knowledge graphs for geo-enrichment, and semantically enabled services for spatial data infrastructures. GEOG 490 is an introduction to machine/deep learning in the field of GeoAI, a subfield that emerged from geographic information science, spatial statistics, and computer science. In this course, students will learn how machine/deep learning techniques as a subcategory of artificial intelligence techniques can be used for research with geographical data. Students are required to use Python for this course.
</p>
</div>
<div>
<p class="no-space-after"><span style="font-weight: bold;">Class Location: </span>online</p>
<p class="no-space-after"><span style="font-weight: bold;">Class Time: </span>no specific time, videos are available each Monday and Wednesday</p>
<p class="no-space-after"><span style="font-weight: bold;">Instructor: </span>Mohammad Eshghi</p>
<p class="no-space-after"><span style="font-weight: bold;">Email: </span><a href="mailto:[email protected]" target="_blank">[email protected]</a></p>
<p><span style="font-weight: bold;">Office hours: </span>12:30 pm - 1:30 pm on Tuesdays and 9am - 10am on Thursdays through Zoom</p>
</div>
</div>
<div class="clear-fix"></div>
<div>
<h3><u>Course Objectives</u></h3>
<div>
<p>
By the end of the class, students should have a sound understanding of how machine/deep learning algorithms are used for geospatial data, how geospatial data are preprocessed to be fed into a predictive modeling procedure, how geospatial data and analysis results are communicated, and why and when these techniques are appropriate.
</p>
</div>
</div>
<div class="clear-fix"></div>
<div>
<h3><u>Draft Schedule (Subject to change)</u></h3>
<table class="table table-striped">
<thead>
<tr>
<th>Week of</th>
<th>Slides</th>
<th>Videos</th>
<th>Topics</th>
<th>Readings</th>
</tr>
</thead>
<tbody>
<tr>
<td>Mar. 30 - Apr. 01</td>
<td><a href="https://drive.google.com/uc?export=download&id=1dwOPnTMtaXhCLA7vLI5yhOFBSv6xrSQw" target="_blank">Lecture-01</a>, <a href="https://drive.google.com/uc?export=download&id=13Sl7m4BSKImd9B-tkZlE34GplTsU2JPa" target="_blank">Lecture-02</a></td>
<td><a href="https://uoregon.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=306f099f-b7c7-426d-895d-ab8f000aacd0" target="_blank">Video-01</a>, <a href="https://uoregon.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=5f32a8e3-af78-4114-8893-ab900176cf55" target="_blank">Video-02</a></td>
<td>Fundamentals</td>
<td><a href="https://www.deeplearningbook.org/contents/linear_algebra.html" target="_blank">Reading-01</a></td>
</tr>
<tr>
<td>Apr. 06 - Apr. 08</td>
<td><a href="https://drive.google.com/uc?export=download&id=1pR-sg9m88hUnPHIJ_jYMyjLTotN6w_Db" target="_blank">Lecture-03</a>, <a href="https://drive.google.com/uc?export=download&id=1YBwdQyh6La_PMwoRFH8e5Iw9zK9fSv8E" target="_blank">Lecture-04</a></td>
<td><a href="https://uoregon.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=52fb710f-ba5f-4343-9d3b-ab9600789406" target="_blank">Video-03</a>, <a href="https://uoregon.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=825126e5-f9f0-4c4d-b864-ab980117ddda" target="_blank">Video-04</a></td>
<td><!-- Decision Trees, Random Forest, AdaBoost, Gradient Boost, XGBoost --> Regression</td>
<td><a href="#" class="disabled hidden" target="_blank">Reading-02</a></td>
</tr>
<tr>
<td>Apr. 13 - Apr. 15</td>
<td><a href="https://drive.google.com/uc?export=download&id=16vAJlgsIK83oDN8k8vyQbkeoXNygFrlf" target="_blank">Lecture-05</a>, <a href="#" class="disabled" target="_blank">Lecture-06 (Zoom meeting)</a></td>
<td><a href="https://uoregon.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=e63e9fd3-fe42-465c-a958-ab9d002a7439" target="_blank">Video-05</a>, <a href="#" class="disabled" target="_blank">Video-06 (Zoom meeting)</a></td>
<td>Perceptron</td>
<td><a href="http://neuralnetworksanddeeplearning.com/chap1.html" target="_blank">Reading-01</a>, <a href="http://neuralnetworksanddeeplearning.com/chap2.html" target="_blank">Reading-02</a></td>
</tr>
<tr>
<td>Apr. 20 - Apr. 22</td>
<td><a href="https://drive.google.com/uc?export=download&id=16hdG2PdTf_6RAzoAUD_xk0HxxgAHn3kx" target="_blank">Review slides (01-04)</a>, <a href="https://colab.research.google.com/drive/1UqpADii7kf3aBfAQOD59lEv3hoIBRFKI" target="_blank">ProjectDemo (nodebook)</a>, <a href="https://drive.google.com/uc?export=download&id=1LWmv7wVPilBs87PJef6Vhd1GbI1dPl95" target="_blank">Lecture-07</a></td>
<td><a href="https://uoregon.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=60b86526-ad1f-4a1c-80d4-aba10120480d" target="_blank">r01p01</a>, <a href="https://uoregon.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=9392fafc-29b3-4913-99ad-aba1012892c2" target="_blank">r01p02</a>, <a href="https://uoregon.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=588a03cd-4249-4575-9ae7-aba10135abd8" target="_blank">r01p03</a>, <a href="https://uoregon.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=fe87ba04-6ff7-481d-9fe2-aba1013b53df" target="_blank">r01p04</a>, <a href="https://uoregon.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=8787372f-510f-42a3-b4e2-aba401368615" target="_blank">ProjectDemo</a>, <a href="https://uoregon.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=625afbcb-58c3-47ea-8cd6-aba60017c1f6" target="_blank">Video-06-01</a>, <a href="https://uoregon.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=60772b95-a4cd-42e5-921f-aba600200c88" target="_blank">Video-06-02</a>, <a href="https://uoregon.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=350b242d-cf5a-47e7-8d32-aba6002040df" target="_blank">Video-06-03</a></td>
<td>Review, Project demo, Neural networks</td>
<td><a href="https://uoregon.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=d2cfb2a7-fcd8-4288-acfc-abaa000bda0b" target="_blank">how to use Kaggle</a>, <a href="" class="disabled" target="_blank">Readings in the slides</a></td>
</tr>
<tr>
<td>Apr. 27 - Apr. 29</td>
<td><a href="https://drive.google.com/uc?export=download&id=15sEh5IGTMtZ4Hip70ouziD5JsA9NxRha" target="_blank">Lecture-08</a>, <a href="https://drive.google.com/uc?export=download&id=1cXu5KZCv62XS-BtfrfWfiwxws3JFNu7t" target="_blank">Lecture-09</a></td>
<td><a href="https://uoregon.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=26667053-d3c4-42e5-9195-abab01252ea9" target="_blank">Video-08</a>, <a href="https://uoregon.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=530a133b-3918-464b-926c-abad001d016e" target="_blank">Video-09</a></td>
<td><!-- Probability and Bayesian Machine Learning -->Deep Learning (continued)</td>
<td><a href="https://www.deeplearningbook.org/contents/mlp.html" target="_blank">Reading-01</a></td>
</tr>
<tr>
<td>May 04 - May 06</td>
<td><a href="https://drive.google.com/uc?export=download&id=1UcQQewi-D1etsQgUPCVTvrsQI9-UAB38" target="_blank">Lecture-10</a>, <a href="https://drive.google.com/uc?export=download&id=1BAi8aOrKT1iFye-KEpwfZ95oFqFwptbc" target="_blank">Lecture-11</a></td>
<td><a href="https://uoregon.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=e099c86b-1edc-4a75-b574-abae0057a259" target="_blank">Video-10</a>, <a href="https://uoregon.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=a0411deb-be20-498f-a344-abae007548ef" target="_blank">Video-11</a></td>
<td><!-- Perceptron, Neural Networks -->Deep Learning-CNN</td>
<td><a href="https://cs231n.github.io/convolutional-networks/" target="_blank">Reading-01</a></td>
</tr>
<tr>
<td>May 11 - May 13</td>
<td><a href="#" class="disabled" target="_blank">Lecture-13</a>, <a href="#" class="disabled" target="_blank">Lecture-14</a></td>
<td><a href="#" class="disabled" target="_blank">Video-13</a>, <a href="#" class="disabled" target="_blank">Video-14</a></td>
<td><!-- Deep learning: CNN -->Deep Learning-RNN</td>
<td><a href="#" class="disabled hidden" target="_blank">Reading-07</a></td>
</tr>
<tr>
<td>May 18 - May 20</td>
<td><a href="#" class="disabled" target="_blank">Lecture-15</a>, <a href="#" class="disabled" target="_blank">Lecture-16</a></td>
<td><a href="#" class="disabled" target="_blank">Video-15</a>, <a href="#" class="disabled" target="_blank">Video-16</a></td>
<td><!-- Deep learning: RNN -->Ensemble learning</td>
<td><a href="#" class="disabled hidden" target="_blank">Reading-08</a></td>
</tr>
<tr>
<td>May 25 - May 27</td>
<td><a href="#" class="disabled" target="_blank">Exam Review</a>, <span style="color: red">Midterm</span></td>
<td><a href="#" class="disabled" target="_blank"> <a href="https://zoom.us/" target="_blank">Zoom</a>, N/A</a>
<td>-------------------------</td>
<td><a href="#" class="disabled hidden" target="_blank">Reading-09</a></td>
</tr>
<tr>
<td>June 01 - June 03</td>
<td><a href="#" class="disabled" target="_blank">Lecture-17</a>, <a href="#" target="_blank" style="color:red;">FP Presentation</a></td>
<td><a href="#" class="disabled" target="_blank">Video-17</a>, <a href="https://zoom.us/" target="_blank">Zoom</a></td>
<td><!-- Semantic Segmentation, Visual Question Answering -->Applications: Semantic Segmentation, VQA</td>
<td><a href="#" class="disabled hidden" target="_blank">Reading-10</a></td>
</tr>
</tbody>
</table>
<div><span style="font-weight: bold;">Canvas: </span>Note that we will use canvas for communication, written assignment, and final project submissions. [<a href="https://canvas.uoregon.edu/courses/159638" target="_blank">Link</a>]
</div>
<div class="clear-fix0"></div>
</div>
<div class="clear-fix"></div>
<div>
<h3><u>Textbook</u></h3>
There is no required textbook for this class. Readings will be provided, if assigned. A printable version of lecture slides and a video recording of the lecture will be online on Mondays and Wednesdays of each week. I do not make them available before class.<!-- However, we will sometime refer to the "Machine Learning for Spatial Environmental Data: Theory, Applications, and Software" book (by Mikhail Kanevski, Vadim Timonin, Alexi Pozdnukhov) and "Elements of Statistical Learning" book (by Hastie) during the class. -->
</div>
<div class="clear-fix"></div>
<div>
<h3><u>Final Project</u></h3>
<table class="table table-striped">
<thead>
<tr>
<th>Due date</th>
<th>Project</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>06/05</td>
<td>Project presentations</td>
<td><a href="#" class="disabled" target="_blank">Instructions for project report</a></td>
</tr>
<tr>
<td>06/09</td>
<td>Final paper</td>
<td><a href="#" class="disabled" target="_blank">Instructions for project report</a></td>
</tr>
</tbody>
</table>
</div>
<div class="clear-fix"></div>
<div>
<h3><u>Homeworks</u></h3>
<table class="table table-striped">
<thead>
<tr>
<th>Due date</th>
<th>Homework</th>
<th>Description</th>
</tr>
</thead>
<tbody>
<tr>
<td>04/22</td>
<td><a href="https://drive.google.com/uc?export=download&id=1fFoeIsJqNXimAALB9Nj6UWnp-LZnSF7h" target="_blank">Homework-01</a></td>
<td>Written</td>
</tr>
<tr>
<td>05/01</td>
<td><a href="#" class="disabled" target="_blank">Homework-02</a></td>
<td>Written</td>
</tr>
<tr>
<td>05/15</td>
<td><a href="#" class="disabled" target="_blank">Homework-03</a></td>
<td>Written</td>
</tr>
</tbody>
</table>
</div>
<div class="clear-fix"></div>
<div>
<h3><u>Grading Policy</u></h3>
<ul>
<li><b>Homework (35%)</b>: written assignments</li>
<li><b>Midterm (40%)</b>: There will be a written test covering the first 2/3rds or so of the course, in order to leave you the last third of the class to focus on the course project. The questions will focus on concepts and theory.</li>
<li><b>Final Project (25%)</b>: Work in a group (2-3 people) of your choice to apply a machine learning to a gepspasial problem of your choice. May involve developing a new algorithm, working with a new dataset, or defining a new problem on an existing dataset. Each group will submit a written report and give a short presentation during finals week.</li>
</ul>
</div>
<div class="clear-fix"></div>
<div>
<h3><u>Grading Rubric</u></h3>
<table class="table table-striped">
<thead>
<tr>
</tr>
</thead>
<tbody>
<tr>
<td>
<ul>
<li>A<sup>+</sup>: 97% and greater</li>
<li>A: 93% to 97%</li>
<li>A<sup>-</sup>: 90% to 93%</li>
</ul>
</td>
<td>
<ul>
<li>B<sup>+</sup>: 87% to 90%</li>
<li>B: 83% to 87%</li>
<li>B<sup>-</sup>: 80% to 83%</li>
</ul>
</td>
<td>
<ul>
<li>C<sup>+</sup>: 77% to 80%</li>
<li>C: 73% to 77%</li>
<li>C<sup>-</sup>: 70% to 73%</li>
</ul>
</td>
<td>
<ul>
<li>D<sup>+</sup>: 67% to 70%</li>
<li>D: 63% to 67%</li>
<li>D<sup>-</sup>: 60% to 63%</li>
</ul>
</td>
<td>
<ul>
<li>F: less than 60%</li>
</ul>
</td>
</tr>
</tbody>
</table>
</div>
<div class="clear-fix"></div>
<div>
<h3><u>Estimated Student Workload</u></h3>
The course contains lectures, assignments, activities, and a final project. Students spend three hours in lectures. Each lecture consists of 70-80 mins of presentation by the instructor in a video-recorded format. Presentations are interleaved with activities, in order to allow students to actively engage with concepts and to make theoretical material tangible with hands-on experience. The day after each lecture is released, there will be a Zoom session for the office hour that is for QA about the lectures. Assignments deepen the practical part of the learning experience enabling students to apply the presented concepts so as to reach learning objectives. Students are expected to spend about at least 12 hours per week on course readings and assignments.
</div>
<div class="clear-fix"></div>
<div>
<h3><u>Other Resources</u></h3>
<ul>
<li><a href="https://www.crcpress.com/Machine-Learning-for-Spatial-Environmental-Data-Theory-Applications-and/Kanevski-Timonin-Pozdnukhov/p/book/9780429147814" target="_blank">Machine Learning for Spatial Environmental Data: Theory, Applications, and Software</a> by Mikhail Kanevski, Vadim Timonin, Alexi Pozdnukhov</li>
<li><a href="https://web.stanford.edu/~hastie/ElemStatLearn/" target="_blank">The Elements of Statistical Learning</a> by Hastie</li>
<li><a href="https://www.cs.ubc.ca/~murphyk/MLbook/index.html" target="_blank">Machine Learning: A Probabilistic Perspective</a> by Kevin Murphy</li>
<li><a href="https://www.deeplearningbook.org/" target="_blank">Deep Learning</a> by Ian Goodfellow and Yoshua Bengio and Aaron Courville</li>
<li><a href="https://www.kaggle.com/" target="_blank" class="">Kaggle ML competitions</a></li>
<li><a href="http://archive.ics.uci.edu/ml/index.php" target="_blank" class="">UCI ML repository</a></li>
</ul>
</div>
<div class="clear-fix"></div>
<div>
<h3><u>Barriers and Accommodations</u></h3>
<div>
To encourage an inclusive environment, we will make reasonable accommodations to provide all students with the resources to participate in class. Students with disabilities who require accommodations to participate in class or meet course requirements are encouraged to first contact the <a href="https://aec.uoregon.edu/" target="_blank">Accessible Education Center</a> located at 164 Oregon Hall, 346-1155, and then contact me as soon as possible.
</div>
</div>
<div class="clear-fix"></div>
<div>
<h3><u>Academic Honesty</u></h3>
<div>
Academic honesty is expected and cases of suspected dishonesty will be handled according to <a href="https://dos.uoregon.edu/academic-misconduct" target="_blank">university policy</a>. In particular, unless explicitly stated in the instructions, all the assignments and projects are individual efforts and students are expected to complete their own work on all assignments and projects. Copying someone else's work (including material found on the web) will not be tolerated. If solutions to assignments are obtained from outside sources, the source must be cited. Academic dishonesty will lead to an <strong>"F"</strong> for the entire course.
</div>
</div>
<div class="clear-fix"></div>
<div>
<h3><u>Conduct</u></h3>
<div>
All students are expected to adhere to the <a href="https://policies.uoregon.edu/vol-3-administration-student-affairs/ch-1-conduct/student-conduct-code" target="_blank">Student Code of Conduct</a> and conduct themselves accordingly in the online classroom settings; students who are disruptive will be asked to leave the Zoom session.
</div>
</div>
<div class="clear-fix"></div>
<div>
<h3><u>Late Policy</u></h3>
<div>
All assignments and final project are due by 11:59 p.m. of the stated due date. Please submit your assignments and final project on Canvas. Only one assignment will be accepted for up to three days past the due date with a penalty of <b>20% for each calendar day</b>. You may ask for <strong>one extension without penalty</strong>, but you should contact me soon.
</div>
</div>
<div class="clear-fix"></div>
<div>
<h3><u>Supplementary Materials</u></h3>
<div>
<ul>
<li><a href="https://www.e-education.psu.edu/geog858/node/719" target="_blank">Emerging Theme: Geospatial Artificial Intelligence (geoAI)</a></li>
<li>You are encouraged to explore deep-learning with <a href="https://keras.io/" target="_blank">Keras</a> and/or <a href="https://pytorch.org/" target="_blank">PyTorch</a> in this class.</li>
<li> You can find trending machine-learning research and the corresponding codes in <a href="https://paperswithcode.com/" target="_blank">Papers With Code</a>.</li>
</ul>
</div>
</div>
<div class="clear-fix"></div>
<div class="">
<h3><u>Acknowledgement</u></h3>
<div>
The course materials are based on different courses by different people. I would like to thank them all.
</div>
</div>
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