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spatial-ai.html
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<!DOCTYPE html>
<link rel="stylesheet" type="text/css" href="style.css">
<link href="https://fonts.googleapis.com/css?family=Open+Sans:300,400,700" rel="stylesheet">
<html>
<head>
<title>CSCI 5980/8980 - Spatial Enabled Artificial Intelligence</title>
</head>
<body>
<h2><strong>CSCI 5980/8980 - Spatial Enabled Artificial Intelligence</strong></h2>
<p>Yao-Yi Chiang <a href="mailto:[email protected]">[email protected]</a> <br></p>
<p>04:00 PM‑05:15 PM MW Amundson Hall 120<br></p>
<h3><strong>Useful Links (Spring 2022)</strong></h3>
<a href="https://piazza.com/class/ky91axj4suo9v"> Piazza </a><br>
<a href="https://canvas.umn.edu/courses/301434"> Canvas (UMN students only) </a>
<br>
<h3><strong>Syllabus</strong></h3>
<a href="https://yaoyichi.github.io/spatial-ai/spatial-ai-syllabus.pdf">PDF</a>,
<a href="https://yaoyichi.github.io/spatial-ai/spatial-ai-syllabus.docx">Word</a>
<br>
<h3><strong>Slides</strong></h3>
<ul>
<li>Week 1:
<ul>
<li><a href="https://yaoyichi.github.io/spatial-ai/w1-intro.pptx">Introduction to Spatial AI</a>, <a href="https://yaoyichi.github.io/spatial-ai/w1-intro.pdf">PDF</a>, <a href="https://youtu.be/GyVmKNHUUtU">Video</a></li>
</ul>
</li>
<li>Weeks 2, 3, 4:
<ul>
<li><a href="https://yaoyichi.github.io/spatial-ai/w2w3.1-SDM.pptx">Spatial Data Management using Spatial Databases</a>, <a href="https://yaoyichi.github.io/spatial-ai/w2w3.1-SDM.pdf">PDF</a></li>
<li><a href="https://yaoyichi.github.io/spatial-ai/w2w3.2-MapReduce.pptx">Spatial Data Management using Big Data Platforms</a>, <a href="https://yaoyichi.github.io/spatial-ai/w2w3.2-MapReduce.pdf">PDF</a></li>
<li><a href="https://yaoyichi.github.io/spatial-ai/w2w3.3.MapReduce-1.pptx">Spatial Data Management using Big Data Platforms - MapReduce 1</a>, <a href="https://yaoyichi.github.io/spatial-ai/w2w3.3.MapReduce-1.pdf">PDF</a></li>
<li><a href="https://yaoyichi.github.io/spatial-ai/w2w3.4.MapReduce-2.pptx">Spatial Data Management using Big Data Platforms - MapReduce 2</a>, <a href="https://yaoyichi.github.io/spatial-ai/w2w3.4.MapReduce-2.pdf">PDF</a></li>
<li><a href="https://yaoyichi.github.io/spatial-ai/assignment-1-description.docx">Assignment 1</a>, <a href="https://yaoyichi.github.io/spatial-ai/assignment-1-description.pdf">PDF</a></li>
<li><a href="https://yaoyichi.github.io/spatial-ai/w2w3.5.intro-spark-geospark.pptx">Introduction to Spark and GeoSpark</a>, <a href="https://yaoyichi.github.io/spatial-ai/w2w3.5.intro-spark-geospark.pdf">PDF</a></li>
</ul>
</li>
<li>Weeks 4, 5, 6:
<ul>
<li><a href="https://yaoyichi.github.io/spatial-ai/w4w5.1-SDA-AQM.pptx">Mining Public Datasets for Modeling Intra-City PM2.5 Concentrations at a Fine Spatial Resolution</a>, <a href="https://yaoyichi.github.io/spatial-ai/w4w5.1-SDA-AQM.pdf">PDF</a></li>
<li><a href="https://yaoyichi.github.io/spatial-ai/w4w5.2-ch07-clustering.pptx">Clustering</a>, <a href="https://yaoyichi.github.io/spatial-ai/w4w5.2-ch07-clustering.pdf">PDF</a></li>
<li><a href="https://yaoyichi.github.io/spatial-ai/w4w5.3-random-forest.pptx">Random Forest</a>, <a href="https://yaoyichi.github.io/spatial-ai/w4w5.3-random-forest.pdf">PDF</a></li>
<li><a href="https://yaoyichi.github.io/spatial-ai/w4w5.4-ch11-dimred.pptx">Dimension Reduction with SVD</a>, <a href="https://yaoyichi.github.io/spatial-ai/w4w5.4-ch11-dimred.pdf">PDF</a></li>
<li><a href="https://yaoyichi.github.io/spatial-ai/assignment-2-description.docx">Assignment 2</a>, <a href="https://yaoyichi.github.io/spatial-ai/assignment-2-description.pdf">PDF</a></li>
</ul>
</li>
<li>Weeks 6, 7, 8 (Spring Break), 9:
<ul>
<li><a href="https://yaoyichi.github.io/spatial-ai/w6w7.1-deep-learning-cnn.pptx">Capturing Spatial Dependencies with Deep Neural Networks I - Deep Learning & CNN</a>, <a href="https://yaoyichi.github.io/spatial-ai/w6w7.1-deep-learning-cnn.pdf">PDF</a></li>
<li><a href="https://www.dropbox.com/s/yqbg4l4l24q333g/w6w7.1.5.intro_to_pytorch.pptx?dl=0">Introduction to PyTorch</a>, <a href="https://yaoyichi.github.io/spatial-ai/w6w7.1.5.intro_to_pytorch.pdf">PDF</a></li>
<li><a href="https://yaoyichi.github.io/spatial-ai/w6w7.2-object-detection-semantic-segmentation.pptx">Capturing Spatial Dependencies with Deep Neural Networks II - Deep Learning & CNN</a>, <a href="https://yaoyichi.github.io/spatial-ai/w6w7.2-object-detection-semantic-segmentation.pdf">PDF</a></li>
</ul>
</li>
<li>Week 10: Final project proposal presentation</li>
<li>Weeks 11, 12, 13:
<ul>
<li><a href="https://yaoyichi.github.io/spatial-ai/w9w10.1.trajectory-mining-detect.pptx">Capturing Temporal Dependencies with Deep Neural Networks - Trajectory Mining</a>, <a href="https://yaoyichi.github.io/spatial-ai/w9w10.1.trajectory-mining-detect.pdf">PDF</a></li>
<li><a href="https://yaoyichi.github.io/spatial-ai/w9w10.2-autoencoder.pptx">Autoencoder</a>, <a href="https://yaoyichi.github.io/spatial-ai/w9w10.2-autoencoder.pdf">PDF</a></li>
<li><a href="https://yaoyichi.github.io/spatial-ai/ww9w10.3-rnn.pptx">Recurrent Neural Networks I - LSTM</a>, <a href="https://yaoyichi.github.io/spatial-ai/w9w10.3-rnn.pdf">PDF</a></li>
<li><a href="https://yaoyichi.github.io/spatial-ai/ww9w10.4-rnn2.pptx">Recurrent Neural Networks II - GRU and others</a>, <a href="https://yaoyichi.github.io/spatial-ai/w9w10.4-rnn2.pdf">PDF</a></li>
</ul>
</li>
<li>Weeks 14:
<ul>
<li><a href="https://yaoyichi.github.io/spatial-ai/w11.1.spatial-ai-chiang-v4-usd.pptx">Spatial AI and Its Applications</a>, <a href="https://yaoyichi.github.io/spatial-ai/w11.1.spatial-ai-chiang-v4-usd.pdf">PDF</a></li>
</ul>
</li>
<li>Weeks 14, 15, 16:
<ul>
<li>Final project presentations</li>
</ul>
</li>
<!--
<li><a href="https://yaoyichi.github.io/spatial-ai/w2w3.3.MapReduce-1.pptx">Spatial Data Management using Big Data Platforms - MapReduce 1</a>, <a href="https://yaoyichi.github.io/spatial-ai/w2w3.3.MapReduce-1.pdf">PDF</a></li>
<li><a href="https://yaoyichi.github.io/spatial-ai/w2w3.4.MapReduce-2.pptx">Spatial Data Management using Big Data Platforms - MapReduce 2</a>, <a href="https://yaoyichi.github.io/spatial-ai/w2w3.4.MapReduce-2.pdf">PDF</a></li>
<li><a href="https://yaoyichi.github.io/spatial-ai/assignment-1-description.docx">Assignment 1</a>, <a href="https://yaoyichi.github.io/spatial-ai/assignment-1-description.pdf">PDF</a></li>
<li><a href="https://yaoyichi.github.io/spatial-ai/w2w3.5.intro-spark-geospark.pptx">Introduction to Spark and GeoSpark</a>, <a href="https://yaoyichi.github.io/spatial-ai/w2w3.5.intro-spark-geospark.pdf">PDF</a></li>-->
</ul>
</li>
<!--
<li>Week 4: <a href="https://yaoyichi.github.io/spatial-ai/w1-intro.pptx">Introduction to Spatial AI</a></li>
<li>Week 5: <a href="https://yaoyichi.github.io/spatial-ai/w1-intro.pptx">Introduction to Spatial AI</a></li>
<li>Week 6: <a href="https://yaoyichi.github.io/spatial-ai/w1-intro.pptx">Introduction to Spatial AI</a></li>
<li>Week 7: <a href="https://yaoyichi.github.io/spatial-ai/w1-intro.pptx">Introduction to Spatial AI</a></li>
<li>Week 8: <a href="https://yaoyichi.github.io/spatial-ai/w1-intro.pptx">Introduction to Spatial AI</a></li>
<li>Week 9: <a href="https://yaoyichi.github.io/spatial-ai/w1-intro.pptx">Introduction to Spatial AI</a></li>
<li>Week 10: <a href="https://yaoyichi.github.io/spatial-ai/w1-intro.pptx">Introduction to Spatial AI</a></li>
<li>Week 11: <a href="https://yaoyichi.github.io/spatial-ai/w1-intro.pptx">Introduction to Spatial AI</a></li>
<li>Week 12: <a href="https://yaoyichi.github.io/spatial-ai/w1-intro.pptx">Introduction to Spatial AI</a></li>
<li>Week 13: <a href="https://yaoyichi.github.io/spatial-ai/w1-intro.pptx">Introduction to Spatial AI</a></li>
<li>Week 14: <a href="https://yaoyichi.github.io/spatial-ai/w1-intro.pptx">Introduction to Spatial AI</a></li>
<li>Week 15: <a href="https://yaoyichi.github.io/spatial-ai/w1-intro.pptx">Introduction to Spatial AI</a></li>
-->
</ul>
<br>
<h3><strong>Course Description</strong></h3>
<p>The location of things in space and how they change over time is the key to understanding complex environmental phenomena and human-environmental interactions. A significant amount of data now contains location and time information, either explicitly, e.g., traffic sensors, air quality sensors, satellite imagery, or implicitly, e.g., images and text documents.</p>
<br>
<p>This course aims to explore the foundation and the state-of-the-art on 1) spatial data management and 2) machine learning & data mining technologies that can exploit the unique spatial data properties (e.g., autocorrelations) to solve real-world problems. This is a seminar course consisting of lectures and paper presentations. Specifically, this course has two main themes. The first theme explores current ways to store and manage spatial data, including topics in spatial databases, spatial Big Data platforms, and/or knowledge graphs & ontology. The second theme looks into how machine learning & data mining technologies solve real-world problems utilizing the unique spatial data properties, including topics in computer vision (e.g., object detection from overhead imagery), location time-series data prediction & forecasting (e.g., air quality prediction and traffic forecasting), and optionally natural language processing (e.g., toponym detection from documents). The course will include several programming assignments and a final project.
</p>
<br>
<h3><strong>Prerequisites</strong></h3>
<p>The students should have excellent knowledge in applied machine learning (e.g., can select an appropriate machine learning model for solving a problem at hand) and databases (e.g., can write SQL queries with the help of the internet) and solid programming skills. Some background in handling spatial data is a plus but not required.
</p>
</br>
<h3><strong>Learning Outcome</strong></h3>
<p>Students will be able to identify the role of spatial data and challenges in using them to solve a real-world problem. They will define the problem scope by identifying appropriate machine learning and data mining technologies and then leveraging the unique spatial data properties to solve the problem. For example, students will learn how to find and integrate spatial data from heterogeneous sources in the assignments. Then they will learn how to build machine learning or data mining methods to handle these spatial data for descriptive and predictive analysis.</p>
<br>
<h3><strong>Assessment</strong></h3>
<p>The course will include several programming assignments and a final project. The programming assignments evaluate the students' capability in completing individual tasks towards the final project. The final project evaluates the students' overall capability in combining all knowledge learned from this course. Specifically, the final project should show working artificial intelligence technologies to tackle a real-world problem involving spatial data.</p>
<br>
<h3><strong>Final Project Guidelines</strong></h3>
<p><b>MS/Senior Undergrad Students</b><br>
Write a comparison of selected state-of-the-art methods for solving a spatial AI problem (e.g., object detection from satellite imagery). <br>
<b>MS/PhD Students</b><br>
Develop a complete research work, which could be related to your research direction.</p>
</body>