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Remote Sensing for Disaster Response
#  ## Program Overview #### Imagine coordinating a response after the chaos of a hurricane or the challenges of a famine lasting years, these big problems require big data to solve. With airplanes and satellites, we collect mountains of data of affected regions but who looks at this data? How do we turn this data into a physical response? The program’s goal is for participants to explore, leverage, and transform open source information and imagery collected from drones, airplanes, helicopters, and satellites to generate actionable intelligence to support a disaster or humanitarian response. ## Students will be exposed to three main components: 1) feature extraction from raw data, 2) classification via machine learning techniques, and 3) data products for decision makers. The program will explore tools and techniques using real world operational data collected from across the globe. BWSI Remote Sensing program will offer students the opportunity to explore the exciting intersection of data science and disaster response. The program consists of two components: (1) online course from January to May, open to all interested and committed students; and (2) a four-week virtual summer program. During the course, the students will learn to understand the basics of Python, Git, GIS, machine learning, and image processing through a series of online teaching modules. Students will explore real world datasets featuring disaster imagery from both satellites and aerial platforms. Students in this course will develop experience in an area of data science that is poised to play a critical role in understanding our world. ### Online Course Prior to the virtual summer course, students will be required to complete an online course which contains important introductory material. The online course will give the students a strong foundation required to successfully complete the four-week summer course. In addition to foundational introductory material, the online course includes discussion of different use cases and expose students to real world challenges and applications of the coursework. | | | | |---|---|---| |Computer Science|Data Science|Real World Data| |Getting started with Python|Advanced NumPy|Civil Air Patrol| |Git & GitHub management|Simple image classification|USGS Landslide Assessment| |Machine learning perspectives|Introduction to Web Services|Zanzibar Mapping Initiative| ### Summer Course The four-week summer component of aims to guide students through the processing of designing experiments to evaluate primarily text-based content. Daily course material, case studies, guest lectures, and small-group projects will expose students to challenges across technical domains. The following is a rough outline for the summer course: ### Week 1: Introduction to GIS • Review of Python fundamentals • Introduction to pandas, geopandas, geospatial information systems • Research questions, hypotheses and objectives • Working with open source tools and data ### Week 2: Analysis of Geospatial Data • Introduction to classifiers and data science • Spatial analysis and networks • Geospatial data sources and how to work with them ### Week 3: Introduction to Image Processing • Fundamentals of images and metadata • Multispectral imaging • Satellite images and analysis ### Week 4: Image Classification and Decision Making • Classify images based on contents • Intro to optimization • Data-driven decision making --- |
Computer Science Summer Institute – Intermediate Track
Get a head start on university computer science concepts
Advance your skills in programming and data analysis
The Computer Science Intermediate Track provides a unique combination of coding boot camp, and lab touring experiences, as well as UCLA coursework covering critical concepts and skills in computer programming related to statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, health data, geographical data, and social networks.
Computer science experience with basic programming skills (python) is required. Knowledge in basic matrix analysis, probability, and statistics is preferred.
Computer Science Summer Institute – Intermediate Track
Curriculum Overview
The fundamental question this course aims to address is how does one analyze real-world data so as to understand the corresponding phenomenon. Students will learn critical concepts and skills in computer programming related to statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, health data, geographical data, and social networks. Planned topics include machine learning, data analytics, and statistical modeling classically employed for prediction. The program will be a blend of theoretical and practical instruction, providing a comprehensive, hands-on overview of the Data Science domain.
Hands-on projects will form the bulk of the work for the class and will seek to teach students the data science lifecycle: data selection and cleaning, feature engineering, model selection, and prediction methodologies.
Commuter-Only Program
Computer Science Summer Institute – Intermediate Track is a commuter program, there is NO on-campus housing available for the Computer Science Intermediate program through the institute program or UCLA Summer Sessions. Additionally, please note that only students aged 17 and older as of June 24th, 2024 are eligible to sign a housing contract, and not all program dates correspond to when contracts are available. Students interested in exploring this option should review information at UCLA Summer Housing.
Participants of the Computer Science Summer Institute – Intermediate Track must commute to the UCLA campus each day of the program. Specific location information (e.g. classroom) will be provided to enrolled students closer to the start of the program.
Parking
Summer Sessions parking permits will be available on a first-come, first-served basis. Students have the option to purchase a summer term permit or a daily permit.
Please review the Transportation and Parking Services web page and read the “Summer Quarter Parking (All Students)” section for more information on all permit types, including cost.
Coursework and Grading
Coursework
Computer Science 97; 4 units
Grading Basis
Students will receive a letter grade upon completion. See University Credit, Grades and Transcripts for more information about academic credit.
In order to successfully complete the program, students must not have more than 1 excused or unexcused absence.
Yizhou Sun
Associate Professor
Yizhou Sun is an associate professor at the department of computer science at UCLA. She received her Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2012. Her principal research interest is on mining graphs/networks, and more generally in data mining, machine learning, and network science, with a focus on modeling novel problems and proposing scalable algorithms for large-scale, real-world applications. She is a pioneer researcher in mining heterogeneous information network, with a recent focus on deep learning on graphs/networks.
Yizhou has over 100 publications in books, journals, and major conferences. Tutorials of her research have been given in many premier conferences. She received 2012 ACM SIGKDD Best Student Paper Award, 2013 ACM SIGKDD Doctoral Dissertation Award, 2020 ACM BCB Best Student Paper Award, 2013 Yahoo ACE (Academic Career Enhancement) Award, 2015 NSF CAREER Award, 2016 CS@ILLINOIS Distinguished Educator Award, 2018 Amazon Research Award, and 2019 Okawa Foundation Research Grant.
Advisory Board
Eli Gafni
Professor
Dr. Gafni was born in Tel-Aviv, Israel. He received his Bs.C from the Technion, Israel in 1972, and M.S. and Ph.D. in Electrical Engineering in 1979 and 1982, from the University of Illinois at Urbana Champaign, and M.I.T, respectively. In 1982 he joined the UCLA computer science faculty. Dr. Gafni was the recipient of a 1983 IBM Faculty Development Award, and a 1984 NSF Presidential Young Investigator Award. His research interests include distributed algorithms, mathematical programming with application to distributed routing and control of data networks, and computer science theory.
Richard Korf
Professor
Richard Korf is a Professor of computer science at UCLA. He received his B.S. from M.I.T. in 1977, and his M.S. and Ph.D. from Carnegie-Mellon University in 1980 and 1983, respectively, all in computer science. From 1983 to 1985, he served as Herbert M. Singer Assistant Professor of Computer Science at Columbia University. His research is in the areas of problem-solving, heuristic search, and planning in artificial intelligence. He is the author of “Learning to Solve Problems by Searching for Macro-Operators” (Pitman, 1985).
He serves on the editorial boards of Artificial Intelligence, and the Journal of Applied Intelligence. Dr. Korf is the recipient of a 1985 IBM Faculty Development Award, a 1986 NSF Presidential Young Investigator Award, the first UCLA Computer Science Department Distinguished Teaching Award in 1989, the first UCLA School of Engineering Student’s Choice Award for Excellence in Teaching in 1996, and the Lockheed Martin Excellence in Teaching Award in 2005. He is a Fellow of the American Association for Artificial Intelligence.
EPS 7, Introduction to Climate Change, Summer A 2024
EPS 7 is online in summer session A of 2024. The lectures and exams will be on bCourses. The lectures will be asynchronous: they will post at the regular class time, but you can watch them at your leisure. On the other hand, the exams on bCourses will be synchronous: you must log in online at the specified time.
Note that this course covers the same material as the semester-long version of EPS 7, but in a third the time. Therefore, every Monday, Wednesday, and Friday will contain the same amount of content as a week of the semester-long version. This also means that homework assigned on a Monday is due on Wednesday just two days later, and likewise for homework assigned on Wednesdays and Fridays. And late homework is not accepted, so this summer course requires a high level of uninterrupted effort. Even with this intensity, however, the course will be just as much fun as the semester-long version!
Overview
This course covers the physical processes that determine Earth's past, present, and future climate, with a particular focus on the essentially irreversible climate change (a.k.a., global warming) caused by the burning of coal, oil, and natural gas. Topics will also include the estimation of future warming and impacts, the Earth resources that can be used to combat climate change, and the policies being used to shift towards the use of those resources.
Time
Fridays 2:00-3:00 for exams
Lectures released mornings of M, W, and F
Location
In the bCourses Media Gallery
Required text
None
Readings will be posted online
Prerequisites
None
Grading
Homework 30% (on bCourses, due every M, W, and F)
Midterm I 20% (on bCourses, May 31, 2pm Pacific)
Midterm II 20% (on bCourses, June 14, 2pm Pacific)
Final 30% (on bCourses, June 28, 2pm Pacific)
Letter grades are 90-100% for an A, 80-90% for a B, 70-80% for a C, etc., with each decile split into equal thirds to determine + and -.
Homework
Late homework is not accepted
To avoid zeros, aim to submit every assignment well before its deadline
Exams
Exams are closed-book
Taking of regularly scheduled exams is mandatory
Only exception is documented medical incapacitation
Do not enroll if unable to attend the exams
Copyright
All course materials are copyrighted
This includes lectures, slides, videos, homework, and exams
Course materials are for your own use; they may not be distributed
Distribution or posting of course material is a violation of law and University policy
To avoid sanctions, do not put course material on websites or cloud services
Honor code
"As a member of the UCB community, I act with honesty, integrity, and respect for others."
The honor code is taken seriously in EPS 7
Academic misconduct or a violation of course policy will result in sanctions
Professor (romps@berkeley.edu)
David Romps
Readers (eps7help@gmail.com)
Toireasa-Marie O'Rourke
Ryan Yohler
Office hours
Log into Zoom with your @berkeley.edu account using the SSO login
Monday, 12-1 Pacific, Ryan, Zoom room: 468 9723 434
Monday, 2-3 Pacific, Professor Romps, Zoom room: 934 5798 5513
Tuesday, 12-1 Pacific, Ryan, Zoom room: 468 9723 434
Tuesday, 5-6 Pacific, Toireasa-Marie, Zoom room: 923 8218 423
Wednesday, 2-3 Pacific, Professor Romps, Zoom room: 934 5798 5513
Thursday, 12-1 Pacific, Ryan, Zoom room: 468 9723 434
Thursday, 5-6 Pacific, Toireasa-Marie, Zoom room: 923 8218 423
Friday, 2-3 Pacific, Professor Romps, Zoom room: 934 5798 5513
Sunday, 11-12 Pacific, Toireasa-Marie, Zoom room: 923 8218 423
Syllabus
05/20
Joule and Watt: A tale of two Jameses
Energy on the move: How it gets from A to B
Fun with units: Meters and thermometers
Homework 1 assigned
05/22
Wien's law: The color of light
Stefan-Boltzmann law: You are glowing, literally
Mercury: Warm and toasty
Homework 1 due
Homework 2 assigned
05/24
Mars: A little chilly
Earth's atmosphere: What is it?
Clausius-Clapeyron: Water, water, everywhere
Homework 2 due
Homework 3 assigned
05/27
No class
05/29
Lapse rate: It is cold up here!
Radiative transfer: Gases glow, too
Greenhouse gases: The Earth's clothing
Homework 3 due
Homework 4 assigned
05/31
Midterm 1
06/03
Discovery of global warming: A short history
Forcing and feedback: Your best life now
Earth's feedbacks: Calculating climate sensitivity
Homework 4 due
Homework 5 assigned
06/05
Cloud taxonomy: Name that cloud
Fossil fuels: Where did this stuff come from?
Drill baby drill: How much have we burned?
Evidence of warming: Is it getting hot in here?
Homework 5 due
Homework 6 assigned
06/07
Ocean acidification: Where does the carbon go?
Climate models: Supercomputers to the rescue
The IPCC: How to win a Nobel Prize
Homework 6 due
Homework 7 assigned
06/10
Other gases: Laughing gas and hairspray
Scary feedbacks: Stuff that could burn
Paleoclimate: The past as guide to the future
Homework 7 due
Homework 8 assigned
06/12
Ice and sea level: Where to invest in property
Superstorms: The revenge of Clausius-Clapeyron
Future Earth: Spacesuits required
Homework 8 due
Homework 9 assigned
06/14
Midterm 2
06/17
Biomass power: Enough room for food and fuel?
Hydro power: What is left to harness?
Nuclear power: Too costly and dangerous?
Homework 9 due
Homework 10 assigned
06/19
No class
06/21
Wind power: Mining the sky
Solar power: Ready to save the day?
Domestic policy: ITC, PTC, alphabet soup
Homework 10 due
Homework 11 assigned
06/24
International agreements: Rio, Kyoto, and Paris
Carbon tax: The simple policy solution
Who obstructs action: Follow the money
Climate rights movement: What will your role be?
Homework 11 due
Homework 12 assigned
06/26
No class
Homework 12 due
06/28
Final
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