Computer Vision Methods for Ecology

Get access to expert instruction in the latest tools for applied Computer Vision, with the contextual framing of ecological research needs.

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What You'll Study

Frame a scientific or ecological research question as a computer vision problem.
- What is the data?
- What is the computer vision task? (classification, detection, tracking, etc.)
- How will solving the computer vision task lead to an answer to my research question? What additional steps will be needed?
Review relevant Computer Vision literature.
Curate a representative dataset to prototype a solution to your computer vision problem, and make well-informed choices about how to spend resources – i.e. what data to annotate, when to use weak labels, how to make the most of your time and money.
Determine how to split your data for training and evaluation based on your required output and target use case.
Use existing well-maintained open-source codebases to train baseline computer vision models, adapt data loaders and model architectures to fit your data.
Learn how to design computer vision experiments with a focus on representative model evaluation to assess how well methods will work for your target outcome.

Program Details

Dates: Remote: January 13th to 15th, 2027

In person: January 18th to 29th, 2027
Available Formats: Professional Training (SMSC 0536, 9 CEUs)
Cost: Professional Training: $2,823.60 (includes shared room and dining package)
Deadlines: Apply by June 13, 2026

Not sure this program is right for you?

Check out Managing Ecological Data in R: Introduction to Data Science and the Art of Wrangling for Ecologists - Online.

Meet the Faculty

Sara Beery

Dr. Beery is an assistant professor at MIT EECS' Faculty of AI and Decision Making and CSAIL. Her research focuses on building computer vision methods that enable global-scale environmental and biodiversity monitoring.

Eric Orenstein

Senior Fellow
King's College London

Dr. Orenstein is a Senior Research Fellow at King's College London in the Department of Informatics. His work combines computer vision and autonomous decision making to enable creative sampling strategies in remote marine environments.

Course Content

Computer vision (CV) is significantly accelerating ecology research by automating the analysis of raw images and video from camera traps, drones, and satellites. While ecologists often have training in statistics and programming, they are rarely exposed to the software engineering, machine learning and CV skills necessary to analyze large sets of visual data on their own. The Workshop on Computer Vision Methods for Ecology (CV4E), is an intensive two-and-a-half-week workshop designed to fill this educational gap. Our mission is to empower ecologists to efficiently process their existing data, design new studies around CV, and scale their research to larger datasets.

The workshop is built on four key pillars: (1) Teach applied CV as a tool for ecological research by providing instruction and tools specifically designed for ecologists, built around real ecological data. (2) Empower ecologists to build their own CV-based systems by building intuition, skills, and confidence by having them work with their own data to address questions about the natural world. (3) Grow the CV-for-Ecology community, a hub where expert ecologists and leading CV scientists can exchange ideas and best practices to address real-world challenges in conservation and sustainability. (4) Provide necessary computational resources to store data, collect annotations, train models, and host solutions during the school.

CV4E is a full-immersion course composed of classroom training and hands-on projects with one-on-one mentorship. The students will be taught the rudiments of computer vision and how to train and evaluate computer vision models on their own data to help answer specific ecological research questions. Students will leave with a working tool, a grasp of the underlying concepts, and the ability to tackle diverse ecological problems with computer vision. School participants will also develop a network of computer vision researchers with whom they can engage and collaborate. Participants can expect access to GPU-accelerated cloud compute for the duration of the course.

Course Format

The format is three days of remote work and two-weeks in person geared to early career scientists and graduate students. Participants will arrive with data they have collected to address an ecological research question. Leading up to the class, instructors will help students get their data prepared for the CV techniques they will experiment with. Students should anticipate dedicating most of each day to the class. They will listen to lectures, hear from guest speakers, participate in group activities, and have intensive work sessions in small groups with a dedicated instructor.

What’s Included

The total cost of the program includes:

A shared room at the SMSC Residence Hall (single rooms available for extra cost)
Full meal package including 3 meals/day and 2 coffee/tea breaks (Mon-Fri only) at the SMSC Dining Commons
Access to remote GPU-accelerated compute and storage
In-person lectures and group activities
Small group work with dedicated instructors focused on thematically similar projects
Optional weekend social outings

Software and Programs

All work will be conducted in Python, the lingua franca of modern machine learning. Students will learn to use the VSCode integrated development environment to access remote compute, train CV models, and evaluate results. We expect students to learn the rudiments of Python before arriving on-site. Instructors are available to help students in that process and we have curated a list of online resources to assist. Students are also encouraged to seek out intro Python courses at their home institutions, where available.

Financial Aid

We have limited financial aid available and will consider student needs on a case-by-case basis. Please fill in the appropriate fields in the application to be considered for financial support. We believe no qualified student should be turned away due to financial inaccessibility and will do our best to make aid available. If you have any particular questions, please email us at [email protected] early in the application period.

Applying to this Course

As with all our other programs, you’ll need to create a profile, then complete an application to this course through our application site HERE. However, there are some additional steps required of those looking to complete an application to this course. Applicants will need to complete all the following steps to be considered:

Create a profile and complete an application on our application website. You’ll need the following materials ready when applying on that site:
- Your CV. This will be uploaded when you create your profile in the system.
- A “statement of interest”. This will be uploaded with your application. For this course, the statement should include the following 2 components uploaded as a single PDF document:
  1. 1. A 1-page project proposal (11pt font, 1″ margins) addressing the following:
      - Research problem — What question are you interested in and how would computer vision methods better enable you to address it?
      - Data — What type of data do you plan to work with? Do you have the data already in hand? How much data do you have, how much will you have by the time the summer school will start? Is the data labeled already, or do you need to develop a labeling plan?
      - Impact/Outlook — What is the likely impact of your research for science, policy, education and conservation?
    2. A 1-page Personal Statement (11pt font, 1″ margins) describing your accomplishments, skills and career objectives.
- A link to a github repository with a programming example. This can be in Python or R. You’ll be asked to provide this link toward the end of the application.
Provide a description of your data using this FORM:
Have a professional letter of reference sent directly to [email protected] by your referee before the application deadline.

Email [email protected] with any questions.

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