Submission information
Submission Number: 199
Submission ID: 4534
Submission UUID: fa50435a-d4d6-4c6f-9291-3254a57331b0
Submission URI: /form/project
Created: Wed, 05/08/2024 - 10:08
Completed: Wed, 05/08/2024 - 10:08
Changed: Wed, 05/08/2024 - 10:08
Remote IP address: 73.159.82.51
Submitted by: Timothy Becker
Language: English
Is draft: No
Webform: Project
| Project Title | Image Based Ecological Water Monitoring Models |
|---|---|
| Program | CAREERS |
| Project Leader | Timothy Becker |
| tbecker@conncoll.edu | |
| Mobile Phone | |
| Work Phone | 8604392017 |
| Mentor(s) | |
| Student-facilitator(s) | |
| Mentee(s) | |
| Project Description | Image Based Ecological Instream Water Monitoring Models The CT DEEP Water Monitoring and Assessment Program (MAP) has developed and implemented a low-cost and innovative approach to evaluate instream flows and connectivity using trail camera images associated with spatial location and time. MAP has demonstrated the effectiveness of this approach by evaluating flow regimes and connectivity with anthropogenic-based water withdrawals: https://doi.org/10.1002/rra.3689. Labeling thousands of images is time intensive however and even the best user interface will cause significant user eye fatigue. We have previously explored a deep learning approach with minimal feature engineering to predict the appropriate label and are now pursuing development of multiple semi supervised weak-classifiers trained on features such as river bank lines, foliage bounding boxes and stream horizon lines to feed an ensemble classifier. We have implemented our prior methods using the python programming language relying upon the established open source OpenCV, piexif, tensorflow and scikit-learn packages for development: https://github.com/timothyjamesbecker/eco_image. Currently we have CNN models that perform well but will build new Vision Transformer models to compare using the new keras 3 API. We plan to finish our preliminary work this Summer 2024 with the help of two paid Conn undergraduate student researchers that have been working on OpenCV projects for this past year in preparation. The result will be a manuscript as well as an open source github pip package for processing this type of river data and to use in ference for assistance with classifying new images. |
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| Student Research Computing Facilitator Profile | |
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| Preferred Start Date | |
| Start as soon as possible. | No |
| Project Urgency | Already behind3Start date is flexible |
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| What will the student learn? | |
| What will the mentee learn? | |
| What will the Cyberteam program learn from this project? | |
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| Notes | |
| What is the impact on the development of the principal discipline(s) of the project? | |
| What is the impact on other disciplines? | |
| Is there an impact physical resources that form infrastructure? | |
| Is there an impact on the development of human resources for research computing? | |
| Is there an impact on institutional resources that form infrastructure? | |
| Is there an impact on information resources that form infrastructure? | |
| Is there an impact on technology transfer? | |
| Is there an impact on society beyond science and technology? | |
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