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MS Final Exam – Laurel Hopkins

Remote Sensing Transfer Learning for Species Distribution Modeling

Species distribution models link species observations to environmental variables. Current methods for collecting environmental variables generally consist of field-based methods or basic summary statistics of remotely sensed data. As the spatial extent of models expand to map continental and global patterns, field-based methods are no longer be feasible. While remotely sensed data offer a powerful alternative to physically collected features, summary statistics are rudimentary when compared to state-of-the-art computer vision techniques. We propose to replace coarse summaries with deep features extracted from high-resolution aerial images to better characterize habitats. We use a transfer learning approach to tune a Convolutional Neural Network to learn features relevant to species distribution modeling. We evaluate our approach using the citizen science based eBird dataset.

Major Advisor: Rebecca Hutchinson
Committee: Fuxin Li
Committee: Alan Fern

Monday, December 3, 2018 at 9:00am to 11:00am

Kelley Engineering Center, 1005
110 SW Park Terrace, Corvallis, OR 97331

Event Type

Lecture or Presentation

Event Topic


College of Engineering, Electrical Engineering and Computer Science
Contact Name

Calvin Hughes

Contact Email

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