A Deep Learning Model for Global Camera Trap Labelling

Profile Display Name:

Benjamin Evans

E-mail Address:


Start Year

2018 (Cohort 5)

Research interests:

– Machine Learning
– Camera Trap Surveys

Hobbies and interests:
PhD Project
PhD Title

A Deep Learning Model for Global Camera Trap Labelling

Research Theme

Biodiversity and Ecology

Primary Supervisor
Primary Institution


Secondary Supervisor
Secondary Institution



Recent years have seen an increase in camera-trap survey monitoring by ecological researchers. Camera-trap surveys collect imagery of medium-large mammal species across a region of interest. Dependent on the activity in an areas and number of camera trap days a survey’s conducted, millions of images may be captured. Currently researchers label each image with species and behavioural information or enlist Citizen Science volunteers.
Developments in machine learning, specifically deep learning has provided promising methods for general image recognition and may be utilised to assist in the camera trap labelling. Current models and methods that have been developed do not generalise well past the dataset they’re trained on.
The aim of the following research is to produce a technique that’s able to generalise to new data from around the world. We predict that introducing bio-geography into the classifier will increase the accuracy along with using multiple models trained for similar sub-species with an overarching model guiding which sub-species model should be used to classify.

Policy Impact
Background Reading
Conferences and Workshops
  • IDA 2020 (April 2020).
  • ECML 2020 SoGood (September 2020). Talk: Reasoning about Neural Network Activations: An Application in Spatial Animal Behaviour from Camera Trap Classifications.
  • CSBPS 2020 (June 2020). Talk
  • CSBPS 2019 (March 2019). Poster
Social Links
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