Citizen Science and Machine Learning for Research and Nature Conservation: The Case of Eurasian Lynx, Free-ranging Rodents and Insects

TL;DR

This paper explores how citizen science and machine learning, particularly convolutional neural networks, can enhance data processing and analysis in wildlife conservation efforts, focusing on Eurasian Lynx, rodents, and insects.

Contribution

It introduces the integration of citizen science with machine learning techniques to improve data labeling and analysis in conservation research, addressing challenges in data volume and volunteer engagement.

Findings

Machine learning automates image recognition for species identification.

Citizen scientists can assist in data labeling, increasing engagement.

Combined approaches expedite conservation research workflows.

Abstract

Technology is increasingly used in Nature Reserves and National Parks around the world to support conservation efforts. Endangered species, such as the Eurasian Lynx (Lynx lynx), are monitored by a network of automatic photo traps. Yet, this method produces vast amounts of data, which needs to be prepared, analyzed and interpreted. Therefore, researchers working in this area increasingly need support to process this incoming information. One opportunity is to seek support from volunteer Citizen Scientists who can help label the data, however, it is challenging to retain their interest. Another way is to automate the process with image recognition using convolutional neural networks. During the panel, we will discuss considerations related to nature research and conservation as well as opportunities for the use of Citizen Science and Machine Learning to expedite the process of data preparation, labelling and analysis.