Can citizen science and social media images support the detection of new invasion sites? A deep learning test case with Cortaderia selloana Cardoso, Ana Sofia Malta-Pinto, Eva Tabik, Siham August, Tom Roy, Helen E. Correia, Ricardo Vicente, Joana R. Vaz, Ana Sofía Artificial intelligence Convolutional neural networks Computer vision Deep learning has advanced the content analysis of digital data, unlocking opportunities for detecting, mapping, and monitoring invasive species. Here, we tested the ability of open source classification and object detection models (i.e., convolutional neural networks: CNNs) to identify and map the invasive plant Cortaderia selloana (pampas grass) in mainland Portugal. CNNs were trained over citizen science images and then applied to social media content (from Flickr, Twitter, Instagram, and Facebook), allowing to classify or detect the species in over 77% of situations. Images where the species was identified were mapped, using their georeferenced coordinates and time stamp, showing previously unreported occurrences of C. selloana, and a tendency for the species expansion from 2019 to 2021. Our study shows great potential from deep learning, citizen science and social media data for the detection, mapping, and monitoring of invasive plants, and, by extension, for supporting follow-up management options. 2024-09-04T11:46:48Z 2024-09-04T11:46:48Z 2024-04-12 journal article Cardoso, A.S. et. al. 81 (2024) 102602. [https://doi.org/10.1016/j.ecoinf.2024.102602] https://hdl.handle.net/10481/93944 10.1016/j.ecoinf.2024.102602 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier