Improving the mapping of coastal invasive species using UAV imagery and deep learning

Spartina anglica C. E. Hubb. is an invasive species of saltmarsh and mudflats in Ireland. It spreads quickly and can form extensive meadows, which can lead to the extinction of native plant and animal species. Traditional ground-based field surveys would be expensive for the scale of monitoring needed to formulate site-specific management strategies. Advanced mapping techniques, such as Unoccupied Aerial Vehicle (UAV) remote sensing and deep learning (DL), offer an opportunity to automatically map invasive species' occurrence. However, training a DL model requires lots of labelled data which can be prohibitively difficult and expensive to obtain. We implemented a DL semantic segmentation technique on UAV imagery of Spartina-invaded habitats and tested a range of hyperparameters-model architectures, encoder backbones and input image patch sizes-to determine an effective segmentation network structure. We also investigated applying data augmentation and pseudo-labelling techniques to increase the size of the labelled dataset. U-Net architecture with Inception-v3 as its backbone trained on 128 x 128-pixel image patches offered the best model performance: mean Intersection-Over-Union (mIOU) score = 0.832. The model trained on the combined augmented and pseudo-labelled data achieved an mIOU score of 0.712 on a test dataset, while there was a decrease of 0.158 in model performance when only the original labelled data were used. This result suggests the potential for using these techniques in creating more robust models. The proposed methodology demonstrates that the combination of UAV imagery and deep learning could be a promising tool for mapping the distribution of invasive plant species.