MarshCover: A Web-based Tool for Estimating Vegetation Coverage in Marsh Images Using Convolutional Neural Networks


  • Lucas Wayne Welch University of North Florida
  • Xudong Liu University of North Florida



Machine Vision, Convolutional Neural Networks, Image Recognition, Web Application


Marsh ecosystems are some of our most important, serving many crucial ecological functions. They are also rapidly changing, and it is vital for scientists to track these changes. This includes monitoring the health of marshes via estimating ground coverage by various grass species, a task that requires human labor to look at marsh images and manually estimate the coverage. Clearly, this task can be quite formidable. To automate this standard yet laborsome process, we develop
a web-based system, called MarshCover, that automates the process of estimating vegetation density in marsh images using convolutional neural networks (CNNs). MarshCover, to the best of our knowledge, is the first such tool available to biologists that uses CNNs for marsh vegetation estimations. In order to select effective CNN models for our MarshCover server, we conduct extensive empirical analyses of three distinct CNNs, i.e., LeNet-5, AlexNet and VGG-16, to compare their performances on a public marsh image dataset. To this end, we address two classification problems for this paper: a binary classification problem classifying points as vegetated and unvegetated, and a multiclass classification problem that classifies points into either an unvegetated class or one of five different species classes. Our experiments identify the VGG16 model as the best classifier to embed in MarshCover for both the binary classification problem and the full classification problem with a two model classifier (called two-shot). These two classifiers had accuracies on test data of 90.76% and 84% respectively. MarshCover is publicly available online.




How to Cite

Welch, L. W., & Liu, X. (2023). MarshCover: A Web-based Tool for Estimating Vegetation Coverage in Marsh Images Using Convolutional Neural Networks. The International FLAIRS Conference Proceedings, 36(1).



Main Track Proceedings