Maryland Shorebird ID Model Object Detection Dataset and Pre-Trained Model by Amber Ewings Workspace

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Explore Dataset

This model aims to accurately identify several different species of shorebirds and colonial waterbirds commonly found on the Eastern Shore of Maryland. The species included in this model range from endangered to non-endangered within the state of Maryland. For instance, the royal tern, common tern, and black skimmer are all classified as endangered in the state making this model particularly useful to citizen scientists interested in identifying which birds are endangered and which are not when they are in the field, like the herring gull, black-backed gull, and others. There is already a large birding community within Maryland’s Coastal Bays interested in the state of our waterbirds, and this model would be great for them to use while exploring Maryland’s Coastal Bays. This model also serves as a important component to my final St. Mary's Project where I'm documenting how habitat loss in Maryland's Coastal Bays impacts royal tern, common tern, and black skimmer populations.

The majority of this data was collected from iNaturalist and the U.S. Fish and Wildlife Services Digital Library.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            maryland-shorebird-id-model_dataset,
                            title = { Maryland Shorebird ID Model Dataset },
                            type = { Open Source Dataset },
                            author = { Amber Ewings Workspace },
                            howpublished = { \url{ https://universe.roboflow.com/amber-ewings-workspace/maryland-shorebird-id-model } },
                            url = { https://universe.roboflow.com/amber-ewings-workspace/maryland-shorebird-id-model },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { apr },
                            note = { visited on 2024-04-29 },
                            }