Aerial News Stories Computer Vision Project

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Project overview: This dataset is intended to aid in the scraping of news sites to identify missions of newsgathering helicopters.

Descriptions of each class type So far we have identified the following classes of "stories": traffic: this includes crashes, if there are cars in the photo, this is a safe bet fire water medevac rescue workers: looking for high-viz vests fallen trees weather night crime scene: look for little yellow cones night: whether it is night vision or traffic at night other aerial: sometimes you can't tell exactly what the story is...

Links to external resources Scraped @Chopper4Brad 08-21 - 08-22 Scraped NBC4 Local last month

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{
                            aerial-news-stories_dataset,
                            title = { Aerial News Stories Dataset },
                            type = { Open Source Dataset },
                            author = { HelicoptersofDC },
                            howpublished = { \url{ https://universe.roboflow.com/helicoptersofdc/aerial-news-stories } },
                            url = { https://universe.roboflow.com/helicoptersofdc/aerial-news-stories },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { aug },
                            note = { visited on 2024-07-27 },
                            }
                        

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Last Updated

2 years ago

Project Type

Classification

Subject

aerial-photos

Views: 41

Views in previous 30 days: 5

Downloads: 2

Downloads in previous 30 days: 1

License

CC BY 4.0

Classes

B429 crime scene fallen trees fire medevac night other aerial rescue workers traffic water weather