Random Forest Object Detection Dataset and Pre-Trained Model by AU

Images

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Explore Dataset

Here are a few use cases for this project:

Smart Parking Assistance: Utilize the Random Forest computer vision model to identify available parking spaces in real-time under tree shades or near shelters, providing drivers with accurate and convenient parking recommendations.
Environmental Analysis: Use the model to monitor ecosystems in urban areas by identifying and tracking the number of trees and shelters, assessing the proportion of green spaces and built structures, and analyzing the relationships between vehicles, trees, and shelters for urban planning purposes.
Traffic Management and Road Safety: Implement the Random Forest model in traffic monitoring systems to detect vehicle density, tree obstructions, or unexpected shelters on the road, allowing for real-time traffic flow adjustments and timely notifications to drivers or traffic officers.
Disaster Response and Recovery: Use the Random Forest model to assess damage to infrastructure caused by natural disasters, such as identifying fallen trees or damaged shelters that may be blocking roads or posing threats to vehicles, and aiding in efficient recovery and clean-up efforts.
Augmented Reality Navigation: Integrate the model into augmented reality applications for pedestrians and drivers, recognizing cars, trees, and shelters in the user's vicinity and providing location-specific information or suggestions based on the identified objects.

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{
                            random-forest_dataset,
                            title = { Random Forest  Dataset },
                            type = { Open Source Dataset },
                            author = { AU },
                            howpublished = { \url{ https://universe.roboflow.com/au-lxtmc/random-forest } },
                            url = { https://universe.roboflow.com/au-lxtmc/random-forest },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { mar },
                            note = { visited on 2024-05-03 },
                            }