YoloDroneBuildingsCorners Object Detection Dataset and Pre-Trained Model by reinzler

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Explore Dataset

Here are a few use cases for this project:

Urban Planning and Development: The model can be used by urban planners to analyze and understand the layout of buildings within a city. This understanding can help in devising effective road networks, planning new infrastructures, or restructuring existing ones.
Drone Navigation and Mapping: The data can be useful in programming drones for better navigation during aerial surveys or deliveries. Specifically, it can be used to map out routes that avoid buildings or to ensure the drone is following the correct path.
Real Estate Evaluation: The model can be employed to obtain detailed information about buildings, providing data for property evaluations or helping investors make informed decisions. For example, the size and layout of rooftops can be analyze to assess the suitability for solar panels installation.
Emergency Services: Firefighters and emergency services can benefit from the model to plan out access routes and devise strategies. For example, in case of fires, understanding the structure of rooftops might help in devising a strategy to safely and effectively combat the blaze.
Climate Studies: Researchers could use this model to study the urban heat island effect. By understanding the layout of rooftops, it can help determine how cities and other urban areas absorb and re-emit heat differently than rural areas.

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.

YOLOv8

This project has a YOLOv8 model checkpoint available for inference with Roboflow Deploy. YOLOv8 is a new state-of-the-art real-time object detection model.

Cite This Project

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

@misc{
                            yolodronebuildingscorners_dataset,
                            title = { YoloDroneBuildingsCorners Dataset },
                            type = { Open Source Dataset },
                            author = { reinzler },
                            howpublished = { \url{ https://universe.roboflow.com/reinzler-fkcdd/yolodronebuildingscorners } },
                            url = { https://universe.roboflow.com/reinzler-fkcdd/yolodronebuildingscorners },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-04-18 },
                            }