Bushfire by smoke Object Detection Dataset and Pre-Trained Model by Kaplan Business School

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Explore Dataset

Here are a few use cases for this project:

Early Detection and Monitoring of Bushfires: This computer vision model can be used by fire departments or environment agencies to help identify the early signs of bushfires by analyzing smoke patterns and clouds in satellite images, allowing them to track, monitor, and allocate resources to potential bushfires more efficiently.
Smoke Pollution Analysis: Environmental research organizations can use this model to monitor and analyze air quality and pollution levels caused by smoke from bushfires. Data gathered can help guide policy decisions and develop strategies to minimize the effects of smoke pollution on the environment and public health.
Enhancing Weather Forecasting: Meteorological departments or weather forecasting agencies can integrate this model's capabilities to improve their analysis of weather patterns. Identifying different types of smoke, clouds and potential bushfires can significantly influence weather forecasts and inform the public regarding potential changes in air quality.
Wildlife and Habitat Conservation: Wildlife organizations and national parks can use this model to track the impact of bushfires on the flora and fauna of natural habitats. By detecting potential bushfire-prone areas, they can take preventive measures to protect vulnerable species and ecosystems.
Improved Emergency Response: Government agencies responsible for disaster management can use the model's capabilities to create a more efficient and swift response to bushfire emergencies. By successfully identifying areas where bushfires are occurring or are more likely to occur, the model can help optimize the deployment of emergency personnel and resources to the affected 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.

YOLOv5

This project has a YOLOv5 model checkpoint available for inference with Roboflow Deploy. YOLOv5 is a proven and tested, production ready, 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{
                            bushfire-by-smoke_dataset,
                            title = { Bushfire by smoke Dataset },
                            type = { Open Source Dataset },
                            author = { Kaplan Business School },
                            howpublished = { \url{ https://universe.roboflow.com/kaplan-business-school-ghxyj/bushfire-by-smoke } },
                            url = { https://universe.roboflow.com/kaplan-business-school-ghxyj/bushfire-by-smoke },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { feb },
                            note = { visited on 2024-06-18 },
                            }