SOUTHERN AFRICA 003 Object Detection Dataset and Pre-Trained Model by Kezala Jere

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Explore Dataset

Here are a few use cases for this project:

Archeological Research: This model can be employed in archeological research where it can help in identifying and classifying different elements of ancient settlements in Southern Africa. It could specifically help archeologists identify walls, buildings, and distinct materials such as rocks and soils used in the constructions.
AI-based tourism guides: "SOUTHERN AFRICA 003" can be utilised in creating intelligent tourism guide applications. These apps can use the model to recognise ancient structures, vegetation, and other elements, providing tourists with real-time relevant information about the sites they are visiting.
Cultural Heritage Preservation: The model can assist in preserving cultural heritage by identifying and documenting ancient settlement patterns, structures and landscapes. It can be used to monitor the condition of these historical sites, detecting changes and possible damages over time.
Education and Virtual Reality: This model can be very useful in education, especially in creating immersive VR experiences. Based on the recognition outputs, designers can reconstruct ancient Southern African settlements in an interactive digital format for learning purposes.
Urban Planning and Sustainability: By understanding the ancient settlement walls and their consistency with the surrounding environment (rocks, soils, vegetation), urban planners may study sustainability practices from these historical structures, which could influence modern sustainable architecture and city planning.

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{
                            southern-africa-003_dataset,
                            title = { SOUTHERN AFRICA 003 Dataset },
                            type = { Open Source Dataset },
                            author = { Kezala Jere },
                            howpublished = { \url{ https://universe.roboflow.com/kezala-jere/southern-africa-003 } },
                            url = { https://universe.roboflow.com/kezala-jere/southern-africa-003 },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { dec },
                            note = { visited on 2024-07-27 },
                            }