# Māori Stone Research

> Research via AI-supported object classification ("Feature map" with R-CNN) of indigenous artefacts to allay deficiencies in archaeological data, among others.

**Wikidata**: [Q123156493](https://www.wikidata.org/wiki/Q123156493)  
**Source**: https://4ort.xyz/entity/maori-stone-research

## Summary
Māori Stone Research is an AI-supported project utilizing R-CNN object classification ("Feature map") to analyze indigenous artefacts, addressing deficiencies in archaeological data. It is part of Otago Museum and began in 2021.

## Key Facts
-   Start time: 2021
-   Instance of: Museum AI project, R-CNN, Deep neural network
-   Part of: Otago Museum
-   Uses: Artificial intelligence, Classification scheme, Agent-based model, Machine learning
-   Maintained by: WikiProject Museum AI projects (MAp)
-   Described at: https://www.auckland.ac.nz/en/news/2021/09/20/using-technologies-of-the-future-to-piece-together-the-past.html (English)
-   Involves: AI-supported object classification ("Feature map" with R-CNN) of indigenous artefacts
-   Purpose: To allay deficiencies in archaeological data

## FAQs
### Q: What does Māori Stone Research study?
A: Māori Stone Research uses AI-supported object classification ("Feature map" with R-CNN) to analyze indigenous artefacts, specifically targeting gaps in archaeological data.

### Q: When and where did Māori Stone Research start?
A: It began in 2021 and is part of the Otago Museum.

### Q: What core technologies does Māori Stone Research employ?
A: The project utilizes artificial intelligence, machine learning, classification schemes, agent-based models, and specifically R-CNN (Region-based Convolutional Neural Network) with feature mapping.

### Q: Why was Māori Stone Research initiated?
A: It was initiated to address deficiencies in archaeological data through AI-supported classification of indigenous artefacts.

### Q: Who maintains or is associated with Māori Stone Research?
A: It is part of Otago Museum and maintained by WikiProject Museum AI projects (MAp).

## Why It Matters
Māori Stone Research addresses a critical challenge in archaeology: the gap in comprehensive data, particularly concerning indigenous artefacts. By employing advanced AI techniques like R-CNN and feature mapping, it offers a scalable method for object classification that can process large datasets efficiently. This approach enhances the understanding and preservation of cultural heritage, providing insights that traditional methods might miss. The project exemplifies the practical application of deep neural networks in museum contexts, potentially setting a precedent for how AI can augment archaeological research and cultural resource management, especially for underrepresented collections.

## Notable For
-   Its specific application of AI-supported object classification ("Feature map" with R-CNN) to indigenous artefacts.
-   Its direct aim to alleviate documented deficiencies in archaeological data through computational methods.
-   Being an instance of a museum AI project specifically integrated within Otago Museum's operations.
-   Its use of deep neural networks for complex artefact classification tasks.
-   Its association and maintenance by WikiProject Museum AI projects (MAp).

## Body

### Purpose
Māori Stone Research is a project focused on addressing deficiencies in archaeological data through AI-supported object classification. Its primary function is to analyze indigenous artefacts, employing computational methods to improve data completeness and accessibility. This research targets the specific challenges encountered in documenting and understanding indigenous material culture.

### Technologies Employed
The project utilizes several core technologies:
*   **Artificial Intelligence (AI):** Underpins the automated analysis and classification processes.
*   **Machine Learning:** Enables the system to learn and improve classification based on data.
*   **R-CNN (Region-based Convolutional Neural Network):** Specifically used for object detection and classification within images of artefacts.
*   **Feature map:** A key component of the R-CNN model, highlighting relevant features for classification.
*   **Classification scheme:** Provides the framework for categorizing the identified artefacts.
*   **Agent-based model:** Used to simulate interactions or processes within the research framework.

### Organizational Context
Māori Stone Research is formally established as an instance of a museum AI project and a deep neural network. It is operationally part of Otago Museum. The project commenced in 2021. Its development and description are documented by the University of Auckland. It is also recognized and maintained within the scope of WikiProject Museum AI projects (MAp).