# Descripci'on autom'atica de secciones delgadas de rocas: una aplicaci'on Web

arXiv: https://arxiv.org/abs/2402.15039 · HTML: https://mlsanity.com/paper/2402.15039/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 23 Feb 2024.

## Builds on (most cited first, 3 of 3)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization](https://mlsanity.com/paper/2004.15004/) (2020)
- [Image Captioning](https://mlsanity.com/paper/1805.09137/) (2018)

## Abstract (arXiv)

The identification and characterization of various rock types is one of the fundamental activities for geology and related areas such as mining, petroleum, environment, industry and construction. Traditionally, a human specialist is responsible for analyzing and explaining details about the type, composition, texture, shape and other properties using rock samples collected in-situ or prepared in a laboratory. The results become subjective based on experience, in addition to consuming a large investment of time and effort. The present proposal uses artificial intelligence techniques combining computer vision and natural language processing to generate a textual and verbal description from a thin section image of rock. We build a dataset of images and their respective textual descriptions for the training of a model that associates the relevant features of the image extracted by EfficientNetB7 with the textual description generated by a Transformer network, reaching an accuracy value of 0.892 and a BLEU value of 0.71. This model can be a useful resource for research, professional and academic work, so it has been deployed through a Web application for public use.