# Satellite Captioning: Large Language Models to Augment Labeling

arXiv: https://arxiv.org/abs/2312.10905 · HTML: https://mlsanity.com/paper/2312.10905/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://mlsanity.com/paper/1807.11626/) (2018)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Exploring Models and Data for Remote Sensing Image Caption Generation](https://mlsanity.com/paper/1712.07835/) (2017)
- [A Survey of Semantic Segmentation](https://mlsanity.com/paper/1602.06541/) (2016)
- [Guiding Long-Short Term Memory for Image Caption Generation](https://mlsanity.com/paper/1509.04942/) (2015)
- [Discoverability in Satellite Imagery: A Good Sentence is Worth a Thousand Pictures](https://mlsanity.com/paper/2001.05839/) (2020)

## Abstract (arXiv)

With the growing capabilities of modern object detection networks and datasets to train them, it has gotten more straightforward and, importantly, less laborious to get up and running with a model that is quite adept at detecting any number of various objects. However, while image datasets for object detection have grown and continue to proliferate (the current most extensive public set, ImageNet, contains over 14m images with over 14m instances), the same cannot be said for textual caption datasets. While they have certainly been growing in recent years, caption datasets present a much more difficult challenge due to language differences, grammar, and the time it takes for humans to generate them. Current datasets have certainly provided many instances to work with, but it becomes problematic when a captioner may have a more limited vocabulary, one may not be adequately fluent in the language, or there are simple grammatical mistakes. These difficulties are increased when the images get more specific, such as remote sensing images. This paper aims to address this issue of potential information and communication shortcomings in caption datasets. To provide a more precise analysis, we specify our domain of images to be remote sensing images in the RSICD dataset and experiment with the captions provided here. Our findings indicate that ChatGPT grammar correction is a simple and effective way to increase the performance accuracy of caption models by making data captions more diverse and grammatically correct.