Summary of Rsteller: Scaling Up Visual Language Modeling in Remote Sensing with Rich Linguistic Semantics From Openly Available Data and Large Language Models, by Junyao Ge et al.
RSTeller: Scaling Up Visual Language Modeling in Remote Sensing with Rich Linguistic Semantics from Openly Available Data and Large Language Models
by Junyao Ge, Xu Zhang, Yang Zheng, Kaitai Guo, Jimin Liang
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel workflow harnesses large language models (LLMs) to generate massive, multimodal datasets for remote sensing (RS) image classification and scene understanding. The approach leverages OpenStreetMap (OSM) data and Google Earth Engine (GEE) platform images to produce paired RS data with rich captions at scale. RSTeller, a dataset comprising over 1.3 million RS images, is presented, accompanied by two descriptive captions. Extensive experiments show that RSTeller enhances the performance of multiple existing vision language models for RS scene understanding through continual pre-training. This methodology reduces manual effort and expertise needed for annotating remote sensing imagery, democratizing access to high-quality annotated data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses big computers to help create huge amounts of labeled data for analyzing satellite images. They used a special kind of map called OpenStreetMap and a platform called Google Earth Engine to make these images with descriptions at the same time. This helps machines understand what they see in the images better. The team created a dataset called RSTeller, which has over 1 million images, each with two descriptions. They tested it and found that it makes machines better at understanding satellite image scenes. This new way of working saves people lots of time and effort, making it easier for others to use this data. |
Keywords
» Artificial intelligence » Image classification » Scene understanding
