🖥VectorTile2Text: Generating Descriptive Text from Vector Tile Maps for Enhanced Geospatial Understanding

[yuiseki]

From:

• https://github.com/UNopenGIS/7/issues/85#issuecomment-1478889166
• https://scrapbox.io/yuiseki/B10-4_Towards_Unsupervised_Remote_Sensing_Image_Captioning_and_Retrieval_with_Pre-Trained_Language_Models

Abstruct:

• GPT-3.5

  • In this paper, we propose the concept of vectortile2text, which generates descriptions from vector tile maps using image captioning. This technology can be used to quickly grasp the geographical features of any region, especially for travelers and visually impaired people. Our goal is to contribute to UN peacekeeping activities in the future. The paper includes the definition of the task, construction of the dataset, implementation of the machine learning model, training of the machine learning model, and evaluation of the machine learning model.

• GPT-4

  • In this paper, we present a novel concept called "vectortile2text", which aims to generate descriptive text from vector tile maps to facilitate rapid comprehension of geographical features in any given region. This approach has significant applications for travelers and visually impaired individuals, as well as potential contributions to United Nations peacekeeping efforts. We define the task, construct a dataset, implement a machine learning model, train the model, and evaluate its performance. Our findings suggest that the vectortile2text approach can effectively provide valuable insight into geographical features and serve as a practical tool for enhancing geospatial understanding.

[yuiseki]

Table of Contents:

1. Introduction 1.1. Background and Motivation 1.2. Problem Statement 1.3. Contributions

2. Related Work 2.1. Image Captioning 2.2. Geospatial Data Processing 2.3. Assistive Technologies

3. Task Definition 3.1. Input: Vector Tile Maps 3.2. Output: Descriptive Text 3.3. Evaluation Metrics

4. Dataset Construction 4.1. Data Collection 4.2. Data Annotation 4.3. Data Preprocessing 4.4. Dataset Statistics

5. Machine Learning Model 5.1. Model Architecture 5.2. Model Training 5.3. Model Optimization

6. Experimental Setup 6.1. Training and Validation Sets 6.2. Hyperparameters 6.3. Evaluation Methodology

7. Results and Evaluation 7.1. Quantitative Evaluation 7.2. Qualitative Evaluation 7.3. Comparison with Baselines 7.4. Ablation Study

8. Applications and Use Cases 8.1. Travelers 8.2. Visually Impaired Users 8.3. United Nations Peacekeeping Efforts

9. Conclusion and Future Work 9.1. Summary of Findings 9.2. Limitations 9.3. Future Research Directions

Appendix A: Implementation Details Appendix B: Additional Evaluation Results References Acknowledgements

[yuiseki]

これはまだ着手できていないけど、やる価値あるはず……

[hfu]

415 の結果、本件を進められる環境が整ってきそうな気がします。