I am eager to learn and try new things. Curious about various subjects and enjoy exploring them thoroughly to gain a complete understanding. In university, I began researching deep learning and image recognition and often taught myself new technologies to realize my ideas.
my research focused on image processing and AI-related fields. Therefore, I strengthened my knowledge of neural networks, digital image processing, computer vision, and other technologies.
During my academic term, I compiled research data and experimental results each year, submitted them to the 2018 TANET seminar, 2019 Yilan Geographic Association, 2020, and 2021 MDPI journals. My papers were accepted and published.
During my internship, implemented the task of visualizing 3D shapefiles and displaying them in a web browser using Cesium
When serving as a research assistant, I try to overlaid Taiwan clinic-related data onto a opensourse map and presented it using R-Shiny
| # | 協助研究計畫 | 主要技術 | 年度 |
|---|---|---|---|
| 1 | 台灣社區照顧關懷據點與長照ABC據點資源配置評估模式探討 | Data Process | 08-2020 |
| 2 | GeoAI之可解釋性技術研發：以遙感影像分析為例 | Deep Learning | 08-2020 |
| 3 | GeoAI之可解釋性技術研發：以遙感影像分析為例(Ⅱ) | Image caption generation | 08-2021 |
| 4 | 基於轉換器架構之語言級遙感影像場景理解技術開發 | Transformer Model | 08-2022 |
Proposed RSSCNet model uses two-stage cyclic learning rate strategy and transfer learning to train remote sensing image. In addition, the effectiveness of the proposed model was verified using manifold learning t-SNE, and the results were analyzed and explained using LIME visualization method.
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This study combines explainable artificial intelligence (XAI) techniques to evaluate a set of automated strategies for enhancing image quality. These methods can enhance details, conducive to extracting image features from deep learning models and further improving classification efficiency.
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