✅ Manage your projects
Image credit: UnsplashEasily manage your projects - create ideation mind maps, Gantt charts, todo lists, and more!
Ideation
Hugo Blox supports a Markdown extension for mindmaps.
Simply insert a Markdown code block labelled as markmap and optionally set the height of the mindmap as shown in the example below.
Mindmaps can be created by simply writing the items as a Markdown list within the markmap code block, indenting each item to create as many sub-levels as you need:
```markmap {height="200px"}
- Hugo Modules
- Hugo Blox
- netlify
- netlify-cms
- slides
```
renders as
- Hugo Modules - Hugo Blox - netlify - netlify-cms - slides
Diagrams
Hugo Blox supports the Mermaid Markdown extension for diagrams.
An example Gantt diagram:
```mermaid
gantt
section Section
Completed :done, des1, 2014-01-06,2014-01-08
Active :active, des2, 2014-01-07, 3d
Parallel 1 : des3, after des1, 1d
Parallel 2 : des4, after des1, 1d
Parallel 3 : des5, after des3, 1d
Parallel 4 : des6, after des4, 1d
```
renders as
Todo lists
You can even write your todo lists in Markdown too:
- [x] Write math example
- [x] Write diagram example
- [ ] Do something else
renders as
- Write math example
- Write diagram example
- Do something else
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I am currently a Ph.D. candidate at the Ai4City-Lab, Urban Governance and Design Thrust, Society Hub, The Hong Kong University of Science and Technology (Guangzhou), under the supervision of Prof. Wufan Zhao and Prof. Yuan Liu. Prior to this, I obtained my Master’s degree from the School of Geospatial Engineering and Science, Sun Yat-sen University, where I was advised by Prof. Wuming Zhang and Prof. Yiping Chen.
My research focuses on 3D visual perception, intelligent interpretation and processing of point cloud data, and multi-modal urban foundation models. I am particularly interested in bridging geometric understanding with semantic reasoning in large-scale urban environments, with an emphasis on open-vocabulary learning, training-free paradigms, and cross-modal fusion between 2D and 3D data.
My goal is to develop scalable, interpretable, and generalizable AI systems for urban analysis, enabling applications such as digital twin construction, urban scene understanding, and intelligent infrastructure management.