[Paper published] Check out our new (deep) machine learning paper for flood detection

New machine/deep learning paper led by Liping: Analysis of remote sensing imagery for disaster assessment using deep learning: a case study of flooding event

A full-text view-only version of the paper can be found via the link: https://rdcu.be/bpUvx.

 

(Geo) VIZ Resources

This page provides some resources about VIZ or GeoVIZ.

(Stay tuned, as I will update the content on this  page while I plow and grow in my deep learning garden:))

(Geo) VIZ Scholars/Groups

Selected Talks from Prof. Tamara Munzner (see complete list of Talks HERE).

(There are videos and demos for some VIZ tools on Dr. Shixia Liu’s home page under Publications)

Some selected papers from Dr. Shixia Liu’s research group:

Liu, S., Wang, X., Collins, C., Dou, W., Ouyang, F., El-Assady, M., … & Keim, D. (2018). Bridging text visualization and mining: A task-driven survey. IEEE transactions on visualization and computer graphics. (PDF, tool — a visualization tool that is part of a survey of text visualization & mining) 

Liu, S., Wang, X., Liu, M., & Zhu, J. (2017). Towards better analysis of machine learning models: A visual analytics perspective. Visual Informatics1(1), 48-56.

Jiang, L., Liu, S., & Chen, C. (2018). Recent research advances on interactive machine learning. Journal of Visualization, 1-17.

Liu, S., Chen, C., Lu, Y., Ouyang, F., & Wang, B. (2019). An interactive method to improve crowdsourced annotations. IEEE transactions on visualization and computer graphics25(1), 235-245.

Liu, S., Andrienko, G., Wu, Y., Cao, N., Jiang, L., Shi, C., … & Hong, S. (2018). Steering data quality with visual analytics: The complexity challenge. Visual Informatics.

Liu, M., Shi, J., Li, Z., Li, C., Zhu, J., & Liu, S. (2017). Towards better analysis of deep convolutional neural networks. IEEE transactions on visualization and computer graphics23(1), 91-100.

Selected papers:

Sacha, D., Sedlmair, M., Zhang, L., Lee, J. A., Weiskopf, D., North, S., & Keim, D. (2016, August). Human-centered machine learning through interactive visualization. ESANN. (PDF)

Selected papers:

Bernard, J., Hutter, M., Zeppelzauer, M., Fellner, D., & Sedlmair, M. (2018). Comparing visual-interactive labeling with active learning: An experimental study. IEEE transactions on visualization and computer graphics24(1), 298-308. (PDF)

 

  • TBA

 

(Geo) VIZ Charts

The Most Searched for Visualization Types, Tools, and Books

Finding D3 plugins with ease.

  • TBA

(Geo) VIZ Tools

  • Coming soon.

Good (Geo) VIZ  / GIS / geospatial Python libraries:

  • Cartopy : Support for geographical data (using a wide range of other libraries)

  • Coming soon.

Good (Geo) VIZ  Posts:

Great post. If you do any visualization or computer vision, these are things you need to know (but most people still don’t know:))

 

Good (GeoAI) VIZ  Posts:

 

Credits:

Prof. Alan M. MacEachren, Dr. Nai Yang, Dr. Teresa Onorati

Many thanks go to Professor Alan M. MacEachren letting me entering the interesting field of (Geo)VIZ!

 

References and Further Reading List:

 

[Paper published] Check out our new machine/deep learning paper

New machine/deep learning paper led by Liping: Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review https://t.co/kSF3O71tbD

Through the synthesis of multiple rapidly developing research areas, this systematic review is relevant to multiple research domains, including but not limited to GIScience, computer science, data science, information science, visual analytics, information visualization, image analysis, and computational linguistics, as well as any domains that need to leverage machine learning and deep learning .