Merge pull request #413 from ualsg/update

Yuhao visit and SD EBM

content/authors/filip/_index.md

@@ -30,7 +30,7 @@ interests:
 
 education:
   courses:
-  - course: PhD in 3D GIS (highest honours)
+  - course: PhD in 3D GIS (cum laude)
     institution: Delft University of Technology, Netherlands
     year: 
   - course: MSc in Geomatics
@@ -113,6 +113,7 @@ The list of his publications is available [here](https://filipbiljecki.com/#publ
 * [NUS Sustainable and Green Finance Institute](https://sgfin.nus.edu.sg) -- Research Affiliate
 * [NUS Institute of Real Estate and Urban Studies](https://ireus.nus.edu.sg) -- Research Affiliate
 * [Landscape and Urban Planning](https://www.journals.elsevier.com/landscape-and-urban-planning) -- Editorial Board Member
+* [Scientific Data](https://www.nature.com/sdata/) -- Editorial Board Member
 * [Transactions in GIS](https://onlinelibrary.wiley.com/journal/14679671) -- Editorial Board Member
 * [PLOS ONE](https://journals.plos.org/plosone/) -- Academic Editor for Geoinformatics
 * [International Journal of Digital Innovation in the Built Environment](https://www.igi-global.com/journal/international-journal-digital-innovation-built/224363) -- Editorial Board Member

content/post/2024-02-ceus-urban-form/index.md

@@ -55,6 +55,8 @@ The abstract follows.
 
 > Most studies of urban morphology rely on morphometrics, such as building area and street length. However, these methods often fall short in capturing visual patterns that carry abundant information about the configuration of urban elements and how they interact spatially. In this study, we introduce a novel method for learning morphology features based on figure-ground maps, which leverages recent developments in computer vision. Our method facilitates discovering and comparing urban form types in a fully unsupervised manner. Specifically, we examine building fabrics by 1 km patches. A visual representation learning model (SimCLR) casts each patch into a latent embedding space where similar patches are clustered while dissimilar patches are dispelled, thus generating morphology representations that entail the layout of building groups. The learned morphology features are tested in urban form typology clustering and comparison tasks in four diverse cities: Singapore, San Francisco, Barcelona, and Amsterdam, with data sourced from OpenStreetMap. Clustering results show effective identification of typical urban morphology types corresponding to urban functions and historical developments. Further analyses based on the representations reveal inner- and cross-city morphological homogeneity relating to socio-economic drivers. We conclude that this method is a promising alternative for effectively describing urban patterns in morphology analysis.
 
+![](2.png)
+
 ### Paper 
 
 For more information, please see the [paper](/publication/2024-ceus-urban-form-discovery/).

content/post/2024-05-sd-ebm/index.md

@@ -0,0 +1,40 @@
+---
+# Documentation: https://sourcethemes.com/academic/docs/managing-content/
+
+title: "Filip Biljecki joins the Editorial Board of Scientific Data"
+subtitle: "Our open science efforts continue to be recognised."
+summary: "Our open science efforts continue to be recognised."
+authors: [admin]
+tags: [journals, publications, papers, open science, open data, open-source software]
+categories: []
+date: 2024-05-19T13:39:19+08:00
+lastmod: 2024-05-19T13:39:19+08:00
+featured: false
+draft: false
+show_related: true
+pager: true
+
+# Featured image
+# To use, add an image named `featured.jpg/png` to your page's folder.
+# Focal points: Smart, Center, TopLeft, Top, TopRight, Left, Right, BottomLeft, Bottom, BottomRight.
+image:
+  caption: ""
+  focal_point: ""
+  preview_only: false
+
+# Projects (optional).
+#   Associate this post with one or more of your projects.
+#   Simply enter your project's folder or file name without extension.
+#   E.g. `projects = ["internal-project"]` references `content/project/deep-learning/index.md`.
+#   Otherwise, set `projects = []`.
+projects: []
+---
+
+Dr {{% mention "filip" %}}, the principal investigator of our research group, joins the Editorial Board of [Scientific Data](https://www.nature.com/sdata/), a top journal that is part of the [Nature Portfolio](https://www.nature.com/nature-portfolio).
+[It is a peer-reviewed, open-access journal for descriptions of datasets, and research that advances the sharing and reuse of scientific data](https://www.nature.com/sdata/journal-information).
+
+
+Our Lab is committed to open science, and [often releases data and software outputs openly](/data-code/).
+
+We have a couple of new exciting open datasets and open-source software packages coming out soon, stay tuned for the announcements!
+

content/post/2024-05-visit-yuhao/index.md

@@ -0,0 +1,56 @@
+---
+# Documentation: https://sourcethemes.com/academic/docs/managing-content/
+
+title: "Visit by Prof Yuhao Kang from the University of South Carolina & University of Texas at Austin"
+subtitle: "Continued collaboration with overseas research groups in GeoAI."
+summary: "Continued collaboration with overseas research groups in GeoAI."
+authors: [admin]
+tags: [visit, nus architecture, usa]
+categories: []
+date: 2024-05-20T01:39:19+08:00
+lastmod: 2024-05-20T01:39:19+08:00
+featured: false
+draft: false
+show_related: true
+pager: true
+
+# Featured image
+# To use, add an image named `featured.jpg/png` to your page's folder.
+# Focal points: Smart, Center, TopLeft, Top, TopRight, Left, Right, BottomLeft, Bottom, BottomRight.
+image:
+  caption: ""
+  focal_point: ""
+  preview_only: false
+
+# Projects (optional).
+#   Associate this post with one or more of your projects.
+#   Simply enter your project's folder or file name without extension.
+#   E.g. `projects = ["internal-project"]` references `content/project/deep-learning/index.md`.
+#   Otherwise, set `projects = []`.
+projects: []
+---
+
+Our Lab hosted Dr [Yuhao Kang](http://www.kkyyhh96.site/), Assistant Professor at the University of South Carolina and University of Texas at Austin. 🇺🇸
+
+Dr. Yuhao Kang is leading the GISense Lab. He was a postdoctoral researcher at MIT and received his Ph.D. from the University of Wisconsin-Madison. He had working experience at Google X and MoBike. Dr. Kang’s research mainly focuses on Human-centered Geospatial Data Science, including understanding human subjective experience at place and develop ethical and responsible geospatial artificial intelligence (GeoAI) approaches. By leveraging human-centered geospatial data science, Dr. Kang’s work has benefited various applications in public health, real estate, crime, and urban planning. His papers have been published on Landscape and Urban Planning, IJGIS, Cities, PNAS, etc. He was the recipient for several fellowships and awards, including the Young Researcher Award by the Austrian Academy of Sciences, CaGIS Rising Award, CaGIS scholarship, ICA scholarship, etc. He actively contributed to the GIScience community. He founded the non-profit global education project GISphere with over 20,000 members, served as the associate editor of Computational Urban Science, and board members for the AAG GISS/CyberGIS/Cartography groups and CPGIS.
+
+During his stay, besides several collaborative exchanges such as discussion sessions and meetings, Yuhao delivered the lecture _Advancing Sense of Place with Human-centered Geospatial Data Science_ (poster and abstract below).
+
+Thanks, and looking forward to future collaborations!
+
+![](1.jpg)
+
+![](2.jpg)
+
+![](3.jpg)
+
+![](4.jpg)
+
+![](5.jpg)
+
+![](poster.jpg)
+
+### Abstract of the lecture
+
+> Human sense of place refers to how we perceive, experience, and interact with a particular location and environment. The emergence of Geospatial Data Science – the use of geographic knowledge and AI approaches to extract meaningful insights from large-scale geographic data – has achieved remarkable success not only in modeling physical geographic phenomena but also in advancing human subjective experiences at place. In this talk, Dr. Kang will present a series of works that utilize geospatial data science to understand human experience and sense of place. First, utilizing eye-tracking systems, his work delved into human subjective safety perceptions (e.g., whether a neighborhood is perceived as a safe place) to identify physical objects that attract human attention from street view images. Second, utilizing large language models (LLMs), his work proposed a Soundscape-to-Image Diffusion model to visualize and translate human auditory perceptions into visual representations of place. His work demonstrated how human multi-sensory experiences can be linked to comprehensively understand human sense of place using Generative AI. Finally, he will share his multifaceted experiences in GISphere that aim to promote global GIScience education.
+