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Conference Report

Attending Geoinformatics 2026 and the CPGIS 2026 Annual Conference in Singapore

From 19 to 22 July 2026, I joined Geoinformatics 2026 and the CPGIS 2026 Annual Conference at the National University of Singapore, where I gave one oral presentation on second-dimension outliers for spatial prediction.

Geoinformatics 2026 participants on the NUS campus
Event Dates
Location
National University of Singapore
Role
Participant and oral presenter
Presentation
Second-dimension outliers for spatial prediction

Geoinformatics 2026 in Singapore

The 33rd International Conference on Geoinformatics and the CPGIS 2026 Annual Conference were held at the National University of Singapore under the theme “Geo-Innovations for a Sustainable and Resilient Society”. The programme brought together work on geospatial data, spatial analysis, GIScience, healthy cities, resilience, and Big Earth Data.

Moving between the lecture theatres, conference information area, and NUS campus made the event feel both focused and open: formal presentations were closely connected with informal conversations among researchers and students.

Conference information area at Geoinformatics 2026
The conference information area at NUS.新加坡国立大学的会议信息区。
Kai Ren's participant badge for Geoinformatics 2026
My participant badge for the 33rd International Conference on Geoinformatics.我的第三十三届地理信息科学国际会议参会证。
A parallel session at Geoinformatics 2026
Following a parallel session at the conference.参加会议分会场报告。

My Presentation on Second-Dimension Outliers

My contribution to the conference was one oral presentation, “Second-dimension outliers for spatial prediction”, delivered on 21 July in Session GSC-5, “Advanced Geospatial Data and Methods for Transforming Healthy Cities Delivery - Part 1”.

The presentation introduced second-dimension outliers as a way to capture local outlier information around unsampled locations and turn it into additional variables for spatial prediction. Using Australian wheat production as the case study, I showed how this added geographic and environmental context can improve machine-learning predictions, particularly for extreme values.

Presenting the study in a geoinformatics session helped me explain the idea beyond the paper itself: the objective is not simply to remove unusual observations, but to quantify their local spatial context and use that information in prediction.

Session GSC-5 programme listing Second-dimension outliers for spatial prediction
Session GSC-5, including my presentation on second-dimension outliers.GSC-5 分会场议程,其中包括我的“面向空间预测的第二维异常值”报告。
Kai Ren presenting at Geoinformatics 2026 at the National University of Singapore
Presenting “Second-dimension outliers for spatial prediction” at NUS.在新加坡国立大学报告“面向空间预测的第二维异常值”。
Kai Ren delivering his oral presentation at Geoinformatics 2026
Another moment from my oral presentation.口头报告现场的另一瞬间。
Kai Ren at the Geoinformatics 2026 conference backdrop
At the Geoinformatics 2026 conference backdrop.在 Geoinformatics 2026 会议背景板前留影。
Read the related journal article

Academic Exchange

Beyond my own report, I attended presentations on GIScience theory, journals, resilience, and large-scale geospatial data. A memorable part of the conference was meeting Professor May Yuan, Editor-in-Chief of the International Journal of Geographical Information Science, and listening to her presentation, “Chora Incognita: The causal alchemy of people, events, and places for resilience research”.

These exchanges offered a broader view of how geographic information science can connect methodological innovation with real questions about cities, human activities, and environmental resilience.

Professor May Yuan presenting at Geoinformatics 2026
Professor May Yuan presenting “Chora Incognita”.袁玫教授作“Chora Incognita”报告。
Kai Ren with Professor May Yuan at Geoinformatics 2026 in Singapore
With Professor May Yuan at Geoinformatics 2026.与 IJGIS 主编袁玫教授在 Geoinformatics 2026 合影。
Professor A-Xing Zhu presenting on Annals of GIS at Geoinformatics 2026
Professor A-Xing Zhu presenting a report on Annals of GIS.朱阿兴教授作《Annals of GIS》期刊报告。
Professor Huadong Guo presenting on Big Earth Data at Geoinformatics 2026
Professor Huadong Guo presenting on Big Earth Data and sustainable development.郭华东教授作大数据与可持续发展报告。
Kai Ren with Professor Min Chen at Geoinformatics 2026
With Professor Min Chen at Geoinformatics 2026.与陈旻教授在 Geoinformatics 2026 合影。
Kai Ren with Associate Professor Yongze Song during Geoinformatics 2026
With A/Prof. Yongze Song during the conference.会议期间与宋泳泽副教授合影。
Kai Ren with Associate Professor Yongze Song at Geoinformatics 2026
Another conference moment with A/Prof. Yongze Song.与宋泳泽副教授的另一张会议合影。

Conference Community and NUS

The conference also brought together members of the Geospatial Intelligence Lab, led by A/Prof. Yongze Song. Group activities around NUS and the conference banquet created time to continue conversations outside the lecture rooms and share the visit as a research group.

Geospatial Intelligence Lab members on the NUS campus
Members of the Geospatial Intelligence Lab, led by A/Prof. Yongze Song, on the NUS campus.宋泳泽副教授带领的地理空间智能实验室成员在新加坡国立大学合影。
Geospatial Intelligence Lab members at the NUS School of Computing
The Geospatial Intelligence Lab group at the NUS School of Computing.地理空间智能实验室成员在新加坡国立大学计算机学院合影。
Geospatial Intelligence Lab members at NUS University Town
The Geospatial Intelligence Lab group at NUS University Town.地理空间智能实验室成员在新加坡国立大学大学城合影。
Conference participants at the Geoinformatics 2026 banquet
Meeting participants during the conference banquet.与参会者在会议晚宴上交流。
NUS campus sign in Singapore
The colourful NUS campus sign.新加坡国立大学校园标志。
Kai Ren at the National University of Singapore
At the National University of Singapore.在新加坡国立大学留影。
NUS campus life sign
A bright afternoon on the NUS campus.新加坡国立大学校园一景。
NUS University Hall during the conference
NUS University Hall during the conference visit.会议期间到访新加坡国立大学 University Hall。

This was a compact but meaningful conference visit: one focused presentation, many conversations, and a valuable opportunity to connect my work on spatial prediction with the wider international GIScience community.