Eqi Luo

Eqi Luo

Academic Assistant at the Urban Sustainability & Climate Change Resilience Department

IHS, Erasmus University

Biography

Eqi Luo currently works as an Academic Assistant at IHS, Erasmus University. He supports the educational, research and advisory activities in the broad fields of urban sustainability, environment, climate change and resilience within the department. His expertise includes GIS analysis, remote sensing analysis, geo-spatial modelling, and programming (e.g., Python, R).

His main research interests revolve around the fields of urban remote sensing, geo-spatial modelling and urban analytics. In particular, he is interested in analysing urban form and dynamics by leveraging the strength of advanced Artificial Intelligence (AI) approaches (e.g., CNN, Random Forest) to investigate, map, and monitor complex urban phenomena in supports of achieving the SDG 11.

Download my CV.

Interests
  • Urban Remote Sensing
  • Geo-Spatial Analysis
  • Big (Geo)Data
  • GeoAI
  • Multiple Deprivation, Inequality
Education

  • MSc in Geo-information Science and Earth Observation (Cum Laude), 2021

    ITC, University of Twente, the Netherlands

  • BSc in Land Resource Management, 2019

    Huaqiao University, China

Experience

 
 
 
 
 
IHS, Erasmus University
Academic Assistant
IHS, Erasmus University
Feb 2022 – Present Rotterdam, the Netherlands

Responsibilities include:

  • Participating in the initial development of Land-Water-Human Nexus tool for urban sustainability.
  • Supporting the educational activities (e.g., workshop, lecture) and management of the master courses.
  • Assisting in the acquisition of research project and research proposal writing.
 
 
 
 
 
ITC, University of Twente
Junior Researcher
ITC, University of Twente
Sep 2021 – Dec 2021 Enschede, the Netherlands

  • Reshaping the MSc thesis into a prestigious peer-reviewed journal paper for publication.

  • Assisting in the courses of GIS/RS analysis for IDEAMAP(Sudan) Project.

  • Participating in the application and promotion of ITC Geo-spatial Computing Platform(CRIB).

 
 
 
 
 
IDEAMAPS, African Population and Health Research Center
Research Assistant
IDEAMAPS, African Population and Health Research Center
Nov 2020 – Apr 2021 (Remotely) Nairobi, Kenya

  • Collected, processed and compiled dozens of open geo-spatial data for deprivation mapping in three big African cities (Accra, Lagos & Nairobi).

  • Organized the deprivation data inventory to be published for modellers and potential users.

  • Co-developed a generalized urban extent definition to support multiple users’ needs in deprivation studies.

Reseach Activities

MSc Research - Mapping and predicting the urban deprivation degrees from RS images
In my MSc reseach, I specifically focused on exploring the potential of directly mapping the degrees of deprivation from Very-High-Resolution (VHR) images. The main work includes 1) developed an integrated methodology of unsupervised learning and deep learning to characterise and quantify deprivation, 2) performed PCA to build data-driven multi-dimensional indices of area deprivation at pixel level and 3) trained a deep CNN-based model via TensorFlow to predict the continuous deprivation indices from VHR images.
MSc Research - Mapping and predicting the urban deprivation degrees from RS images
The IDEAMAPS Project
The IDEAMAPS Network aims to develop and maintain an Integrated DEprived Area MAPping System that leverages the strengths of our current silo-ed approaches to ‘slum’ area mapping. During my work at the project, I was involved in one of the reseach topics ‘A maximum urban extent definition covering multiple needs’, where I contributed to 1) compared existing urban definitions to arrive at a generalized urban extent conceptualization, 2) co-developed and tested three algorithms to define urban areas in 14 sample cities and 3) worked on a scientific paper together documenting our reseach.
The IDEAMAPS Project
The SLUMAP Project
The overall objective of SLUMAP is to propose an open-source framework that allows for the processing of remote sensing images for (i) providing information on the location and extension of slums within a city and (ii) characterizing the physical environment within slums (i.e., in terms of greenness, built-up density, etc.) at limited cost. Our methods will be tested on a set of three sub-Saharan cities containing slums with different characteristics, namely Nairobi (Kenya), Kisumu (Kenya) and Ouagadougou (Burkina Faso).
The SLUMAP Project

Awards & Teaching Assistantship

  • ITC Excellence Scholarship (2019-2021)
  • Chinese National Scholarship, Huaqiao University (2016)
  • UMD18, Urb. Env. Sust. and Clim. Chang.,IHS, Erasmus University (Spring 2022)
  • UMD18, Action Planning Nairobi, IHS, Erasmus University (April 2022)
  • UMD18, Research Design, IHS, Erasmus University (April 2022)

Skills

R

80%

Python

90%

Statistics

90%

Geo-spatial Analysis

100%

Machine Learning

80%

Conferences & Presentations

Online Speaker (2021). Is it possible to learn about open geospatial data platforms in 75 mins to Harness Non-Official Sources for the 2030 Agenda?. In UN World Data Forum, Bern. Online.

Source Document

Eqi Luo, Monika Kuffer, Jiong Wang (2021). Mapping And Predicting The Intra-urban Degrees Of Deprivation Using EO Data. In 40th EARSEL Symposium, Warsaw. Online.

Slides

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