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Code Module Credits Duration Prerequisite(s)

Table I: Compulsory Modules (Cont.)
COMP6164 Smart City Remote Sensing 3 45 hrs ---
In the module, basics of remote sensing, Geographical Information System (GIS), Global

Navigation Satellite System (GNSS), and the relevance to smart cities are covered. GIS
are tools for managing, describing, analyzing, and presenting information about the
relationships between where features are (location, size and shape) and what they are
like (descriptive information - attribute data). Mapping is the common technique used
to represent social and environmental data. Basic principles of remote sensing (Earth
observation sensors and platforms, thermal remote sensing, spectral signatures) of
different land cover features are discussed. Satellites and aerial vehicles are tools for
capturing images. Signal processing and interpretation could be important in both
detection and prediction. The technological principles of GNSS are discussed with focus
on GNSS receivers, GNSS data processing methods, errors and accuracy. Advanced GNSS
processing, applications such as GPS signal characteristics, data formats (broadcast,
precise ephemeris), and mobile mapping may be discussed. Skill will be developed in
using remote sensing software tool, such as the widely popular ArcGIS Pro software. The
module also explores case studies of smart cities utilizing remote sensing.

COMP6165 Selected Topics in Environmental 3 45 hrs ---
Intelligence
Environmental Intelligence explores the intersection of environmental sciences, artificial
intelligence (AI), and data analytics to address and solve complex environmental
challenges. This module is designed for graduate students who aim to utilize advanced
computational tools and techniques in environmental research and policy-making.
Students will gain a foundational understanding of how AI technologies such as machine
learning, remote sensing, and big data analytics can be leveraged for environmental
monitoring, resource management, and climate change mitigation. It covers how AI can
be used in data analysis, predictive modeling, and system optimization to make more
informed decisions for environmental management. This module perfectly suited for
any graduate student interested in how advanced technology can be harnessed to
support and enhance environmental stewardship and sustainability. This comprehensive
introduction encourages students to engage with technological solutions that have the
potential to address some of the most pressing ecological issues of our time, preparing
them to contribute thoughtfully and effectively in diverse professional roles that
intersect with environmental and technological domains.
Table II

COMP6298 Project Report 9 --- ---
Students are required to apply the techniques and technologies which they have learned

in a significant advanced project. Under the supervision of an advisor, the students shall
focus on a contemporary research topic or technological problem and make use of the
leading-edge techniques to produce new research findings or solutions. Upon
completion, the Project Report is to be submitted and evaluated using the standard
criteria for advanced project.

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