Abstract:
As one of the fields of civil and architecture engineering, 3D urban mapping plays a
vital role in various applications such as urban planning, surveillance, virtual reality,
virtual tourism, and military training. In this regard, the interest in 3D urban mapping
technologies is rapidly increasing within the surveying and photogrammetric
community. Such an interest is also motivated by the advent of new technologies, which
enable accurate and practical 3D urban mapping. In other words, the proliferation of
direct geo-referencing, digital imaging system (including medium-format digital
camera) and LiDAR (Light Detection And Ranging) provide the respective research
body with the potential to satisfy the detail level and complexity needed by the above
applications. Hence, there must be a framework for integrating these different kinds of
sensors. The proposed framework in this paper consists of three main components: 1)
Quality Assurance/Quality Control; 2) Co-registration; and 3) Element Matching. More
specifically, quality assurance of the mapping process and quality control of delivered
data/products are the first components of the proposed framework. Quality assurance
encompasses management activities to ensure that a process, item, or service is of the
quality needed by the user. The key activity in the quality assurance is the system
calibration procedure. After the calibration of the involved systems, quality control
procedures determine whether the desired quality has been achieved through internal
and external evaluation. As the second component of the framework, a registration
procedure is conducted to ensure that the datasets from different systems are georeferenced
with respect to a common reference frame. After the registration procedure is completed, matching between different information from different systems is carried out
to derive realistic 3D urban mapping that takes advantage of the synergistic
characteristics of the available datasets. For example, the spectral information from a
digital imaging system can be related to the positional information from LiDAR. The
paper will illustrate the main components and the necessary activities of the proposed
framework with the help of a real dataset.