101-0526-00L Introduction to Visual Machine Perception for Architecture, Construction and Facility Management
| Semester | Spring Semester 2022 |
| Lecturers | I. Armeni |
| Periodicity | yearly recurring course |
| Language of instruction | English |
Courses
| Number | Title | Hours | Lecturers | ||||
|---|---|---|---|---|---|---|---|
| 101-0526-00 G | Introduction to Visual Machine Perception for Architecture, Construction and Facility Management | 2 hrs |
| I. Armeni |
Catalogue data
| Abstract | The course is an introduction to Visual Machine Perception technology, and specifically Computer Vision and Machine Learning, for Architecture, Construction, and Facility Management (ACFM). It will explore fundamentals in these Artificial Intelligence (AI) technologies in a tight reference to three applications in ACFM, namely architectural design, construction renovation, and facility management. |
| Objective | By the end of the course students will develop computational thinking related to visual machine perception applications for the ACFM domain. Specifically, they will: -Gain a fundamental understanding of how this technology works and the impact it can have in the ACFM industry by being exposed to example applications. -Be able to identify limitations, pitfalls, and bottlenecks in these applications. -Critically think on solutions for the above issues. -Acquire hands-on experience in creatively thinking and designing an application given a base system. -Use this course as a “stepping-stone” or entry-point to Machine Learning-intensive courses offered in D-BAUG and D-ARCH. |
| Content | The past few years a lot of discussion has been sparked on AI in the Architecture, Construction, and Facility Management (ACFM) industry. Despite advancements in this interdisciplinary field, we still have not answered fundamental questions about adopting and adapting AI technology for ACFM. In order to achieve this, we need to be equipped with rudimentary knowledge of how this technology works and what are essential points to consider when applying AI to this specific domain. In addition, the availability of sensors that collect visual data in commodity hardware (e.g., mobile phone and tablet), is creating an even bigger pressure in identifying ways that new technology can be leveraged to increase efficiency and decrease risk in this trillion-dollar industry. However, cautious and well-thought steps need to be taken in the right direction, in order for such technologies to thrive in an industry that showcases inertia in technological adoption. The course will unfold as two parallel storylines that intersect in multiple places: 1) The first storyline will introduce fundamentals in computer vision and machine learning technology, as building blocks that one should consider when developing related applications. These blocks will be discussed with respect to latest developments (e.g., deep neural networks), pointing out their impact in the final solution. 2) The second storyline consists of 3 ACFM processes, namely architectural design, construction renovation, and facility management. These processes will serve as application examples of the technological storyline. In the points of connection students will see the importance of taking into account the application requirements when designing an AI system, as well as their impact on the building blocks. Guest speakers from both the AI and ACFM domains will complement the lectures. |
| Prerequisites / Notice | The course does not require any background in AI, Computer Science, coding, or the ACFM domain. It is designed for students of any background and knowledge on these topics. Despite being an introductory class, it will still engage advanced students in the aforementioned topics. |
Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 3 credits |
| Examiners | I. Armeni |
| Type | graded semester performance |
| Language of examination | English |
| Repetition | Repetition only possible after re-enrolling for the course unit. |
| Additional information on mode of examination | The grading for this course will be a combination of class exercises and submission of a final project. Throughout the course students will be asked to solve mini exercises that would either require critical thinking, research in prior work, or hands-on interaction with a pre-existing system. The course also includes a final project. Students will be asked to creatively design and develop an application based on the material covered in the course lectures. The mini exercises throughout the semester are designed to complement the final project. Slides and other material will be made available online. The course does not have a final exam. |
Learning materials
| Main link | information |
| Only public learning materials are listed. | |
Groups
| No information on groups available. |
Restrictions
| There are no additional restrictions for the registration. |
