ID:
510443
Duration (hours):
39
CFU:
3
SSD:
TELECOMUNICAZIONI
Year:
2025
Overview
Date/time interval
Primo Semestre (29/09/2025 - 16/01/2026)
Syllabus
Course Objectives
This course provides the basic of machine learning and quantum computing techniques, with examples related to the use of remote sensing data or to the achievement of results of interest using remotely sensed data through a mix of on-line lectures, at home experiments and on-site experiences.
Course Prerequisites
The student should possess basic knowledge on physics, chemistry,
mathematical analysis, usually acquired from Bachelor-level courses.
mathematical analysis, usually acquired from Bachelor-level courses.
Teaching Methods
The overall plan of the programme a virtual and a physical mobility components, a per the call, organized according to the following schedule. Virtual mobility • 2 weekly hours of on-line lectures intertwined with • 2 weekly hours of on-line exercises in a cloud environment. Physical Mobility • 2 days of seminars by experts on the latest machine learning and quantum computing for EO applications on site in Italy, followed by • 2 days for final exercise finalization, and concluded by • 1 final day with public presentations and final evaluation by the organizers of the works by the participants.
Texts
Thomas Lillesand, Ralph W. Kiefer, Jonathan Chipman: Remote Sensing
and Image Interpretation, 7th Edition. Wiley, January 2015, 736 pages.
ISBN: 978-1-118-91947-7
Aaron E. Maxwell, Timothy A. Warner & Fang Fang (2018) Implementation
of machine-learning classification in remote sensing: an applied review,
International Journal of Remote Sensing, 39:9, 2784-2817, DOI:
10.1080/01431161.2018.1433343
Holloway, J.; Mengersen, K. Statistical Machine Learning Methods and
Remote Sensing for Sustainable Development Goals: A Review. Remote
Sens. 2018, 10, 1365. https://doi.org/10.3390/rs10091365
G. Cheng, X. Xie, J. Han, L. Guo and G. -S. Xia, "Remote Sensing Image
Scene Classification Meets Deep Learning: Challenges, Methods,
Benchmarks, and Opportunities," in IEEE Journal of Selected Topics in
Applied Earth Observations and Remote Sensing, vol. 13, pp. 3735-3756,
2020, doi: 10.1109/JSTARS.2020.3005403.
Qiangqiang Yuan, Huanfeng Shen, Tongwen Li, Zhiwei Li, Shuwen Li, Yun
Jiang, Hongzhang Xu, Weiwei Tan, Qianqian Yang, Jiwen Wang, Jianhao
Gao, Liangpei Zhang, “Deep learning in environmental remote sensing:
Achievements and challenges”, Rem. Sens. of Envir. Vol. 241, 2020,
111716, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2020.111716.
and Image Interpretation, 7th Edition. Wiley, January 2015, 736 pages.
ISBN: 978-1-118-91947-7
Aaron E. Maxwell, Timothy A. Warner & Fang Fang (2018) Implementation
of machine-learning classification in remote sensing: an applied review,
International Journal of Remote Sensing, 39:9, 2784-2817, DOI:
10.1080/01431161.2018.1433343
Holloway, J.; Mengersen, K. Statistical Machine Learning Methods and
Remote Sensing for Sustainable Development Goals: A Review. Remote
Sens. 2018, 10, 1365. https://doi.org/10.3390/rs10091365
G. Cheng, X. Xie, J. Han, L. Guo and G. -S. Xia, "Remote Sensing Image
Scene Classification Meets Deep Learning: Challenges, Methods,
Benchmarks, and Opportunities," in IEEE Journal of Selected Topics in
Applied Earth Observations and Remote Sensing, vol. 13, pp. 3735-3756,
2020, doi: 10.1109/JSTARS.2020.3005403.
Qiangqiang Yuan, Huanfeng Shen, Tongwen Li, Zhiwei Li, Shuwen Li, Yun
Jiang, Hongzhang Xu, Weiwei Tan, Qianqian Yang, Jiwen Wang, Jianhao
Gao, Liangpei Zhang, “Deep learning in environmental remote sensing:
Achievements and challenges”, Rem. Sens. of Envir. Vol. 241, 2020,
111716, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2020.111716.
Contents
The programme aims at training professionals trained in the use of geospatial data coming from satellite for Earth Observation applications, such as the monitoring of the UN Sustainable Development Goals. It is based on a virtual mobility that provides the basic of machine learning techniques applied to satellite data sets and challenges the participants to provide answers to data processing questions in groups of students by different institutions collaborating on-line.
The challenges, to be completed on an open cloud computing platform, refer to environmental monitoring problems in a real urban environment, and will be selected in accordance to a more than decadal experience by some of the instructors in the organizing group in the organization of international contests using EO data sets.
By learning the basic of EO data processing, experiencing the limits and advantages of machine learnings techniques applied to satellite imagery, and solving real problems for a real environment, the virtual component of this blended programme is meant to provide the participants with new abilities, as well as a first example of both distance learning, which will prove important for their future professional career, and team working in a remote environment.
The challenges, to be completed on an open cloud computing platform, refer to environmental monitoring problems in a real urban environment, and will be selected in accordance to a more than decadal experience by some of the instructors in the organizing group in the organization of international contests using EO data sets.
By learning the basic of EO data processing, experiencing the limits and advantages of machine learnings techniques applied to satellite imagery, and solving real problems for a real environment, the virtual component of this blended programme is meant to provide the participants with new abilities, as well as a first example of both distance learning, which will prove important for their future professional career, and team working in a remote environment.
Course Language
English
More information
This is a course funded by TNE project HERIT4FUTURE.
Degrees
Degrees
Electronic Engineering
Master’s Degree
2 years
No Results Found
People
People
No Results Found