The use of geospatial data for mapping ground, underground, and underwater properties and systems is of paramount importance in the design, planning, and management of land and the environment. The course provides methodological competencies and practical skills in survey methods based on topographical and geophysical techniques. Students will learn survey design and execution, data processing, interpretation, data visualization, metrical representation, and mapping through a mix of classroom hours, fieldwork, team assignments, and computer lab exercises. The fundamentals of statistics related to experimental errors, geodesy, cartographic representation, and signal processing required for methodological and practical implementations will also be provided.
The use of geospatial data for mapping ground, underground, and underwater properties and systems is of paramount importance in the design, planning, and management of land and the environment. The course provides methodological competencies and practical skills in survey methods based on topographical and geophysical techniques. Students will learn survey design and execution, data processing, interpretation, data visualization, metrical representation, and mapping through a mix of classroom hours, fieldwork, team assignments, and computer lab exercises. The fundamentals of statistics related to experimental errors, geodesy, cartographic representation, and signal processing required for methodological and practical implementations will also be provided.
The expected learning outcomes are designed to provide students with both professional and academic skills. Students will gain competence in environment description, mapping, and representation on the ground, in the subsurface, and underwater. For this purpose, methods and techniques for topographic/land survey and geophysical data acquisition will be taught.
The main expected learning outcomes are:
- **Survey planning and design** :Capability to select appropriate methods, design optimal sampling plans, and plan data acquisition, processing, and interpretation strategies for various engineering problems related to land survey and environmental analysis.
- **Tools and equipment** : Competence with traditional and innovative survey equipment and technologies (Total Station, Leveling, GNSS receivers), and the ability to use them in the field.
- **Field acquisition** : Capability to manage and execute the field acquisition of topographical and geophysical data.
- **Survey data processing** : Ability to process field topographic and geophysical data using standard or specifically implemented processing tools, incorporating statistical analysis and signal processing techniques.
- **Modeling and inversion techniques** : Ability to implement simple modeling tools or run codes for more complex modeling of topographic and geophysical data, and the capability to handle inversion methods.
- **Spatial data representation and mapping** : Metrical representation of objects and phenomena using mapping techniques and digital data in technical or thematic cartographic representation.
The expected learning outcomes are designed to provide students with both professional and academic skills. Students will gain competence in environment description, mapping, and representation on the ground, in the subsurface, and underwater. For this purpose, methods and techniques for topographic/land survey and geophysical data acquisition will be taught.
The main expected learning outcomes are:
- Survey planning and design: Capability to select appropriate methods, design optimal sampling plans, and plan data acquisition, processing, and interpretation strategies for various engineering problems related to land survey and environmental analysis.
- Tools and equipment: Competence with traditional and innovative survey equipment and technologies (Total Station, Leveling, GNSS receivers seismic and electrical geophysical instruments), and the ability to use them in the field.
- Field acquisition: Capability to manage and execute the field acquisition of topographical and geophysical data.
- Survey data processing: Ability to process field topographic and geophysical data using standard or specifically implemented processing tools, incorporating statistical analysis and signal processing techniques.
- Modeling and inversion techniques: Ability to implement simple modeling tools or run codes for more complex modeling of topographic and geophysical data, and the capability to handle inversion methods.
- Spatial data representation and mapping: Metrical representation of objects and phenomena using mapping techniques and digital data in technical or thematic cartographic representation.
Physics (), Geometry (vectors and matrices operations), Math (continuos and discrete functions), Computer sciences (Matlab or Python).
Fundamental of Physics (from Physics I and II), Geometry (vectors and matrices operations), Math (continuous and discrete functions), Computer sciences (Matlab or Python).
Common topics:
1) Introduction to terrestrial and geophysical survey methods and applications
2) Statistical data analysis
3) Fundamentals of signal processing
4) Topics of Topographic Land Surveying:
- Basics of Geodesy and Reference Systems
- Cartographic representation
- Topographic survey methods (Levelling, Total station, GNSS positioning)
5) Topics of Geophisical Land Surveying:
- Seismic wave propagation
- Seismic refraction tomography
- Seismic surface wave methods
- Seismic borehole methods
- Resistivity and electric field interaction with subsurface
- Electrical Resistivity Tomography
6) Field acquisition of topographic and geophysical data
7) Lab works on the processing of topographic data
8) Lab works on the processing of geophysical data
1) Introduction to terrestrial and geophysical survey methods and applications
2) Statistical data analysis
3) Fundamentals of signal processing
4) Topics of Topographic Land Surveying:
- Basics of Geodesy and Reference Systems
- Cartographic representation
- Topographic survey methods (Levelling, Total station, GNSS positioning)
5) Topics of Geophysical Land Surveying:
- Seismic wave propagation
- Seismic refraction tomography
- Seismic surface wave methods
- Seismic borehole methods
- Resistivity and electric field interaction with the subsurface
- Electrical Resistivity Tomography
6) Field acquisition of topographic and geophysical data
7) Lab works on the processing of topographic data
8) Lab works on the processing of geophysical data
The course has a large part of learning by doing activities concerning field data acquisition and data processing and interpretation is 100 hour long and it is organised as follows:
65 hours - lectures on theoretical and methodological aspects
10 hours – field work with different tools
25 hours – computer lab data processing and team work (python/matlab or specific processing software)
The course has a large part of learning by doing activities concerning field data acquisition and data processing and interpretation is 100 hour long and it is organised as follows:
65 hours - lectures on theoretical and methodological aspects
10 hours – field work with different tools
25 hours – computer lab data processing and team work (Python/Matlab or specific processing software)
Material available on the web (portale della didattica): lecture slides, limited portions of reference books, collection of papers.
We recommend the following textbooks::
• Manzino A. M. Quaderni di Topografia, (2017) Volume 1 Levrotto e Bella, ISBN 978-88-8218-194-9
• Manzino A. M. Quaderni di Topografia, (2019) Volume 2 Levrotto e Bella, ISBN 978-88-8218-202-1
• Manzino, A. M. Esercizi di Topografia svolti in MATLAB® (2013) Ebook Otto Ed. ISBN 9788895285443
• Cina, A. (2002). Trattamento delle misure topografiche. CELID, Torino. ISBN 88-7661-534-2
For further information:
• Cina, A. (2014) – Dal GPS al GNSS (Global Navigation Satellite System) per la Geomatica. Torino, CELID
• Barzaghi, R. Pagliari, D. Pinto, L. (2022): elementi di Topografia e trattamento delle osservazioni. Milano, Città Studi.
• Sansò, F., Betti, B. Albertella A. (2019): Positioning. Posizionamento classico e satellitare. Milano, Città Studi.
Material available on the web (portale della didattica): lecture slides, limited portions of reference books, collection of papers.
We recommend the following textbooks::
• Manzino A. M. Quaderni di Topografia, (2017) Volume 1 Levrotto e Bella, ISBN 978-88-8218-194-9
• Manzino A. M. Quaderni di Topografia, (2019) Volume 2 Levrotto e Bella, ISBN 978-88-8218-202-1
• Manzino, A. M. Esercizi di Topografia svolti in MATLAB® (2013) Ebook Otto Ed. ISBN 9788895285443
• Cina, A. (2002). Trattamento delle misure topografiche. CELID, Torino. ISBN 88-7661-534-2
For further information:
• Cina, A. (2014) – Dal GPS al GNSS (Global Navigation Satellite System) per la Geomatica. Torino, CELID
• Barzaghi, R. Pagliari, D. Pinto, L. (2022): elementi di Topografia e trattamento delle osservazioni. Milano, Città Studi.
• Sansò, F., Betti, B. Albertella A. (2019): Positioning. Posizionamento classico e satellitare. Milano, Città Studi.
Slides; Esercitazioni di laboratorio;
Lecture slides; Lab exercises;
Modalità di esame: Prova orale obbligatoria; Elaborato progettuale in gruppo;
Exam: Compulsory oral exam; Group project;
...
Exam: (A) Team public oral presentation of the assignments + (B) Team assignment report + (C) Compulsory oral exam
(A) Before the exam, students, divided into teams of 4-5 members, will present their solutions to the assigned task/problem to the lecturers and classmates. This presentation will be graded and will constitute 20% of the final score.
(B) Prior to the exam, students, also divided into teams of 4-5 members, will submit their assignment report to the lecturers. This report will be graded and will account for 30% of the final score.
(C) If the combined score from (A) and (B) is positive (≥18), the student will be admitted to the mandatory oral exam, which will be graded separately and make up 50% of the final score. The oral exam will include three questions covering the entire theoretical program and/or a discussion of the results presented in the team report. With respect to the report, questions will focus on strategies for data processing and interpretation. The oral exam aims to assess the learning outcomes and skills related to data processing and interpretation, including the use of simplified modeling and processing tools.
The final grade will be computed as follows: (20% from A) + (30% from B) + (50% from C).
Gli studenti e le studentesse con disabilità o con Disturbi Specifici di Apprendimento (DSA), oltre alla segnalazione tramite procedura informatizzata, sono invitati a comunicare anche direttamente al/la docente titolare dell'insegnamento, con un preavviso non inferiore ad una settimana dall'avvio della sessione d'esame, gli strumenti compensativi concordati con l'Unità Special Needs, al fine di permettere al/la docente la declinazione più idonea in riferimento alla specifica tipologia di esame.
Exam: Compulsory oral exam; Group project;
Exam: (A) Team public oral presentation of the assignments + (B) Team assignment report + (C) Compulsory oral exam
(A) Before the exam, students, divided into teams of 4-5 members, will present their solutions to the assigned task/problem to the lecturers and classmates. This presentation will be graded and will constitute 20% of the final score.
(B) Prior to the exam, students, also divided into teams of 4-5 members, will submit their assignment report to the lecturers. This report will be graded and will account for 30% of the final score.
(C) If the combined score from (A) and (B) is positive (≥18), the student will be admitted to the mandatory oral exam, which will be graded separately and make up 50% of the final score. The oral exam will include three questions covering the entire theoretical program and/or a discussion of the results presented in the team report. With respect to the report, questions will focus on strategies for data processing and interpretation. The oral exam aims to assess the learning outcomes and skills related to data processing and interpretation, including the use of simplified modeling and processing tools.
The final grade will be computed as follows: (20% from A) + (30% from B) + (50% from C).
In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.