Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

Type D and Type F activities

A.A. 2026/2027 A.A. 2025/2026

Le attività formative di tipologia D sono a scelta dello studente, quelle di tipologia F sono ulteriori conoscenze utili all’inserimento nel mondo del lavoro (tirocini, competenze trasversali, project works, ecc.). In base al regolamento didattico del corso, alcune attività possono essere scelte e inserite autonomamente a libretto, altre devono essere approvate da apposita commissione per verificarne la coerenza con il piano di studio. 

Per eventuali limitazioni si rimanda agli articoli relativi  alle ATTIVITÀ A SCELTA  e  ATTIVITÀ FORMATIVE TRASVERSALI (F), STAGE, TIROCINI, ALTRO e del regolamento didattico del cds.

I crediti D / F possono essere acquisiti principalmente con attività didattiche nelle seguenti 4 tipologie:

1. Attività specifiche per il corso di laurea automaticamente inseribili a libretto.

2. Insegnamenti del catalogo generale di ateneo.

3. Lingue – incluso l’italiano per stranieri.

4. Competenze trasversali – TALC.

Per il punto 1 si veda in fondo alla pagina; per i punti 2-3-4 si rimanda al servizio specifico.

 

 

 

Attività specifiche per il corso di laurea automaticamente inseribili a libretto nell'a.a. 2025/26

1st semester From 10/1/25 To 1/30/26
years Modules TAF Teacher
1° 2° Elements of Cosmology and General Relativity D Claudia Daffara (Coordinator)
1° 2° Introduction to quantum mechanics for quantum computing D Claudia Daffara (Coordinator)
1° 2° Introduction to smart contract programming for ethereum D Sara Migliorini (Coordinator)
1° 2° APP REACT PLANNING D Graziano Pravadelli (Coordinator)
2nd semester From 3/2/26 To 6/12/26
years Modules TAF Teacher
1° 2° Digitalization of the green and agro economy D Davide Quaglia (Coordinator)
1° 2° Geospatial data science and AI-driven analytics D Sara Migliorini
1° 2° HW components design on FPGA D Franco Fummi (Coordinator)
1° 2° Rapid prototyping on Arduino D Franco Fummi (Coordinator)
1° 2° Protection of intangible assets (SW and invention)between industrial law and copyright D Mila Dalla Preda (Coordinator)

Geospatial data science and AI-driven analytics  (2025/2026)

Teaching code

4S014938

Teacher

Sara Migliorini

Credits

3

Also offered in courses:

Language

Italian

Scientific Disciplinary Sector (SSD)

IINF-05/A - Information Processing Systems

Period

2nd semester dal Mar 2, 2026 al Jun 12, 2026.

Erasmus students

Not available

Courses Single

Not Authorized

Lessons timetable MoodleMoodle Seminars 0

Learning objectives

Acquire knowledge in the field of spatial data and advanced techniques for analyzing this type of information, including through the use of LLM (Large Language Models).

Prerequisites and basic notions

Basic knowledge of databases and data analysis using machine learning techniques.

Program

1. Big Spatial and Satellite Data Analytics
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Introduction to scalable methods for managing and processing large geospatial and remote sensing datasets. Students will study data representation, spatial indexing, raster data modeling, and distributed query processing using frameworks such as Spark and AsterixDB. Case studies will illustrate applications in urban planning, disaster anagement, precision agriculture, and smart mobility systems.

2. Data Exploration and Visualization
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Focus on interactive and scalable methods that make big data accessible to users. Students will examine query processing techniques and visual analytics tools that support intuitive exploration and the discovery of patterns across thousands of datasets.

3. Large Language Models (LLMs) for Map Processing
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Exploration of recent advances in AI for simplifying geospatial query processing. This includes geospatial data synthesis, natural language interfaces, and AI-driven optimization methods, with applications in decision support for time-sensitive domains such as disaster response and urban
planning.

Didactic methods

In presence lessons

Learning assessment procedures

Oral exam or final project on an assigned topic.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Evaluation criteria

Have acquired knowledge on the processing of vector and raster spatial data, as well as their visualization and processing using AI techniques.

Criteria for the composition of the final grade

Approved/not approved.

Exam language

English