This version of the course guide is provisional until the period for editing the new course guides ends.

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3D Projects and Business Strategies in Geoinformation

Code: 45550 ECTS Credits: 6
2025/2026
Degree Type Year
Geoinformación OP 1

Contact

Name:
Maria Assumpcio Termens Perarnau
Email:
assumpcio.termens@uab.cat

Teachers

Marc Torres Saura
Ariadna Just Orriols

Teaching groups languages

You can view this information at the end of this document.


Prerequisites

This course has no specific requirements beyond a general knowledge of the field of geoinformation.


Objectives and Contextualisation

The primary aim of this module is to equip students with the methodologies and techniques necessary for developing product specifications in the field of geoinformation. This includes designing business models, drafting product exploitation plans, and formulating digital marketing strategies. The module also addresses the use of digital marketing tools to promote, distribute, and position geoinformation products and services effectively.

The module also includes a dedicated section on 3D geoinformation products—an emerging and rapidly evolving area within the geoinformation sector. This section provides an overview of the current state of the art in the generation, processing, and application of three-dimensional data derived from LiDAR sensors and photogrammetric techniques.

3D geoinformation models are becoming increasingly important across a wide range of applications, including urban planning, smart city management, forest management, wildfire prevention, and terrain morphology analysis. These datasets support the development of innovative products and services that contribute to both economic growth and environmental sustainability.

Upon the completion of this module, students will be able to:

  • Understand the fundamental principles behind the generation and application of 3D data acquired through Earth observation systems.

  • Acquire both theoretical and practical knowledge of the technologies and tools used for the acquisition, processing, and analysis of 3D geoinformation.

  • Analyse and assess the potential applications of 3D geoinformation in fields such as urban management, territorial planning, forest resource monitoring, and terrain morphology studies.


Learning Outcomes

  1. CA22 (Competence) Plan a technical project for the production and distribution of information products or services, incorporating solutions for users' needs.
  2. CA22 (Competence) Plan a technical project for the production and distribution of information products or services, incorporating solutions for users' needs.
  3. CA23 (Competence) Devise technical specifications and quality control systems related to geospatial information products or services.
  4. CA24 (Competence) Generate 3D geoinformation products based on knowledge about digital marketing.
  5. KA21 (Knowledge) Select the different types of general and specific distribution, use and reproduction licenses for cartography, geospatial data and software, especially related to the potential that 3D geoinformation offers in the management of cities and the urban environment.
  6. KA22 (Knowledge) Identify legislation related to the publication and dissemination of data and results derived from cartography, geospatial information and, in general, all types of data, including the protection of personal data.
  7. KA23 (Knowledge) Define the strategy for a distribution and/or marketing plan for geospatial information products or services.
  8. SA25 (Skill) Organise information to ensure and maintain traceability of geoinformation products and services.
  9. SA26 (Skill) Analyse 3D geoinformation to improve territorial management.

Content

Marketing and distribution of geoinformation products and services

1.  Introduction to digital marketing.

2.  The value of the idea. From the idea to business.

3.  Business models. How can I monetize?

4.  Customer development. How to bring customers into the business.

5.  Value proposition canvas. The customer's need as a seed of business.

6.  Business model canvas.

  • Value proposal.
  • Segments of customers.
  • Channels.
  • Relationship with customers.
  • Revenue model.
  • Partners.
  • Examples.

7.  Digital marketing. Prototyping. 

  • Digital marketing plan
  • The website
  • Lead generation
  • SEO, SEM, and AdWords. The presence of our business on the Internet.
  • Digital marketing on social networks

 

3D Geoinformation: Uses, Applications, and the Urban Ecosystem

  1. Introduction and Motivation for 3D

  2. Acquisition of 3D Data

    • Active Sensors

    • Passive Sensors

  3. Semantic Interpretation of a 3D Point Cloud

    • Classification

    • Segmentation

  4. 3D City Models

    • Principles

    • Creation of 3D City Models

    • Use Cases

  5. 3D in Urban Ecosystems and Natural Environments
    • Principles
    • Vegetation Modelling
    • Use Cases
  6. Territorial Morphology and Change Monitoring

Activities and Methodology

Title Hours ECTS Learning Outcomes
Type: Directed      
Lectures on basic concepts 36 1.44 CA22, CA23, CA24, KA21, KA22, KA23, SA25, SA26, CA22
Type: Supervised      
Semester project, exercises and oral presentations 15 0.6 CA22, CA23, CA24, KA21, KA22, KA23, SA25, SA26, CA22
Type: Autonomous      
Practical exercises 69 2.76 CA22, CA23, CA24, KA21, KA22, KA23, SA25, SA26, CA22

Learning is achieved by means of three types of activities:

Directed activities: Directed activities are theoretical and practical lectures in a computer lab. They include solving case studies and practical exercises, using as the main method a problem based learning approach. Lectures serve to systematize all the content, to present the state of the art of the different subjects, to provide methods and techniques for specific tasks, and to sum up the knowledge to learn. Lectures also organize the autonomous and complementary work done by the students.

Supervised activities: Supervised activities are focused on the execution of a semester project, consisting of a real case study, carried out through workshop hours, autonomous work and tutorials. This semester project allows applying together all the knowledge and technical skills learnt in all the courses of the semester. The semester project is a milestone for the students and the current demonstration that they had achieved the learning goals of all the courses of the semester. It is also the main evidence for evaluation, as students should have to submit at the end of the semester a report that summarizes the whole project and do an oral presentation.

Autonomous activities: Autonomous work of the students includes personal readings (papers, manuals, relevant reports, etc.), data and documentation search, complementary exercises and the personal development of the semester project.

The activities that could not be done on-site will be adapted to an online format made available through the UAB’s virtual tools. Exercises, projects, and lectures will be carried out using virtual tools such as tutorials, videos, Teams sessions, etc. Lecturers will ensure that students are able to access these virtual tools, or will offer them feasible alternatives.

Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

 

Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.


Assessment

Continous Assessment Activities

Title Weighting Hours ECTS Learning Outcomes
Oral presentations 25% 7.5 0.3 CA22, CA23, CA24, KA21, KA22, KA23, SA25, SA26
Pratical exercises 45% 13.5 0.54 CA22, CA23, CA24, KA21, KA22, KA23, SA25, SA26
Report submissions 30% 9 0.36 CA22, CA23, CA24, KA21, KA22, KA23, SA25, SA26

In the event that assessment activities cannot be taken on-site, they will be adapted to an online format made available through the UAB’s virtual tools (original weighting will be maintained). Homework, activities, and class participation will be carried out through forums, wikis and/or discussion on Teams, etc. Lecturers will ensure that students are able to access these virtual tools, or will offer them feasible alternatives.

CONTINUOUS EVALUATION. This subject/module does not incorporate single assessment.

a) Evaluation procedure and activities:

Evaluation of the course is based mostly on the semester project, that comprises two evaluation activities. The elaboration and submission of a synthesis report and the oral presentation of the project done. Given the technical content of the course, the weight assigned to the project report is 30% of the total course grading, assuming that it is the most appropriate means to explain all the technical details of the project, and a weight of 25% at the oral presentation. The course assessment is completed with the evaluation of the practical exercises done along the course, that account for another 45% of the total course grading.

Except when expressly noticed, all the evaluation activities (report and oral presentation of the semester project, as well as practical exercises) have to be carried out individually.

Time assigned to each evaluation activity includes the time spent in making all the material evidences for evaluating each activity (e.g., writing of the report, preparing the presentation slides, etc.).

b) Evaluation schedule:

2nd semester project report: Making during all the semester. Submission at the end of semester, on 17 April 2026.

2nd semester project oral presentation: Making during all the semester. Oral presentation at the end of semester, on 24 April 2026.

Course practical exercises: Making and submission weekly or biweekly along the semester.

c) Grade revision:

On carrying out each evaluation activity, lecturers will inform students (on Moodle) of the procedures to be followed for reviewing all grades awarded, and the date on which such a review will take place.

Once the grades obtained are published, students will have one week to apply for a grade revision by arranging an appointment with the corresponding teachers.

d) Procedure for reassessment:

2nd semester project report: It could be reassessed in the following two weeks after the submission date scheduled. Reassessment will require the submission of a new whole report in case of negative evaluation of the former report submitted.

2nd semester project oral presentation It could be reassessed in the following week after the date scheduled for the oral presentation. Reassessment will require doing again the oral presentation in case of negative evaluation of the former presentation done.

Course practical exercises: Can not be reassessed.

To have the right to a reassessment, the student will have to have been previously evaluated in a set of activities that account for at least two thirds of the total course grading. Therefore, he or she will have to have been evaluated of the 1st semester project report (40%) and of the 1st semester project oral presentation (30%) in the dates scheduled.

The right to a reassessment will only be granted to students that, having not passed the course (e.g., having a total course grade below 5 over10), had obtained at least a total course grade above 3,5 over 10.

e) Conditions for a ‘Not assessable’ grade:

Students will receive the grade ‘Not assessable’ instead of ‘Fail’ if they had submitted neither the 2nd semester project report nor done the 2nd semester project oral presentation. Students will obtain a Not assessed/Not submitted course grade unless they have submitted more than 1/3 of the assessment items.

f) UAB regulations on plagiarism and other irregularities in the assessment process:

In the event of a student committing any irregularity that may lead to a significant variation in the grade awarded to an assessment activity, the student will be given a zero for this activity, regardless of any disciplinary process that may take place. In the event of several irregularities in assessment activities of the same subject, the student will be given a zero as the final grade for this subject.

Assessment activities with a zero grade because of irregularities can not be reassessed.

On carrying out each evaluation activity, lecturers will inform students of the procedures to be followed for reviewing all grades awarded, and the date on which such a review will take place.

For this subject, the use of Artificial Intelligence (AI) technologies is permitted exclusively for support tasks. Students must clearly identify which parts have been generated with this technology, specify the tools used, and include a critical reflection on how these have influenced the process and the final outcome of the activity. Non-transparency in the use of AI in this assessable activity will be considered a lack of academic honesty and may lead to a partial or total penalty in the activity's grade, or more severe sanctions in serious cases.


Bibliography

 

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The Fierce Rise of Airborne Lidar. https://www.gim-international.com/content/article/the-fierce-rise-of-airborne-lidar

 

M.Carós, A.Just, S.Seguí, J.Vitrià, 2024. Effective Training and Inference Strategies for Point Classification in LiDAR Scenes. Remote sensing 16(12):2153. DOI 10.3390/rs16122153

 

M.Carós, A.Just, S.Seguí, J.Vitrià.,2023. “Addressing Scale and Density Challenges in LiDAR Point Classification.”

 

M.Carós, A.Just, S.Seguí, J.Vitrià. Sept. 2023. Self-Supervised Pre-Training Boosts Semantic Scene Segmentation on LiDAR data.

 

M.Carós, A.Just, S.Seguí, J.Vitrià. Oct. 2022. Object Segmentation of Cluttered Airborne LiDAR Point Clouds. In: Artificial Intelligence Research and Development. vol. 356. IOS Press; 2022.

 

Charles R. Qi, Hao Su, Kaichun Mo, Leonidas J. Guibas. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Conference on Computer Vision and Pattern Recognition (CVPR) 2017

 

Victor F. Strîmbu, Bogdan M. Strîmbu, A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data,ISPRS Journal of Photogrammetry and Remote Sensing, Volume 104, 2015, Pages 30-43, ISSN 0924-2716, https://doi.org/10.1016/j.isprsjprs.2015.01.018.

 

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H. Ni, X. G. Lin, J.X.Zhang. APPLICATIONS OF 3D-EDGE DETECTION FOR ALS POINT CLOUD. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-2/W7, 2017 ISPRS Geospatial Week 2017, 18–22 September 2017, Wuhan, China

 

Nebiker S, Bleisch S, Christen M (2010): Rich point clouds in virtual globes – A new paradigm in city modeling? Computers, Environment and Urban Systems, 34(6): 508–517.

 

Wallerman J, Bohlin J, Fransson JE, Lundberg K (2009): Forest data capture using optical 3D digital surface models from the C3 Technologies system. ASPRS/MAPPS 2009 Specialty Conference, Digital mapping–From Elevation to Information. San Antonio, Texas, United States, pp. 16–19.

 

Biljecki F (2017): Level of detail in 3D city models. PhD thesis, TU Delf, 353 pp.

 

Garland M, Heckbert PS (1997): Surface Simplification Using Quadric Error Metrics. Proceedings of the 24th annual conference on Computer graphics and interactive techniques SIGGRAPH 97. ACM Press/Addison-Wesley Publishing Co., Los Angeles, CA, United States, pp. 209–216.

 

Bauchet Jean-philippe, Lafarge Florent (2019): City Reconstruction from Airborne LIDAR: A computation geometry approach.

 

Stoter Jantien, Vallet Bruno, Lithen Thomas, Pla Maria, Wozniak Piotr, Kellenberger Tobias, Streilein Andre, Ilves Thobias Risto, Ledoux Hugo (2016): State-of-the-art of 3D national mapping. Kemp, A. (2011). BIM isn’t Geospatial .... Or is it?. https://communities.rics.org/gf2.ti/f/200194/67 68101/pdf/-/RICS12Pres_ACK_Speech.pdf

 

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A. Just Orriols, J. Vayreda Duran. Segunda Edición De Los Mapas de Variables Biofísicas Del Arbolado de Cataluña. 8° Congreso Forestal Español (2022)

 

Martín-Alcón S, Coll L, De Cáceres M, Guitart L, Cabré M, Just A, González-Olabarria JR (2015) Combining aerial LiDAR and multispectral imagery to assess post-fre regeneration types in a Mediterranean forest. Can J For Res 45(7):56866. https://doi. org/10.1139/cjfr-2014-0430

 

Blázquez-Casado Á, González-Olabarria JR, Martín-Alcón S, Just A, Cabré M, Coll L (2015) Assessing post-storm forest dynamics in the Pyrenees using high-resolution LIDAR data and aerial photographs. J Mt Sci 12:841–853. https://doi.org/10.1007/ s11629-014-3327-3

 

Olivier Martin-Ducup, Jean-Luc Dupuy, Maxime Soma, Juan Guerra-Hernandez, Eva Marino, Paulo M. Fernandes, Ariadna Just, Jordi Corbera, Marion Toutchkov, Charlie Sorribas, Jerome Bock, Alexandre Piboule, Francesco Pirotti, François Pimont, Unlocking the potential of Airborne LiDAR for direct assessment of fuel bulk density and load distributions for wildfire hazard mapping, Agricultural and Forest Meteorology, Volume 362,2025,110341,ISSN 0168-1923, https://doi.org/10.1016/j.agrformet.2024.110341.

 

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Software

QGIS

ArcGIS Desktop

LASTools

Cloudcompare

Fusion (USDA)

Google Analytics


Groups and Languages

Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.