Open source drone mapping

Matera, 13-14 June 2016

The open source spatio-temporal data analyses and processing summer school is an immersion 5 day experience opening new horizon on the use of the outstanding power of the Linux environment and the command line approach to process data.

We will guide newbies who have not ever used a command line terminal to a stage which will allow them to understand and use very advanced open source data processing routines. Our focus is on the self-learning perspective allowing participants to keep on progressing and updating their skills in a continuously evolving technological environment.


Course requirement:

The summer school is aimed at students who are currently at the master or doctoral level, as well as researchers and professionals with a common interest in spatio-temporal data analysis and modelling. Nonetheless we accept candidatures from undergraduate students. Participants should have basic computer skills, and a strong desire to learn command line tools to process data. We expect students to have a special interest on geographical data analyses and it will help them to already have experienced the use of Geographic Information Systems. Students need to bring their own laptop with a minimum of 4GB RAM and 30GB free disk space.

Academic program:

The summer school provides students with the opportunity to develop vital skills required for advanced spatial data processing. Throughout the week programme students will focus on developing independent learning skills which will be fundamental for continuing in a process of advanced data processing which is a continuous journey in evolution with the availability of more complex data and the ongoing technological revolution. Many different, complementary and sometimes overlapping tools will be presented to provide an overview of the existing arena of open source softwares available for spatial data processing. We show their strengths, weakness and specificity for different data processing objectives (ex.: modelling, data filtering, query, GIS analyses, graphics or reporting) and data types. Specifically, we guide students to practice the use of softwares and tools with the objective of helping them to step up from the steep side of the learning curve generally experienced while  using a new way of analysing data with a programming command line approach. Broadly, we focus our trainings on helping students to develop independent learning skills to find online help, solutions and strategies to fix bugs and independently progress with complex data processing problems.

The Academic Programme is divided into 3 main areas of study:

Lectures: (15min to 1h each) Students will take part in a series of lectures introducing basics functioning of tools, theoretical aspects or background informations needed for understanding concepts to be successively applied in data processing.

Hands on Tutorials: Students will be guided during hands on session where trainers will perform data analyses on real case study datasets and students fill follow the same procedure using their laptops. During tutorials session students are followed by two trainers, one for the demonstrations and one to supervise student’s work and helping with individual guidance on coding.

Hands on Exercise: In addition to tutorial and lectures, students are encouraged to embark on independent study during exercise sessions. Specific tasks will be set allowing to reinforce the newly learned data processing capacity presented in lectures and practically learned during the tutorial sessions. Such exercise sessions equip students with the confidence and skills to become independent learners and effectively engage with the demands of advanced spatial-data processing.

According to the number of participants and to their pre-existing knowledge in programming more or less topics can be addressed in according to student’s needs. The exercises and examples will be cross disciplinary: forestry, landscape planning, predictive modelling and species distribution, mapping, nature conservation, computational social science and other spatially related fields of study. Nevertheless these case studies are template procedures and could be applied to different thematic applications and disciplines.

Learning objectives

Our summer school will enable students to further develop and enhance their spatio-temporal data processing skills. Most importantly will allow them, to start using professionally a fully functional open source operating system inclusive of all required software toolkit. Through continuous practise during the week students will familiarize with a command line approach and focus on developing specific areas, including:

  • Developing a broad knowledge of existing tools and be able to judge the most appropriate for their needs and which have more potentials for future learning.
  • Building confidence with the use of several command line utilities for spatial data processing and with Linux operating system.
  • Developing data processing skills and knowing more on data type, data modelling and data processing techniques.
  • Encouraging independent learning, critical thinking and effective data processing.

Summer school certification

At the end of the summer school the attendees will receive a course certification upon successful completion of the course, although it is up to the participant’s university to recognize this as official course credit.


Time table: (7h teaching/day)

9:00 – 10:45   morning session 1         1h45

10:45 – 11:05  coffee break

11:05 – 12:50  morning session 2        1h45        

12:50 – 14:00  Lunch

14:00 – 15:45  afternoon session 1        1h45

15:45 – 16:00  break

16:00 – 17:45  afternoon session 2        1h45