14th March 2016

Hands-on Open Source Drone Mapping and High Performance Computing for Big Geo-Data

Matera, 13-17 June 2016


Webinars / online lectures and tutorials:

This summer school is an immersion 5 day experience on advanced data processing using high performance computers (HPC) and emerging technologies such as drone mapping, rasdaman (Fastest Array Database on Earth) and cloud computing. We provide a walk-through journey from the introduction of Linux operating system and different open source software, to capturing data out in the field using an Unmanned Aerial Vehicle, complex image processing and data analyses. We focus on on how to process data in different environments according to data types and size: maximising computation performance using multicore on single computer, switching to distributed clusters of computers (using grid engine scheduler) and ultimately data analytics with cutting edge rasdaman software (Big Data Analytics Server).

Course requirements:

The summer school is aimed at students who are already using a command line approach in spatial data processing either via Python, R or GRASS. We expect students to have basic knowledge of geographical data analyses and the use of Geographic Information Systems.

It is an advanced training following the fundamentals thought during the previous week (6-10 June) summer school on Spatio-Temporal data analyses using free and open source software. We recommend beginner students to follow both summer school.

Without any previous knowledge on command line spatial data analyses and GIS it is only possible to follow the Drone mapping sessions exclusively (Day 1 and Day 2).

Students need their own laptops with a minimum of 4GB RAM and 30GB free disk space.


Registration is on a first come, first served basis and it will be closed when it reaches 30 participants. Therefore, we encourage participants to register ASAP. A waiting list will be established in case exceeding the limit.

Academic programme:

The summer school provides students with the opportunity to develop key skills required for advanced spatial data processing. Throughout the training students will focus on developing independent learning skills which will be fundamental for a continuous learning development in advanced data processing. This is a continuous journey of development with the availability of more complex data and the ongoing technological revolution. During this week we guide students dealing with the access of Linux OS and data processing software types, gathering field data using a drone UAV, as well as active processing of that data on single computers, on cloud facilities and on computer clusters using grid engine scheduler. We have also assembled a fully functional micro cluster computer “Grappolowhich is a portable and pedagogic machine replicating the operating system and functioning of a real high performance computer. Students will access “Grappolo” and learn how to process BIG spatial data using scripting routines within a grid scheduler environment. Considering open source software, we will show different types of cutting edge technologies: GRASS and R scripting, different python libraries, rasdaman (Fastest Array Database on Earth) and opendronemap (data processing toolchain for civilian unmanned aerial system image processing).

Overall, we focus our training 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 four main areas of study:

  • Field experiment: Demonstration of a flight mission for very high resolution data gathering. Case study: natural environment, precision agriculture.
  • Lectures: (15min to 1h each) Students will take part in a series of lectures introducing basics functioning of tools, theoretical aspects and background information, which is needed for a better understanding the profound concepts to be successively applied in advanced data processing.
  • Hands on Tutorials: Students will be guided during hands on session where trainers will perform data analyses on real case study datasets (including data gathered in the field with drone) and students will follow the same procedure using their laptops. During tutorials session students are supported by two trainers, one for the demonstrations and one to supervise the students’ work as well as helping with individual guidance on coding.
  • Hands on Exercise: In addition to tutorial and lectures, students are encouraged to take up their own 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.

Learning objectives

Our summer school will enable students to further develop and enhance their spatio-temporal data processing skills. We will focus on the understanding of dataflows, bottlenecks expanding processing time and how to maximize the use of single laptops using multi cores to distributed computing systems (HPC clusters, cloud computing).

Additionally, our open source approach will allow participants to start using professionally a fully functional operating system and software. With continuous practise during the week students will improve their command line approach and will focus on developing specific areas, including:

  • Developing a broad knowledge of existing solutions for Big Data processing and be able to judge the most appropriate for their needs.
  • Building confidence with the use of open source spatial data software and languages (python, Grass, R, rasdaman, opendronemap) and with Linux operating system.
  • Developing data processing skills on cluster computers, cloud processing services on demand and the use of grid scheduler; 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 certificate 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



Course programme

  • Day 1:        LINUX and OSGeo-live operating system / Hands-on Drone mapping
  • Day 2:        Processing drone data for mapping & monitoring / Spatial data processing with python
  • Day 3:        Optimizing data processing with multicore and cloud processing
  • Day 4:        Big geo-spatial data processing: Rasdaman fastest data array on earth
  • Day 5:        Cluster data processing and grid engines

Detailed course programme



Fellowship award

We offer two types of fellowships for this summer school:

Social media fellowships  — CLOSED —

A grant of 100 Euro will be awarded to student posting multi-media content on facebook and twitter before, during and after the summer school. We are looking for someone with experience in the use of social media. This fellowship is open to all participants. Contact info@spatial-ecology.net for more details

University of Basilicata fellowships

Three students from the University of Basilicata will be able to attend the course free of the summer school fees. An awards committee from the U. of Basilicata and Spatial Ecology will select best candidates and we encourage students to apply ASAP. This call is closing 15.5.2016. During your registration please specify that you are a student from the University of Basilicata on the form to be filled and send us a short resume and motivation letter.

Discount for participants attending both weeks

Participants receive a 50% discount for the second week of training. So professionals pay  820GBP + 410GBP and students 410GBP + 205GBP for 2 weeks’ training.

Developing country fellowships — CLOSED —

A reduction of the fees may apply to participants from developing countries (look up list here). Please contact info@spatial-ecology.net.