155ISDP Introduction to Spatial Data Processing

Z GeoWikiCZ

Basic Information

  • Up-to-date and complete information are ON THIS PAGE
  • Code: 155ISDP
  • Lectures:

Ing. Martin Landa, Ph.D.

  • Practical classes:

Ing. Martin Landa, Ph.D. (head of the classes), Ing. Ondřej Pešek, Ph.D.

  • lectures per week: 2 hours
  • exercises per week: 2 hours
  • Credits: 6
  • finished with: an exam / project
  • summer semester

Anotation

Introduction into geospatial data processing. Various workflow automation techniques (interactive tools, Python programming language, and Structured Query Language). Demonstration of various processing environments (desktop applications, database environments, cloud-based computing).

Lectures

Course tutors: Ing. Martin Landa, Ph.D. (ML), Ing. Ondřej Pešek, Ph.D. (OP)

  • Wednesday 4-5:40pm room: B-870
  • Thursday 9-10:40am room: B-s111
  1. (19+20.2.) [ML] Introduction into GIS, open geospatial datasets
  2. (26+27.3.) [OP] Geospatial data and web services
  3. (05+06.3.) [OP/ML] Data processing automation
  4. (12+13.3.) [ML] Introduction into Python data processing (Esri)
  5. (19+20.3.) [OP] Introduction into Python data processing (open source)
  6. (26+27.3.) [OP] Geospatial Python packages
  7. (02+03.4.) [ML] Geospatial Python packagesSpace-time geospatial data processing
  8. (09+10.4.) [ML] Introduction into databases, SQL
  9. (16+17.4.) Class cancelled
  10. (23+24.4.) [ML] Geospatial data in SQL
  11. (29.4+7.5.) [ML] Geospatial data processing in SQL (change: 29.4. 9-10:40am B-s111)
  12. (14+15.5.) [TB] Cloud-based geospatial data processing

Exercises

Materials: https://geo.fsv.cvut.cz/courses/155isdp/

Sample data: https://geo.fsv.cvut.cz/courses/155isdp/data

JupyterHub: http://gislab.fsv.cvut.cz:8000/hub

Assignments

Each student will work on two mini-projects, one using Python and the other using SQL. Student's evaluation is based on successful fulfillment of these two assignments.

Project assignment/requirements:

  • use open geospatial data as input
  • demonstrate knowledge of automated geospatial data processing and its analysis
  • provide results interpretation (discussion, graphs, tables, ...)

Python-based project assignment

  • Assignment 27.3.
  • Presentation 29.4.

Expected submission form: Jupyter Notebook or Python script (input data included)

SQL-based project assignment

  • Assignment 24.4.
  • Presentation 15.5.

Expected submission form: SQL file (input data included)

Install instructions for MS Windows

  • QGIS
  • GRASS GIS (on MS Windows GRASS GIS is installed together with QGIS)

JupyterLab

Or use online platforms, eg. Google Colab

In Jupyter notebook run 

!pip install pandas geopandas

to install required packages.

Literature

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