Training Course on Open-Source Geospatial Libraries in Python (Fiona, Rasterio)

Training Course on Open-Source Geospatial Libraries in Python (Fiona, Rasterio)

Introduction

Training Course on Open-Source Geospatial Libraries in Python (Fiona, Rasterio) empowers participants with the essential skills to proficiently handle and analyze geospatial data using industry-standard open-source Python libraries, specifically Fiona and Rasterio. In today's data-driven world, the ability to automate geospatial workflows, perform advanced spatial analysis, and extract meaningful insights from diverse vector and raster datasets is crucial for professionals across various sectors. This course is designed to bridge the gap between theoretical understanding and practical application, ensuring participants can confidently implement Python programming for real-world GIS challenges, fostering innovation and efficiency.

Participants will delve into the intricacies of Fiona for seamless vector data I/O and Rasterio for robust raster image processing. Through hands-on exercises and practical case studies, learners will gain expertise in data manipulation, coordinate reference system (CRS) transformations, geospatial data visualization, and the automation of complex tasks. The curriculum emphasizes a practical, project-based approach, equipping attendees with the coding skills and analytical prowess necessary to thrive in the rapidly evolving landscape of geospatial technology and data science.

Course Duration

10 days

Course Objectives

This training aims to equip participants with the ability to:

1. Efficiently read and write various vector (Shapefile, GeoJSON, GeoPackage) and raster (GeoTIFF, NetCDF) formats using Fiona and Rasterio.
2. Proficiently handle and transform CRS to ensure data alignment and accuracy in geospatial projects.
3. Execute core vector spatial analysis tasks like buffering, intersections, unions, and re-projections with Fiona.
4. Conduct advanced raster image processing including clipping, masking, band manipulation, and raster algebra (e.g., NDVI, NDWI) using Rasterio.
5. Develop Python scripts to automate repetitive GIS tasks, enhancing productivity and reducing manual effort.
6. Leverage NumPy for numerical operations on raster arrays and Pandas for tabular attribute data management, extending geospatial analytics.
7. Create compelling static and interactive geospatial visualizations using complementary libraries like Matplotlib and Folium.
8. Learn techniques for efficient processing and management of large geospatial datasets that may not fit into memory.
9. Execute complex spatial queries and filters on both vector and raster data for targeted analysis.
10. Understand the foundational concepts of geospatial data science in the context of open-source tools
11. Diagnose and resolve common errors encountered during geospatial data processing in Python.
12. Gain an introduction to concepts of processing geospatial data in cloud environments using these libraries.
13. Develop Practical Solutions: Apply learned skills to solve real-world geospatial challenges through practical exercises and case studies.

Organizational Benefits

• Automation of repetitive GIS tasks through Python scripting drastically reduces manual effort and processing time.
• Leveraging open-source GIS tools eliminates the need for expensive proprietary software licenses.
• Deeper insights from advanced spatial analysis lead to more informed strategic planning and operational decisions.
• Ability to work seamlessly with diverse geospatial data formats, promoting data sharing and collaboration.
• Equipping staff with cutting-edge geospatial programming skills enhances their professional development and engagement.
• Open-source solutions offer greater flexibility and scalability to adapt to evolving project requirements and data volumes.
• Fosters an environment of innovation by empowering teams to develop custom geospatial solutions and analytical models.
• Python scripts ensure that geospatial analyses are reproducible, auditable, and easily shared within the organization.

Target Audience

1. GIS Professionals & Analysts
2. Environmental Scientists & Researchers
3. Urban Planners & Demographers.
4. Data Scientists & Analysts.
5. Software Developers.
6. Remote Sensing Specialists.
7. Civil Engineers & Surveyors
8. Anyone with Python Basics

Course Outline

Module 1: Introduction to Geospatial Python & Environment Setup

• Understanding the Python Geospatial Ecosystem: Overview of key libraries
• Setting up your Python development environment: Anaconda, Miniconda, virtual environments.
• Introduction to Jupyter Notebooks and JupyterLab for interactive coding.
• Basic Python refresher for data types, control flow, and functions.
• Accessing and navigating geospatial data sources.
• Case Study: Setting up an environment for a project to analyze deforestation using satellite imagery.

Module 2: Fundamentals of Coordinate Reference Systems (CRS)

• Geographic vs. Projected Coordinate Systems: Understanding their differences and applications.
• EPSG Codes and their significance in geospatial data.
• Identifying and inspecting CRS using Fiona and Rasterio.
• Reprojecting geospatial data between different CRSs.
• Common CRS issues and best practices for managing them.
• Case Study: Aligning a road network shapefile (WGS84) with a local urban planning map (UTM zone) using Fiona.

Module 3: Vector Data Handling with Fiona - Part 1 (Reading & Writing)

• Introduction to Fiona: A simple API for reading and writing GIS files.
• Opening and inspecting vector datasets (Shapefile, GeoJSON, GeoPackage).
• Understanding the Fiona data model: Collections, Features, Geometry, Properties.
• Iterating through features and accessing geometry and attributes.
• Writing new vector files and defining schemas.
• Case Study: Ingesting a GeoJSON file of city parks and exporting it as a ESRI Shapefile, inspecting its schema.

Module 4: Vector Data Handling with Fiona - Part 2 (Filtering & Selection)

• Attribute-based filtering of vector data using Python logic.
• Spatial filtering: Selecting features based on bounding boxes or geometries.
• Efficiently handling large vector datasets with Fiona's streaming capabilities.
• Combining Fiona with Shapely for geometric operations.
• Creating new datasets from filtered selections.
• Case Study: Extracting all buildings within a specific administrative boundary from a large OpenStreetMap dataset.

Module 5: Raster Data Processing with Rasterio - Part 1 (Reading & Metadata)

• Introduction to Rasterio: Reading and writing geospatial raster data.
• Understanding raster data models: Bands, Pixels, NoData values.
• Opening and inspecting raster datasets (GeoTIFF, NetCDF, JPEG2000).
• Accessing raster metadata: CRS, transform, dimensions, data types.
• Reading specific bands and windows of raster data.
• Case Study: Loading a satellite image and extracting its metadata (e.g., resolution, acquisition date, CRS).

Module 6: Raster Data Processing with Rasterio - Part 2 (Manipulation & Writing)

• Clipping rasters using vector geometries or bounding boxes.
• Masking raster data to focus on areas of interest.
• Performing raster algebra
• Writing processed raster data to new GeoTIFF files.
• Handling multiple raster bands and creating composite images.
• Case Study: Calculating NDVI for an agricultural area and saving the resulting raster.

Module 7: Integration with NumPy for Advanced Raster Operations

• Leveraging NumPy arrays for efficient raster data manipulation.
• Performing complex mathematical operations on raster bands.
• Applying custom functions and filters to raster data.
• Working with multi-dimensional raster data.
• Optimizing NumPy operations for large raster files.
• Case Study: Developing a custom Python function to detect water bodies from a multi-spectral image using thresholding on specific bands.

Module 8: Geospatial Data Visualization with Matplotlib & Folium

• Basic geospatial plotting with Matplotlib and GeoPandas.
• Visualizing vector data with different symbologies.
• Displaying raster data and overlaying vector layers.
• Creating interactive web maps using Folium.
• Adding markers, popups, and custom layers to Folium maps.
• Case Study: Creating an interactive map of urban heat islands by overlaying a temperature raster on a base map with city boundaries.

Module 9: Advanced CRS and Transformations

• Proj4 strings and WKT (Well-Known Text) representations of CRS.
• Advanced techniques for CRS detection and handling.
• Transforming geometries and rasters between arbitrary CRSs.
• Handling vertical coordinate systems (VCS).
• Best practices for ensuring geospatial data consistency.
• Case Study: Re-projecting a global elevation model from geographic coordinates to a local projected system for accurate area calculations.

Module 10: Performance Optimization & Large Data

• Strategies for handling large geospatial files that exceed memory limits.
• Chunked reading and writing with Rasterio for large rasters.
• Utilizing Fiona's bounding box filters for selective reading.
• Introduction to Dask-GeoPandas and Dask-Rasterio for scalable computing.
• Memory management and profiling techniques for geospatial scripts.
• Case Study: Processing a multi-gigabyte satellite image mosaic by processing it in smaller, manageable chunks.

Module 11: Error Handling and Debugging in Geospatial Python

• Common errors in geospatial data processing
• Strategies for identifying and debugging issues in Fiona and Rasterio scripts.
• Implementing robust error handling with try-except blocks.
• Logging and reporting for automated geospatial workflows.
• Best practices for writing clean and maintainable geospatial code.
• Case Study: Debugging a script that fails to overlay two shapefiles due to differing CRSs, and implementing a CRS check.

Module 12: Geospatial Data Validation and Quality Control

• Techniques for validating geometries using Shapely.
• Identifying and fixing topological errors in vector data.
• Checking raster data integrity
• Automated quality control checks for incoming geospatial datasets.
• Reporting and visualizing data quality issues.
• Case Study: Validating a digitized parcel map for self-intersections and correcting invalid geometries programmatically.

Module 13: Introduction to Geospatial Data Science with Fiona & Rasterio

• Overview of spatial statistics concepts.
• Basic spatial interpolation techniques
• Extracting features from raster data for analysis.
• Aggregating raster data by vector polygons.
• Preparing geospatial data for machine learning models.
• Case Study: Extracting average land surface temperature (from a raster) for different land cover types

Module 14: Advanced Topics & Emerging Trends

• Introduction to Cloud-Native Geospatial: COGs (Cloud Optimized GeoTIFFs) and STAC
• Brief overview of other powerful geospatial Python libraries
• Web geospatial services integration with Python.
• Working with vector tiles and raster tiles.
• Future trends in open-source GIS and geospatial AI.
• Case Study: Accessing and visualizing a COG from a cloud storage bucket.

Module 15: Project Workshop & Best Practices

• Applying learned skills to a comprehensive, multi-component geospatial project.
• Designing and implementing a complete geospatial workflow from data acquisition to visualization.
• Code organization and modularity for larger geospatial projects.
• Version control with Git for collaborative geospatial development.
• Sharing and deploying geospatial analysis results.
• Case Study: A capstone project involving the analysis of urban growth patterns over time using multi-temporal satellite imagery and census data, generating a report and interactive map.

Training Methodology

Our training methodology for this course is designed for maximum practical impact and skill retention. It is a highly interactive, hands-on, and project-based approach:

• Interactive Lectures: Concise theoretical explanations followed by immediate practical demonstrations.
• Live Coding Sessions: Instructor-led coding sessions where participants code along.
• Hands-on Exercises: Practical assignments after each module to reinforce learning and apply concepts.
• Real-World Case Studies: Application of learned skills to solve actual geospatial problems, fostering critical thinking.
• Guided Projects: Participants work on a larger, integrated project, building a complete geospatial workflow.
• Collaborative Learning: Encouraging peer-to-peer learning and problem-solving.
• Q&A and Troubleshooting: Dedicated time for addressing participant queries and debugging assistance.
• Resource Sharing: Provision of comprehensive code notebooks, datasets, and supplementary reading materials.
• Practical Demonstrations: Visual examples and live coding to illustrate complex concepts clearly.
• Blended Learning: A mix of synchronous (live sessions) and asynchronous (self-paced exercises, pre-recorded content) learning.

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104 

 

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

 We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to DATASTAT CONSULTANCY LTD account, as indicated in the invoice so as to enable us prepare better for you.