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Online Course: Geomachine Learning for Crop Identification with Python - 12, 14, 16, 19, 21, 23 July 2021


  • Avenida Caminos del Inca 288 Surco, Lima Peru (map)
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The development of machine learning and geospatial libraries in Python as Scikit Image, Rasterio and Fiona gave us a new range of tools to analyze vegetation from traditional sources as satellite imagery and new sources as drone orthophotos. The appliance of Python programming for crop identification depends on many factors such as the image resolution, the vegetation stage and even the algorithm setup.

We have developed a course crop identification / delineation with geospatial and machine learning tools of Python, the course is aimed to GIS or related professionals that have basic Python knowledge. The course covers the introductory concepts of the geospatial libraries and a series of machine learning applications for crop indentification in olive trees, palms, agave and corn fields.

Note:

This course is for professionals with previous knowledge of Python. Basic concepts of Python programming will not be covered in the lessons and course material. In case you don’t have any knowledge of Python, we recommend you to take this online course.


Objectives

At the end of this course the student will be capable of:

  • Manage vector and raster spatial data with Python

  • Plot spatial data with Matplotlib and perform filtering

  • Apply different tools of Scikit Learn for geospatial objects

  • Work with a Python class for crop recognition

  • Have a overview of Python coding to develop custom solutions


Content

The course is divided into 6 sessions. These are topics considered for each session:

Session 1: Vector spatial data exploration with Fiona and Python

  • Installation of Python, Fiona, Rasterio under a Conda environment.

  • Open a shapefile / vector data with Fiona

  • Get spatial object features

  • Explore spatial objects metadata and geometry

  • Spatial data representation with matplotlib

  • Analysis of the supported spatial vector data types

  • Creation of point, line and polygon shapefiles

  • Spatial data filtering by attributes

Session 2: Raster data management with Rasterio and Python

  • Read monoband and panchromatic Tiff images.

  • Explore raster dataset info and attributes.

  • Analyze spatial information.

  • Read raster band data.

  • Raster plotting options with Rasterio and Matplotlib.

  • Clip rasters with shapefile in Python.

  • Raster algebra example: NDVI vegetation index calculation.

Session 3: Tree Counting Classification with Scikit Image and Python

  • Explore single crop and multiple crop templates.

  • Perform match template for a single and multiple crop.

  • Analyze the match template array.

  • Define filter criteria and count crops.

  • Representation of identified crops over raster image.

Session 4: Geospatial crop counting of palm trees

  • Open raster and vector files with Python

  • Coupled representation of spatial data with Matplotlib and Rasterio

  • Template extraction for spatial point data

  • Auxiliary template generation for different angles

  • Match template for a group of templates

  • Representation of the interpreted points

  • Cluster analysis with Birch algorithm

  • Representation of clustered points

  • Export points as csv

Session 5: Crop line detection for corn crops

  • Import raster ad read raster bands

  • Perform Canny filter for edge detection

  • Identification of lines with Hough line transform

  • Convert results to geospatial data

  • Save resulting line to shapefile

  • Representation of interpreted crop rows

Session 6: Spatial Python class for crop recognition

  • Overview of the Python class coding

  • Define crop orthophoto and point plant location

  • Define parameter for the match template and raster band

  • Perform single match templates

  • Match template for all points

  • Cluster analysis and save results as shapefile

Final Exam


Trainer

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Saul Montoya M.Sc.

Hydrogeologist - Numerical Modeler

Mr. Montoya is a Civil Engineer graduated from the Catholic University in Lima with postgraduate studies in Management and Engineering of Water Resources (WAREM Program) from Stuttgart University – Germany with mention in Groundwater Engineering and Hydroinformatics. Mr Montoya has a strong analytical capacity for the interpretation, conceptualization and modeling of the surface and underground water cycle and their interaction. 

He is in charge of numerical modeling for contaminant transport and remediation systems of contaminated sites. Inside his hydrological and hydrogeological investigations Mr. Montoya has developed a holistic comprehension of the water cycle, understanding and quantifying the main hydrological dynamic process of precipitation, runoff, evaporation and recharge to the groundwater system. 

Over the last 9 years Saul has developed 2 websites for knowledge sharing in water resources: www.gidahatari.com (Spanish) and www.hatarilabs.com (English) that have become relevant due to its applied tutorials on groundwater modeling, spatial analysis and computational fluid mechanics.


Methodology

Some details of the course methodology:

  • Manuals, input files and exercises will be delivered.

  • The teaching part is developed by videos in our elearning platform.

  • Online support for questions regarding the course content.

  • Practical lessons videos will be available forever.

  • To achieve a digital certificate you must submit the final exam after 1 month.

  • Digital certificate available at approval of the final exam.

Date and time

July 2021 (Amsterdam Time)

  • Monday 12 from 6:00 pm to 9:00 pm.

  • Wednesday 14 from 6:00 pm to 9:00 pm.

  • Friday 16 from 6:00 pm to 9:00 pm.

  • Monday 19 from 6:00 pm to 9:00 pm.

  • Wednesday 21 from 6:00 pm to 9:00 pm.

  • Friday 23 from 6:00 pm to 9:00 pm


Cost and payment method

Price: $180 ($160 before 30th June 2021)

This online course will be given on out elearning platform: elearning.hatarilabs.com . You will need to create an account first and then make your payment.

For any other information please write to: saulmontoya@hatarilabs.com