Hatarilabs has developed a complete program that offers a comprehensive exploration of the basic concepts to cutting-edge techniques for geospatial analysis, land cover evaluation, geomachine learning and surface modeling with Python. The program was designed to give the student the fundamental knowledge necessary to excel in advanced environmental modeling, spatial land analytics or applied machine learning of surface features. The program content incorporates the research findings on the latest versions from the most powerful spatial analysis libraries in Python as Shapely, Geopandas, Rasterio, Scikit Image and Landlab.
The program has an approach of step-by-step learning coupled with applied examples that covers from basic Python concepts to most complex use of geospatial and machine learning libraries.
Objectives
The objectives of this diploma include the following:
Understand the principles and concepts of geospatial analysis, including spatial data types, coordinate systems, projections, and spatial analysis techniques.
Develop proficiency in Python programming language for data analysis and geospatial analysis tasks.
Develop skills to assess and analyze land cover types using remote sensing data and satellite imagery.
Explore the application of machine learning algorithms and techniques to geospatial data.
Acquire knowledge and skills in surface analysis and modeling of surface processes.
Apply the acquired knowledge to solve real-world problems related to land cover evaluation, surface modeling, and geospatial analysis.
Content
The diploma is divided into six modules, described below.. See the complete diploma syllabus on this link.
The summarized content of every module is described below:
Module 1: Python Fundamentals
This course develops the basic concepts of Python programming under Anaconda and Jupyter. Exercises will cover the basic Python data structures, conditional statements, loops coupled with an introduction to array manipulation in Numpy, tabular data management with Pandas and applied exercises with precipitation data …more info.
Session 1: Anaconda distribution and Jupyter interface
Session 2: Python data types
Session 3: Python loops and data structures
Session 4: Numpy and matplotlib for water resources
Session 5: Precipitation data analysis with Pandas
Session 6: Precipitation and streamflow data analysis and visualization …more info.
Module 2: Vectorial data analysis with Python
Once we have covered the fundamentals topic on Python, we will learn how to use Python for vector data analysis with different libraries such as Shapely, Geopandas and Fiona …more info.
Session 1: Introduction to Fiona
Session 2: Spatial Analysis of Total Coliforms with Fiona
Session 3: Introduction to Shapely
Session 4: Processing spatial vector data
Session 5: Introduction to geopandas
Session 6: Analysis of flooded areas with Geopandas …more info.
Module 3: Raster data analysis with Python
Raster data processing is an important task in geospatial analysis that include cropping and reprojecting raster data, using raster math to derive new rasters, and reclassifying rasters using a set of values …more info.
Session 01: Introduction to Rasterio
Session 02: Basic operation with rasters images - Glacier delimitation
Session 3: Landsat 8 imagery processing and analysis with Python and Rasterio
Session 04: Geospatial interpolation with Python, Scipy, Geopandas and Rasterio
Session 05: Creation of an Elevation Raster from Contour Lines
Session 06: Watershed and Stream Network Delimitation with Python and Pysheds …more info.
Module 4: Geomachine Learning for Crop Identification with Python
This module develops topics focused on crop identification / delineation with geospatial and machine learning tools of Python. The course covers the introductory concepts of the geospatial libraries and a series of machine learning applications for crop identification in olive trees, palms, agave and corn fields …more info.
Session 01: Tree Counting classification with Scikit Image and Python
Session 02: Geospatial crop counting of palm trees
Session 03: Crop line detection for corn crops
Session 04: Spatial Python class for crop recognition
Session 05: Interactive crop identification for olive trees
Session 06: Crop fields delimitation using marching squares method with Python …more info.
Module 5: Land cover analysis with Python and geospatial libraries
This course is oriented to the application of Python and its spatial and machine learning libraries for applied cases of land cover analysis and land cover dynamics. The module is specifically oriented on land cover analysis techniques on applied cases …more info.
Session 01: Land cover change analysis with Python and Rasterio
Session 02: Land use change analysis from vector data with Python
Session 03: Delimitation of water bodies with Canny filters
Session 04: Land cover classification using a Naives Bayes algorithm with Python
Session 05: Glacier delimitation using image segmentation with Scikit Image
Session 06: River and riparian zone delimitation based on segment analysis with Scikit Learn …more info.
Module 6: Surface process modeling with Landlab and Python
Landlab is a Python library for the numerical simulation of surface processes. The library is designed for scientific fields and calculates the dynamics of earth surface such as geomorphology, hydrology, glaciology, stratigraphy and others related. This course covers applied examples using Landlab and other libraries for different cases of surface analysis …more info.
Session 01: Introduction to surface process modeling with Landlab
Session 02: River erosion simulation with Landlab
Session 03: Stream network delimitation with Landlab
Session 04: Modeling Land Evolution at Basin Scale with Python and Landlab
Session 05: Flood simulation over a surface with Landlab
Session 06: Simulation of surface and groundwater flow on a conceptual catchment …more info.
Trainer
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 / Examination
Mode: Online with streaming - Synchronous
Some details about the diploma methodology:
The manuals and files for the exercises will be delivered through our online platform.
The course will be developed by video streaming with life support and interaction.
Recorded videos will be available on our eLearning platform.
There is online support for questions regarding the exercises developed through email and meetings.
Video of the classes will be available for six months.
The exams are certification is organized as follows:
The program has three exams that comprise the content of 2 courses.
A digital certificate is available at the end of the program upon exam approval.
To receive the digital certificate, you must submit the exams on the following date:
First exam before 30st October 2023,
Second exam before 21st December 2023.
Third exam before 22st February 2024.
Date and time
The course is offered in sessions of approximately 1.5 to 2 hours. All sessions start at 6pm Central European Time (CET) - Amsterdam Time.
Module 1 - September 2023 (12, 14, 19, 21, 26 and 28)
Module 2 - October 2023 (10, 12, 17, 19, 24 and 26)
Module 3 - November 2023 (02, 07, 09, 14, 16 and 21)
Module 4 - December 2023 (05, 07, 12, 14, 19 and 21).
Module 5 - January 2024 (09, 11, 16, 18, 23 and 25)
Module 6 - February 2024 (06, 08, 13, 15, 20 and 22)
Cost and payment method
The normal cost of the program is $ 1250 dollars.
Registry
After payment with Paypal, fill out the following registration form including the information related to your payment. We will send you an e-mail to confirm your registration.
For any other information please write to: saulmontoya@hatarilabs.com
