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COURSE TRAINERS

  • Spatial Ecology course trainers

COURSES AROUND THE WORLD

  • Western Connecticut State University 2021
  • Stockholm University 2021
  • GeoComp & ML 2022 course
  • GeoComp & Modelling 2022 course
  • GeoComp & ML 2023 course
  • GeoComp & ML 2024 course
  • GEO-OPEN-HACK-2024
  • GeoComp 2024 course
  • Geo Comp/Analysis 2025 course
  • GeoComp & ML 2025 course

GEO DATA

  • Geomorpho90m: technical documentation

LINUX VIRTUAL MACHINE

  • Prepare Colab for Spatial Ecology courses
  • Prepare OSGeoLive for Spatial Ecology courses

WEB SEMINARS

  • Raster/Vector Processing using GDAL/OGR
  • Image Processing using Pktools
  • Introduction to GRASS GIS
  • GeoComputation with High Performance Computing

BASH

  • Linux Operation System as a base for Spatial Ecology Computing
  • Manipulate text files in bash
  • Multi-core bash

AWK

  • AWK Tutorial

GDAL

  • Use GDAL/OGR for raster/vector operations - osgeo
  • Use GDAL/OGR for raster/vector operations - colab

PKTOOLS

  • Use PKTOOLS for raster/vector operations - osgeo
  • Use PKTOOLS for raster/vector operations - colab
  • Introduction to pyjeo
  • pyjeo: an open source image processing library in Python
  • Performing raster and vector operations in Python using pyjeo
  • Scaling-up: batch processing on the cluster with pyjeo

R

  • R Introduction

PYTHON

  • Python environments or how to survive to your journey in the geodata space
  • Introduction to Python
  • Python & GeoComputation
  • Python data analysis
  • Python geospatial data analysis
  • RasterIO for dummies: a brief intro to a pythonic raster library
  • 2. Preparing the dataset for next ML exercises via rasterio
  • 3. Beyond the basics
  • Generalities about OGC Geospatial extensions for SQL

GRASS

  • GRASS Introduction
  • Start a new GRASS project
  • Using GRASS for stream-network extraction and basins delineation
  • SDM1 : Montane woodcreper - Gecomputation for the Random Forest model using GRASS
  • SDM1 : Montane woodcreper - Random Forest Model using GRASS

HIGH PERFORMANCE COMPUTING

  • Geocomputation at High Performance Computing Cluster (HPC)
  • Use of GRASS in HPC

ASSIGNMENTS

  • Assignments Fall 2022

CASE STUDY

  • SDM1 : Montane woodcreper - Gecomputation
  • SDM1 : Montane woodcreper - Model
  • SDM1 : Montane woodcreper - Gecomputation for the Random Forest model using GRASS
  • SDM1 : Montane woodcreper - Random Forest Model using GRASS
  • SDM2 : Varied Thrush - Model
  • Manipulate GSIM files
  • Data type in GTiff
  • Temporal interpolation of landsat images
  • Dynamic Time Warping
  • Estimating nitrogen and phosphorus concentrations in streams and rivers
  • Estimating nitrogen concentrations in streams and rivers using NN
  • Autoencoder (AE), Variational Autoencoder (VAE) and Generative Adversarial Network (GAN)
  • LSTM Network
  • Estimation of tree height using GEDI dataset - Data explore
  • Estimation of tree height using GEDI dataset - Predictors extraction at point location
  • Estimation of tree height using GEDI dataset - Random Forest prediction
  • Estimation of tree height using GEDI dataset - Support Vector Machine for Regression (SVR) - 2022
  • Estimation of tree height using GEDI dataset - Support Vector Machine for Regression (SVR) - 2023
  • Estimation of tree height using GEDI dataset - Support Vector Machine for Regression (SVR) - 2024
  • Exercise: explore the other parameters offered by the SVM library and try to make the model better. Some suggestions:
  • Estimation of tree height using GEDI dataset - Perceptron 1 - 2022
  • Estimation of tree height using GEDI dataset - Perceptron 1 - 2023
  • Estimation of tree height using GEDI dataset - Perceptron - 2024
  • Estimation of tree height using GEDI dataset - Perceptron tree prediction - 2023
  • Estimation of tree height using GEDI dataset - Perceptron complete - 2024
  • Estimation of tree height using GEDI dataset - Clean Data - Perceptron 2 - 2022
  • Estimation of tree height using GEDI dataset - Neural Network 1
  • Estimation of tree height using GEDI dataset - Neural Network 1 - 2024
  • Neural Nets (pt.3), Interpretability and Convolutional Neural Networks
  • Using Multi-layer Perceptron and Convolutional Neural Networks for Satellite image classification - 2022.
  • Using Multi-layer Perceptron and Convolutional Neural Networks for Satellite image classification - 2023
  • Using CNNs for a image dataset
  • Prithvi 100M model
  • Proposed exercises
  • Autoencoder (AE), Variational Autoencoder (VAE)
  • Implementing an Autoencoder
  • Autoencoding MNIST
  • Section 2
  • Section 3 - Generative Models
  • Using LSTM for time-series predictions
  • Using GPT to implement a Convolutional Neural Networks for Satellite image classification.
  • Classification in Python using pyjeo and sklearn
  • Google Earth Engine use via Python, containers and other mythical beasts

Students Projects

  • 1. 2021 SWEDEN
  • 2. 2022 MATERA

OUTDOOR

  • Do not get lost in the wilderness
  • Shelter locations close to Gioia del Colle (BA)
  • Accomodation in Gioia del Colle (BA)
  • Discover Puglia by bike!

TALKS

  • Intelligent modelling in time and space: combine GeoComputation and Machine Learning for environmental application.

ADMIN

  • Install pktools on the gdrive and be able to use from any Colab
  • Video tips
  • Compiling OTB from source
Spatial Ecology's code documentation
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  • View page source
  • Install pktools on the gdrive and be able to use from any Colab
  • Video tips
  • Compiling OTB from source
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