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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

GEO DATA

  • Geomorpho90m: technical documentation

LINUX VIRTUAL MACHINE

  • Prepare Colab for Spatial Ecology courses
  • Prepare OSGeoLive for Spatial Ecology courses
  • Prepare OSGeoLive para el curso de ecología espacial

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
  • Introdction to pyjeo: installation and raster visualization
  • Performing raster and vector operations in Python using pyjeo

R

  • R Introduction

PYTHON

  • Introduction to Python
  • Python & GeoComputation

GRASS

  • GRASS Introduction
  • Using GRASS for stream-network extraction and basins delineation

CASE STUDY

  • SDM1 : Montane woodcreper - Gecomputation
  • SDM1 : Montane woodcreper - Model
  • 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 - Support Vector Machine for Regression (SVR)
  • Estimation of tree height using GEDI dataset - Random Forest prediction
  • Estimation of tree height using GEDI dataset - Perceptron 1
  • Estimation of tree height using GEDI dataset - Clean Data - Perceptron 2
  • Estimation of tree height using GEDI dataset - Neural Network 1
  • Neural Nets (pt.3), Interpretability and Convolutional Neural Networks
  • Using Multi-layer Perceptron and Convolutional Neural Networks for Satellite image classification.
  • The real data
  • LSTM for Regression Using the Window Method
  • LSTM for Regression with Time Steps
  • LSTM with Memory Between Batches
  • Stacked LSTMs with Memory Between Batches
  • Adding Early Stopping
  • Multivariate Time-series - Data

Students Projects

  • 1. 2021 SWEDEN
    • 1.1. Calculating landcover distribution & vegetation extraction
    • 1.2. Compiling OTB from source
    • 1.3. Observed and simulated internal variability climate feedbacks comparison.
    • 1.4. Statistical comparison global gridded climate datasets and their influence on LPJ-GUESS model outputs
    • 1.5. Emulating FLEXPART with a Multi-Layer Perceptron
    • 1.6. Processing Elmer/Ice output
    • 1.7. pan-Arctic classified slope and aspect maps (Geo computation only)
    • 1.8. Seasonal Analisis of discharges in the Mälaren catchement.
    • 1.9. Mapping of soil organic carbon stocks with Random Forest
    • 1.10. NDVI Computation
    • 1.11. Phase Change Analysis
    • 1.12. Relationship between continental-scale patterns of fire activity and modes of climate variability
  • 2. 2022 MATERA
    • 2.1. Damaged vs undamaged trees - Random Forest classification
    • 2.2. Stream Network Abstraction

OUTDOOR

  • Do not get lost in the wilderness

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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  • 1. 2021 SWEDEN
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1. 2021 SWEDEN

SWEDEN 2021

  • 1.1. Calculating landcover distribution & vegetation extraction
  • 1.2. Compiling OTB from source
  • 1.3. Observed and simulated internal variability climate feedbacks comparison.
  • 1.4. Statistical comparison global gridded climate datasets and their influence on LPJ-GUESS model outputs
  • 1.5. Emulating FLEXPART with a Multi-Layer Perceptron
  • 1.6. Processing Elmer/Ice output
  • 1.7. pan-Arctic classified slope and aspect maps (Geo computation only)
  • 1.8. Seasonal Analisis of discharges in the Mälaren catchement.
  • 1.9. Mapping of soil organic carbon stocks with Random Forest
  • 1.10. NDVI Computation
  • 1.11. Phase Change Analysis
  • 1.12. Relationship between continental-scale patterns of fire activity and modes of climate variability

2. 2022 MATERA

MATERA 2022

  • 2.1. Damaged vs undamaged trees - Random Forest classification
  • 2.2. Stream Network Abstraction
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