<no title> — Spatial Ecology's code documentation 0.0.1 documentation

Spatial Ecology's code documentation

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 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
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

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
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

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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PYTHON

Python environments or how to survive to your journey in the geodata space
Introduction to Python
Python & GeoComputation
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

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