Additional Material for JETRO GLODAL + SV CU LDD Training at TNI, May 2023
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Updated
Feb 29, 2024 - Jupyter Notebook
Additional Material for JETRO GLODAL + SV CU LDD Training at TNI, May 2023
An implementation of the neural network described in "Convolution Based Spectral Partitioning Architecture for Hyperspectral Image Classification"
Upsampling already available land cover raster layers using machine learning inside Google Earth Engine platform.
Land Use /Land Cover Classification using PyTorch with the RGB EuroSat Dataset
Landcover classification models validator using the SIGPAC data
This is a Repository used for getting insights about EuroSat dataset and also for training a model in order to classify those 10 classes
The aim of this project was to create a land cover classification of the area near Surat in India for 3 timesteps (2015, 2018, 2022) using a Random Forest classifier to access the process of urbanization
Future Urban-Wildfire Risk Mapping (FUWRM), pronounced as "form". This repository holds the programming script files and some of the binaries that represent the predictive risk maps for wildfires in urban regions of Southern Victoria (AUS) and Northern California (USA) in 2030 and 2040.
A TensorFlow implentation of fixed size kernel CNN
Landcover classification of satelite images
An Earth Engine based landcover mapping tool for the Polesia region, built for the British Trust for Ornithology by Artio Earth Observation.
Python module to download and preprocess Sentinel-2 data from Theia platform at tile-level
This is a script that reads in Landsat-8 data, Esri Sentinel-2 10m land cover time series data and train a random forest classification algorithm to estimate fractional built cover at 30m scale. The trained model can be used to produce fractional land cover for other regions.
PASTECA project - Land cover mapping with deep learning
GRASS GIS addon for Incora landcover classification. See also https://github.com/mundialis/incora
Landcover classification on sentinel-2 data with Prithvi, EfficientNet-Unet and OSM / CNES Landcover labels.
GEE code for pixel-based land cover classification with Random Forest (RF) algorithm, and for NDVI time series visualization.
The Supervised Land Cover Classification (SLaCC) tool is a Google Earth Engine script created by the Summer 2019 Southern Maine Health and Air Quality Team. It uses NASA Earth observations, the National Land Cover Database, land cover classification training data, and a shapefile of Cumberland County, Maine, USA. The goal of the project was to e…
Rough implementation of the Automated landcover classification using unsupervised classification methods.
Source code for the paper, "Water Body Extraction from Sentinel-2 Imagery with Deep Convolutional Networks and Pixelwise Category Transplantation".
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