landcover-classification
There are 0 repository under landcover-classification topic.
manila95 / BASS-Net
Band-Adaptive Spectral-Spatial Feature Learning Deep Neural Network for Hyperspectral Image Classification
Language:Lua 68jiankang1991 / GRN-SNDL
codes for TGRS paper: Graph Relation Network: Modeling Relations between Scenes for Multi-Label Remote Sensing Image Classification and Retrieval
Language:Python 34ringochuchudull / CNN-Based-Hyperspectral-Image-Classification
A TensorFlow implentation of fixed size kernel CNN
Language:Python 25stevinc / TheColorOutOfSpace
Pytorch code for the paper "The color out of space: learning self-supervised representations for Earth Observation imagery"
Language:Python 18custom-computing-ic / SpecPatConv3D-Network
An implementation of the neural network described in "Convolution Based Spectral Partitioning Architecture for Hyperspectral Image Classification"
Language:Python 17Lecaethomas / LCLU_Frenchguiana
In order to map LCLU in french-Guyana, few scripts were developped or adapted to enable either to automaticaly map either to explore cloudless mosaic and even automaticaly detect floodings with Sentinel 1 SAR data.
- Language:Python 7
JoshuaBillson / Waterbody-Detection-Via-Deep-Learning
Source code for the paper, "Water Body Extraction from Sentinel-2 Imagery with Deep Convolutional Networks and Pixelwise Category Transplantation".
Language:Python 6ocsmit / alcc
Rough implementation of the Automated landcover classification using unsupervised classification methods.
Language:Python 4MaxWolf-01 / sentinel2-landcover-classification
Landcover classification on sentinel-2 data with Prithvi, EfficientNet-Unet and OSM / CNES Landcover labels.
Language:Python 3MrBourriz / Land-Cover-mapping-using-GEE
GEE code for pixel-based land cover classification with Random Forest (RF) algorithm, and for NDVI time series visualization.
Language:JavaScript 3NASA-DEVELOP / SLaCC
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 evaluate land cover and tick habitat suitability in southern Maine. The SLaCC script occurs in two parts. Part 1 of the script allows users to create a supervised land cover map over a region using a Classification And Regression Tree (CART) model. Part 2 of the script allows users to create a map that displays the "edges" of chosen land covers.
Language:JavaScript 3Andrewhjt / hjt_2024_repository
A surface cover product dynamic update system utilizing the CCDC algorithm and Random Forest tree algorithm.
chathumal93 / EuroSat-RGB-Classifiers
This repository contains three different models (ResNet-18, ResNet-50, and ViT-Base-Patch16-224) fine-tuned on the EuroSAT dataset, along with their performance comparisons.
Language:Jupyter Notebook 1A TensorFlow implentation of fixed size kernel CNN
Language:Python 1j-desloires / sentinel2-process
Python module to download and preprocess Sentinel-2 data from Theia platform at tile-level
Language:Python 1kkumar555 / Fractional-Built-up-Prediction-10m-from-Landsat-8-using-Random-Forest-Algorithm-
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.
Language:Python 1MadIceTea / FUWRM
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.
Language:JavaScript 1mar-koz22 / Satelite_image_classification_over_time
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
Language:Jupyter Notebook 1mundialis / r.incora
GRASS GIS addon for Incora landcover classification. See also https://github.com/mundialis/incora
Language:Python 1pachums / landcover_classification
Landcover classification of satelite images
Language:Jupyter Notebook 1tgrippa / PASTECA_LC
PASTECA project - Land cover mapping with deep learning
Language:Jupyter Notebook 1tpfd / Polesia-Landcover
An Earth Engine based landcover mapping tool for the Polesia region, built for the British Trust for Ornithology by Artio Earth Observation.
Language:Python 1jesusaldanamartin / satelite_images_sigpac
Landcover classification models validator using the SIGPAC data
Language:Python 0lookmeebbear / OpenRS_LDDTNI
Additional Material for JETRO GLODAL + SV CU LDD Training at TNI, May 2023
Language:Jupyter Notebook 0MuhammedM294 / EuroSat
Land Use /Land Cover Classification using PyTorch with the RGB EuroSat Dataset
Language:Jupyter Notebook 0pauldamsa / Eurosat-Project
This is a Repository used for getting insights about EuroSat dataset and also for training a model in order to classify those 10 classes
Language:Jupyter Notebook 0KonstantinosF / Upsampling-LC-Maps-Using-Machine-Learning
Upsampling already available land cover raster layers using machine learning inside Google Earth Engine platform.
An implementation of the neural network described in "Convolution Based Spectral Partitioning Architecture for Hyperspectral Image Classification"
Language:Python