Band-Adaptive Spectral-Spatial Feature Learning Deep Neural Network for Hyperspectral Image Classification

codes for TGRS paper: Graph Relation Network: Modeling Relations between Scenes for Multi-Label Remote Sensing Image Classification and Retrieval

A TensorFlow implentation of fixed size kernel CNN

Source code for the paper, "Water Body Extraction from Sentinel-2 Imagery with Deep Convolutional Networks and Pixelwise Category Transplantation".

Pytorch code for the paper "The color out of space: learning self-supervised representations for Earth Observation imagery"

An implementation of the neural network described in "Convolution Based Spectral Partitioning Architecture for Hyperspectral Image Classification"

Landcover classification on sentinel-2 data with Prithvi, EfficientNet-Unet and OSM / CNES Landcover labels.

Open source canopy classification system

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.

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.

GEE code for pixel-based land cover classification with Random Forest (RF) algorithm, and for NDVI time series visualization.

Land Use /Land Cover Classification using PyTorch with the RGB EuroSat Dataset

Rough implementation of the Automated landcover classification using unsupervised classification methods.

generates rasters of Köppen-Geiger land-cover classification

Python module to download and preprocess Sentinel-2 data from Theia platform at tile-level

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.

Spatial digital humanities project for understanding space and place of Holocaust rescue.

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 surface cover product dynamic update system utilizing the CCDC algorithm and Random Forest tree 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.

Landcover classification of satelite images

This codebase is actively being developed as part of my master's thesis on Knowledge-based semantic enrichment for semantic image segmentation for the task of land cover classification.

GRASS GIS addon for Incora landcover classification. See also https://github.com/mundialis/incora

An Earth Engine based landcover mapping tool for the Polesia region, built for the British Trust for Ornithology by Artio Earth Observation.

A lightweight ensemble deep learning model achieving 98% accuracy on EuroSAT land-cover classification using 4-channel Sentinel-2 data.

a Webmap to present annual land cover dataset for the Baltic Sea Region with crop types and peat bogs at 30 m from 2000 to 2022

Additional Material for JETRO GLODAL + SV CU LDD Training at TNI, May 2023

This repository introduces a Python Jupyter Notebook code cells for geospatial analysis and land suitability assessment

Landcover classification models validator using the SIGPAC data