A web mapping app to test, tweak and train the land cover classification from a deep neural network model built by @microsoft
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Updated
May 24, 2018 - JavaScript
A web mapping app to test, tweak and train the land cover classification from a deep neural network model built by @microsoft
Analysis of MRLC land cover data for Smith County
Country-level Land Cover - categories and transitions
deegree workspace for CLC10 INSPIRE
Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.
Land use data for Vietnam from GlobCover ESA's project
Satsense is a Python library for land use/cover classification using satellite imagery
LINDER (Land use INDexER) is an open-source machine-learning based land use/land cover (LULC) classifier using Sentinel 2 satellite imagery
Land Use and Land Cover (LULC) Classification using Convolutional Neural Networks and Transfer Learning
Tutorial do pacote OpenLand.
This repository contains the computer code of a semi-automated framework for land cover mapping using OBIA and local USPO
Reproducible remote sensing analysis using Google Earth Engine (GEE) to identify vegetation change in Columbia.
Hosting repository for the RLCMS methodology and code using GEE
Papers for Copernicus Land Monitoring Services evolution - based on H2020 ECoLaSS project
TiSeLaC ECML/PKDD 2017 discovery challenge solution
Code repository for the ENS Challenge Data 2021 by Preligens
R Client Library for Land Cover Classification System Web Service
Land cover mapping of the Orinoquía region in Colombia, in collaboration with Wildlife Conservation Society Colombia. An #AIforEarth project
CORINE landuse map to WAsP roughness
This project is to find the land cover on a satellite image using machine learning models. This project uses CNN and SVM as Hybrid to do so.
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