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.
GEE code for national-scale land cover classification using Sentinel imagery
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
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