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.

Classification of the Hyperspectral Image Indian Pines with Convolutional Neural Network

LANDSAT Time Series Analysis for Multi-temporal Land Cover Classification using Machine Learning techniques in Python and GUI development for automation of the process.

Land Cover Change Detection using Satellite Image Segmentation.

Land cover mapping of the Orinoquía region in Colombia, in collaboration with Wildlife Conservation Society Colombia. An #AIforEarth project

Image segmentation of cultivated land

[ISPRS 2024] LoveNAS: Towards Multi-Scene Land-Cover Mapping via Hierarchical Searching Adaptive Network

Satsense is a Python library for land use/cover classification using satellite imagery

Reproducible remote sensing analysis using Google Earth Engine (GEE) to identify vegetation change in Columbia.

QGIS Plugin 'Curve Number Generator' has multiple algorithms to generate the curve number layer for any area of interest using different datasets.

This repository contains the computer code of a semi-automated framework for land cover mapping using OBIA and local USPO

A web mapping app to test, tweak and train the land cover classification from a deep neural network model built by @microsoft

LINDER (Land use INDexER) is an open-source machine-learning based land use/land cover (LULC) classifier using Sentinel 2 satellite imagery

R Client Library for Web Land Trajectory Service

Code repository for the ENS Challenge Data 2021 by Preligens

32 satellite imagery maps of Sri Lanka from 2017-2024, ranging from normal RGB, to high contrast versions, to gridded land cover / land use mapped by our in-house machine learning models.

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

A crash course in analysing spatial patterns of the landscape using R

TiSeLaC ECML/PKDD 2017 discovery challenge solution

Land Cover Prediction from Satellite Imagery Using Machine Learning Techniques