Tools tagged with "Classification"

Learn to process and visualize point cloud data efficiently using QGIS's native tools and the powerful PDAL wrench.

A unified framework for end-to-end segmentation of forest LiDAR 3D point clouds.

This course provides essential Machine Learning skills tailored for Earth Observation research.

A pan-European label dataset providing harmonized crop type information for large-scale agricultural classification.

A community-sourced repository providing standardized, machine-learning-ready imagery and annotations for coral reef conservation.

An introductory textbook covering fundamental concepts and applications of remote sensing.

A powerful Python package for integrating artificial intelligence with geospatial data analysis and visualization.

Your gateway to accessing geospatial land and resource data, applications, maps, and publications.

Generate and process large-scale geospatial datasets with Google Earth Engine for AI research.

Provides fast and efficient tools for processing and visualizing LiDAR point clouds.

A collaborative deep learning framework for conservation, enabling the creation and sharing of AI models.

An easy-to-use Python library for processing and manipulating 3D point clouds and meshes.

Satellite image time series analysis for Earth Observation data cubes.

Providing diverse datasets for research in computer vision, image processing, and pattern recognition.

An online viewer for ESA/CCI Land Cover and conditions project data.

AI models for classifying wildlife in images from motion-triggered camera traps.

A Python package for segmenting geospatial data with the Segment Anything Model (SAM).

ML Flood Finder Package for Sentinel 1 (SAR) Imagery

OpenCV provides a real-time optimized Computer Vision library, tools, and hardware for ML and AI applications.

A learning plan to integrate AI, ML, and DL with GIS for solving geographic problems.

Flash Point Classifier quickly and efficiently classifies airborne lidar point clouds for actionable insights.

Global Land Cover data in raster format, classifying the entire globe into 20 categories based on FAO's LCCS.

A GitHub repository for processing Airborne Laser Scanning (ALS) data to analyze and correct road features.

An open, user-friendly, and customizable global crop mapping system supporting global food security.

An urban-scale point cloud dataset for semantic segmentation research with billions of annotated points.

Download global forest extent and change data derived from Landsat images for 2000-2014.

Provides high-resolution C-CAP land cover data for coastal areas across the nation.

AI-powered platform for automatic and manual 3D point cloud classification and analysis.

Innovative geospatial software built on open-source principles for a wide range of analytical tasks.

An open-source project for detecting mining activity using geospatial data.

A comprehensive guide to cloud-based remote sensing using Google Earth Engine, covering fundamentals and applications.

A curated list of ground-truth forest datasets for machine learning and forestry research.

A Python package for airborne RGB machine learning in ecology.

A comprehensive open-source library for advanced 2D/3D point cloud processing and analysis.

An R package for calculating landscape metrics from categorical map patterns.

A global 1-km consensus land-cover product for biodiversity and ecosystem modeling.