Tools tagged with "Github"

lidR is an R package designed for processing airborne LiDAR data. It provides powerful tools for point cloud manipulation, analysis, and visualization, with a strong focus on applications in forestry. The package enables users to perform tasks such as terrain modeling, individual tree segmentation, and forest structure analysis.

GeoArrow-rs is an open-source project implementing the GeoArrow specification for high-performance geospatial data processing. It provides bindings for Rust, Python, and JavaScript (via WebAssembly), enabling efficient, vectorized geometry operations across various environments. This project aims to standardize and accelerate geospatial data workflows.

This GitHub organization hosts the Algorithm Development Team (ADT) for the OPERA project. The team focuses on developing and sharing open-source algorithms for processing remote sensing data, particularly from Synthetic Aperture Radar (SAR) missions. Their work supports the creation of various Earth observation products for end-users.

This open-source tool is designed for the automatic extraction and analysis of river and delta channel networks. It processes binary masks, converting them into quantifiable graphical representations for hydrological and geomorphological studies.

keeptrack.space provides an accessible platform for understanding complex astrodynamics. It focuses on 3D visualization of satellite movements and the ground-based sensors that monitor them, making space data more intuitive. This open-source project aims to demystify satellite tracking for a broader audience beyond traditional engineering fields.

This GitHub repository provides a comprehensive collection of resources focused on the intersection of mineral exploration and machine learning. It features useful code examples, libraries, and tools to assist practitioners and researchers in applying advanced analytical methods to geological data. The repository serves as a valuable hub for discovering practical applications and learning materials in this specialized field.

geojs is an open-source JavaScript library designed for high-performance visualization of scientific and geospatial data. It enables interactive data exploration within web applications, handling complex location-aware datasets efficiently. This tool is ideal for developers building sophisticated web-based mapping and data analysis platforms.

This repository offers a global dataset of building density and height, generated quarterly by applying machine learning models to PlanetScope basemaps. It provides a global public layer and detailed data for five high-growth locations, distributed as Cloud-Optimized GeoTIFFs (COGs). The resource supports both data exploration and model training for users interested in urban development analysis.

Pytroll is an easy-to-use, modular, free, and open-source Python framework designed for the processing of Earth Observation (EO) satellite data. Its packages are suitable for both research and development (R&D) environments and 24/7 operational production. The framework focuses on atmospheric applications and imaging sensors, supporting a wide range of Earth sciences.

The TACO dataset addresses the critical need for annotated images to train deep learning models for trash detection. It aims to advance AI applications in environmental monitoring, such as drone-based trash surveying and robotic litter collection, by providing a comprehensive and continually updated collection of segmented trash images.

climlab is a powerful Python package offering a flexible engine for process-oriented climate modeling. It enables users to simulate climate processes as stand-alone models or integrate them into more complex systems, based on a general concept of models as interacting processes. The package facilitates the definition and interactive execution of new model classes by combining various sub-processes.

SegmentAnyTree is an open-source GitHub repository dedicated to tree segmentation. It provides code and resources for identifying and delineating individual trees, likely utilizing advanced machine learning techniques on various types of geospatial imagery. This project supports detailed forest analysis and environmental monitoring efforts.

This GitHub repository serves as an "awesome list" for photogrammetry, collecting a wide range of projects, libraries, and tools related to the field. It provides a community-driven compilation for developers, researchers, and enthusiasts looking for relevant resources. The list aims to categorize and highlight significant contributions in photogrammetry.