Tools tagged with "Jupyter Notebooks"

Lidar is a specialized Python package designed for analyzing high-resolution topographic data, particularly Digital Elevation Models (DEMs) derived from LiDAR. It enables the delineation of the nested hierarchy of surface depressions, offering crucial insights for hydrological and geomorphological studies. The tool is openly available and supported by comprehensive documentation.

This GitHub repository features an extensive, community-driven compilation of geospatial tools and resources. It serves as a valuable hub for developers, researchers, and enthusiasts seeking various software, libraries, datasets, and educational materials in the geospatial domain. The list is continuously updated, reflecting the dynamic nature of the geospatial technology landscape.

Pyrocko is a flexible open-source seismology environment and framework, providing building blocks for researchers and students. It is utilized for various geophysical tasks, including seismological data processing and analysis, modeling of InSAR and GPS data, dynamic waveforms, and seismic source characterization.

CoastSat is an open-source Python software toolkit designed for global shoreline mapping. It enables users to obtain time-series of shoreline position at any coastline worldwide. The tool leverages 40 years (and growing) of publicly available satellite imagery from Landsat and Sentinel-2 missions.

Pysheds is an open-source Python library designed for efficient watershed delineation and digital elevation model (DEM) processing. It provides tools for conditioning elevation data, such as filling pits and depressions, and for extracting hydrological features like stream networks and watersheds. The library is built for speed and ease of use in geospatial analysis workflows.

This GitHub repository serves as a central hub for information on the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) mission. It provides essential resources and guidance for users to effectively locate, retrieve, and process ECOSTRESS data products for various scientific analyses and applications.

This dataset offers Radiometrically Terrain Corrected (RTC) Sentinel-1 C-band Synthetic Aperture Radar (SAR) imagery, processed from original Ground Range Detected (GRD) scenes. It provides analysis-ready data, suitable for environmental monitoring, available over the Contiguous United States (CONUS) since 2017. The data is managed by Indigo Ag, Inc. and targets daily updates.

GemPy is an open-source, Python-based software designed for generating 3D structural geological models. It utilizes an implicit modeling approach to create complex geological features like folds, faults, and unconformities from interface and orientation data. The software also supports probabilistic modeling to effectively address parameter and model uncertainties.

Geocomputation with Python is an open-source book designed to teach reproducible geographic data analysis using Python. It provides practical guidance for solving geospatial problems and building advanced applications, emphasizing transparency and accessibility.

Astropy is a foundational Python library providing core tools and functionality for astronomers and astrophysicists. It offers a comprehensive framework for scientific computing, data manipulation, and analysis within the field. This open-source project facilitates collaborative development and provides essential utilities for various astronomical tasks.

This repository provides an ensemble of AI models, known as SpeciesNet, developed by Google to classify wildlife in camera trap images. It combines an object detector (MegaDetector) to find objects of interest and an image classifier to identify species, significantly accelerating wildlife monitoring efforts. These models are utilized within the Wildlife Insights platform.

This Python package is designed for segmenting geospatial data, integrating the Segment Anything Model (SAM) to bring advanced AI-driven computer vision to the geospatial domain. It enables efficient identification and delineation of objects or regions within various raster datasets, including satellite imagery and aerial photos.

S1FloodFinder is a user-friendly open-source solution designed for flood mapping using Synthetic Aperture Radar (SAR) imagery from Sentinel-1. This package addresses the limitations of optical satellite imagery during floods, which often struggle with cloud cover, by leveraging SAR's ability to penetrate clouds. It provides specialized techniques and tools for accurate floodwater extent measurements, crucial for emergency response and minimizing disaster impact.

geopandas is an open-source Python library that simplifies working with geospatial data in Python. It extends the popular pandas data structures to include geometric types, enabling users to perform spatial operations easily. This library integrates seamlessly with other Python data science tools, making it a powerful choice for geographic data analysis and manipulation.

This resource provides a structured introduction to GIS programming, guiding users through software setup, Python fundamentals, and key geospatial libraries. It covers essential tools for data analysis, visualization, and cloud computing in a geospatial context, making complex topics accessible for learners.

traffic is a comprehensive Python library designed to handle, process, and analyze diverse air traffic data. It provides robust tools for data manipulation, visualization, and advanced analytical tasks related to aviation movements and patterns.

HyperCoast is a Python package designed for working with hyperspectral data, specifically focusing on coastal environments. It provides tools for both visualizing and analyzing this complex type of geospatial imagery. The package aims to facilitate research and applications related to coastal zone monitoring and environmental assessment.

InSAR.dev is the next evolution of PyGMTSAR, offering a federated Python ecosystem for satellite interferometry. It separates the workflow into SLC data preparation and core interferometric analysis, designed to scale efficiently across hundreds or thousands of bursts, multiple polarizations, and orbital paths. The ecosystem consists of three Python packages: insardev_toolkit, insardev_pygmtsar, and insardev, facilitating advanced InSAR processing.

leafmap is a Python package designed for interactive geospatial analysis and mapping. It simplifies complex GIS tasks within Jupyter environments, enabling users to create dynamic maps and perform advanced spatial analysis with minimal coding.

This dataset provides access to the USGS 3D Elevation Program (3DEP) LiDAR point cloud data covering the conterminous United States, Hawaii, and U.S. territories. It offers two data realizations: public access Entwine Point Tiles (EPT) and Requester Pays access to the original Raw LAZ format. The data is periodically updated and available under a US Government Public Domain license.

GeostatsPy is a robust Python package designed for spatial data analytics and geostatistics. It began as a reimplementation of GSLIB (Geostatistical Library) from Fortran to Python, making advanced geostatistical methods accessible. The package now includes numerous additional methods, supporting both 2D and 3D spatial analysis and simulation.

This open-source book serves as a comprehensive guide to spatial data management using PostgreSQL and PostGIS. It covers essential topics from installation and SQL basics to advanced spatial relationships and data projection. The resource aims to equip users with the knowledge to effectively work with geospatial data within a robust database environment.

The Center for Sustainability and the Global Environment (SAGE) at the University of Wisconsin-Madison conducts interdisciplinary research. It focuses on regional and global environmental problems, examining the complex interactions between environmental systems, natural resources, and human activities, while also providing data and models.

lonboard is a powerful Python library designed to enable rapid and interactive visualization of geospatial vector data. It integrates seamlessly into Jupyter notebooks, providing a robust solution for data scientists and GIS professionals to explore and analyze spatial information directly within their development environment.

msticpy is an open-source Python library from Microsoft, offering a suite of threat intelligence security tools. It assists security analysts and incident responders in data enrichment, analysis, and visualization for cybersecurity investigations. The project enhances threat hunting workflows by integrating with security data sources and supporting custom analytics.

Landlab is an open-source, Python-based modeling library designed for simulating Earth surface dynamics. It provides a flexible framework with a focus on gridded data structures and interchangeable components, enabling researchers to build and customize complex geoscientific models. The library is particularly useful for exploring various landscape evolution scenarios and hydrologic processes.

This dataset provides wildfire projections for California and its surrounding areas, developed to support California's Fifth Climate Assessment. It integrates historical weather observations and renewable energy capacity profiles, aiding in climate resilience and grid operations planning. The data is managed by Cal-Adapt: Analytics Engine.

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 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.

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.