Tools tagged with "Python"

MintPy is an open-source Python package developed for processing Interferometric Synthetic Aperture Radar (InSAR) time-series data. It specializes in analyzing ground deformation by extracting precise displacement measurements from satellite imagery. The software provides tools for various geodetic applications, including monitoring earthquakes, volcanoes, and land subsidence.

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.

Terratorch is an open-source Python toolkit developed by terrastackai. It provides essential functionalities and tools for researchers and developers to fine-tune Geospatial Foundation Models (GFMs), adapting them to specific tasks and datasets. This platform facilitates advanced machine learning applications within the geospatial domain.

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.

Open3D is an open-source library designed for the rapid development of software involving 3D data. It provides a highly optimized backend for parallelization and exposes a curated set of data structures and algorithms in both C++ and Python. The library supports various 3D data processing tasks, including scene reconstruction, surface alignment, and advanced visualization with physically based rendering.

GeeFlow is a Python library from Google DeepMind designed for generating and processing large-scale geospatial datasets using Google Earth Engine. It focuses on supporting geospatial AI research by creating datasets in TFDS format, ready for model training, and enabling the creation of inference maps up to global scale.

rioxarray is an open-source Python library that extends xarray with geospatial capabilities, leveraging the power of rasterio. It simplifies working with georeferenced raster data, enabling users to efficiently read, write, and manipulate multi-dimensional geospatial arrays. This tool is ideal for data scientists and researchers handling large-scale imagery and raster datasets.

This plugin seamlessly integrates Google Earth Engine with QGIS, providing users with powerful geospatial analysis capabilities. It leverages the Google Earth Engine Python API to enable advanced remote sensing and data processing directly within the QGIS environment.

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.

geobr provides a user-friendly interface to access official spatial data sets of Brazil. It simplifies the process of downloading, reading, and organizing various geographic data for researchers and analysts. The package supports both R and Python environments, making it versatile for different programming preferences.

Who’s On First is a comprehensive gazetteer containing over 26 million records of places worldwide, ranging from continents to individual venues. Each entry features a stable identifier and a rich set of descriptive properties. The project offers its data in various formats, including GeoJSON and SQLite, and provides an API for programmatic access.

GeoIntel is an open-source project leveraging Google's Gemini API for advanced geo-location analysis. It focuses on using artificial intelligence to accurately determine the precise geographic location where a photograph was originally captured. This tool provides capabilities for uncovering hidden spatial data within image metadata.

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.

City Energy Analyst (CEA) is an open-source urban building energy modeling (UBEM) platform. It provides a comprehensive suite of computational tools designed to facilitate the planning and design of low-carbon and highly efficient urban environments. The platform supports various analyses for sustainable city development.

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.

MDAL is an open-source Mesh Data Abstraction Library designed to handle and manage mesh data for various geospatial applications. It serves as a crucial component for interacting with unstructured, 3D layered, and 1D meshes, particularly within GIS software ecosystems. The project is recognized as an OSGeo Community Project, ensuring its commitment to open standards and collaborative development.

"Minds Behind Maps" is a podcast featuring conversations with individuals who build and utilize maps in various fields. It explores how maps are created and applied across different industries, from data science to video production. The show delves into the techniques and stories behind geospatial data and visualization.

mappymatch is an open-source, pure-Python package designed for efficient map matching. It helps align GPS traces or other spatial trajectories to a digital road network, providing accurate location context for moving objects. Developed by NREL, this tool supports various geospatial analysis and transportation applications.

This GitHub repository serves as a comprehensive collection point for open-source resources dedicated to agriculture. It aims to centralize access to datasets, software tools, and deployment platforms that support open-source agricultural practices and research. The initiative provides a valuable hub for developers, researchers, and practitioners in the agri-tech community.

AnytimeFormer is a deep learning model designed to reconstruct land surface reflectance at any given time by effectively fusing irregular and asynchronous Synthetic Aperture Radar (SAR) and optical time series data. It addresses the challenge of combining diverse satellite data streams to provide continuous, high-quality environmental monitoring. The project offers a robust solution for generating comprehensive reflectance products from disparate remote sensing observations.

s2geometry is a powerful open-source library developed by Google for performing computational geometry and spatial indexing on the surface of a sphere. It provides a robust framework for tasks such as proximity searches, geometric operations, and efficient storage of spherical data.

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.

PyTorch Wildlife is an AI platform designed for the AI for Conservation community. It allows users to create, modify, and share powerful AI conservation models, directly loading various models like MegaDetector, DeepFaune, and HerdNet from its expanding model zoo for animal detection and classification. The framework aims to provide a unified and straightforward experience for building and deploying conservation AI.

geodiff is a powerful open-source library designed to compare two geospatial datasets and generate change sets, or 'diffs'. It supports various data formats including GeoPackage files, PostGIS, SQLite, and PostgreSQL databases. The library allows users to apply these generated diffs to update existing datasets, making it an essential tool for managing changes in spatial data.

async-tiff is an open-source library designed for asynchronously reading TIFF files, specifically catering to geospatial applications. It provides robust capabilities for handling large TIFF datasets efficiently in both Rust and Python environments, making it suitable for modern data processing workflows. This project facilitates the integration of TIFF data into high-performance computing and cloud-native architectures.

HELIOS++ is developed and maintained by the 3DGeo Research Group at Heidelberg University. This C++11-written package provides robust capabilities for simulating various LiDAR operations, making it a valuable resource for researchers and practitioners in geospatial science. It supports detailed simulations of laser scanning surveys, essential for planning and analysis in 3D geographic data acquisition.

point-cloud-utils is a Python library designed for efficient processing and manipulation of 3D point clouds and meshes. It offers a straightforward interface for common operations, making it accessible for developers and researchers working with 3D geospatial data. The library aims to simplify complex tasks related to 3D data handling.

This open-source software library provides highly efficient and precise capabilities for performing zonal statistics on raster data. It is designed to extract aggregated information from raster datasets based on defined polygonal zones, offering critical functionality for various geospatial analyses.

sits is an open-source R/Python package designed for satellite image time series analysis. It enables users to apply machine learning techniques for classifying image time series obtained from Earth observation data cubes, providing a complete workflow.

rio-color is a lightweight rasterio plugin that enables basic color-oriented image operations on geospatial rasters. It offers gamma adjustment, sigmoidal contrast, and saturation controls, specifically designed for efficiency and to retain all GeoTIFF information.

OpenDroneMap is a suite of open-source tools designed for processing aerial images from drones. It enables users to create high-quality geospatial products like orthomosaics, point clouds, 3D models, and digital elevation models, supporting various camera types and image orientations. The project fosters a sustainable ecosystem for aerial data collection, processing, analysis, and display, backed by a collaborative community.

The Chalmers Cloud Ice Climatology (CCIC) provides a deep-learning-based climate record of atmospheric ice-particle concentrations. This dataset offers high spatial and temporal resolution, making it suitable for evaluating weather and climate models or studying individual weather systems. It is updated quarterly and available under a CC BY 4.0 license.

Learn the latest GIS technology through free live training seminars, self-paced courses, or classes taught by Esri experts. Resources are available for professionals, educators, and students.

This blog, maintained by Robin Wilson, serves as a valuable resource for discovering interesting links and discussions within the geospatial and GIS community. It frequently highlights new open datasets, web mapping libraries, command-line tools, and developments in spatio-temporal asset catalogs (STAC). The content often delves into technical aspects of databases, programming, and open-source software.

Terracotta is an open-source XYZ tile server designed for serving raster data efficiently. It leverages Flask for the web framework and Rasterio for robust geospatial data processing, making it suitable for a wide range of web-mapping applications. This tool provides a straightforward way to turn your geospatial rasters into web-ready tiles.

This GitHub repository hosts a PyTorch-based implementation of MetNet and MetNet-2, neural network models developed by Google Research. The project focuses on providing open-source access to these powerful models for weather prediction. It enables researchers and developers to leverage deep learning techniques for meteorological applications.