GRASS GIS
GRASS GIS (Geographic Resources Analysis Support System) is a free and open-source Geographic Information System (GIS) software suite utilized for geospatial data management and analysis, image processing, graphics and map production, spatial modeling, and visualization. It supports raster, vector, and geospatial processing, enabling advanced modeling, data management, imagery processing, and time series analysis with a Python API, optimized for large-scale analysis. GRASS GIS is compatible with multiple operating systems, including OS X, Windows, and Linux, and can be accessed through a graphical user interface or integrated with other software such as QGIS. The software includes over 350 modules for rendering maps and images, manipulating raster and vector data, processing multispectral image data, and creating, managing, and storing spatial data. GRASS GIS is widely used in academic and commercial settings, as well as by governmental agencies.
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GeoExpress
The massive, high-definition imagery used throughout the geospatial industry provides invaluable insights, but huge file sizes can make sharing, viewing, and manipulating this data extremely challenging. GeoExpress enables geospatial professionals to compress imagery to our proprietary, industry-standard MrSID format. This format supports lossless and visually lossless compression, enabling users to shrink file sizes without sacrificing image quality. GeoExpress also provides editing capabilities for geospatial imagery compression, so that you can provide improved visual data for analysis. It includes standard imagery editing functionality such as cropping and color balancing, while also enabling you to reproject, mosaic, and more. Maximize space, optimize usage, and facilitate easier distribution by compressing image files and geospatial data to as much 5% of their original size or cutting file size in half — all while maintaining visual image fidelity.
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Google Earth Engine
Google Earth Engine is a cloud-based platform for scientific analysis and visualization of geospatial datasets, providing access to a vast public data archive that includes over 90 petabytes of analysis-ready satellite imagery and more than 1,000 curated geospatial datasets. This extensive catalog encompasses over 50 years of historical imagery, updated daily, with resolutions as fine as one meter per pixel, featuring datasets such as Landsat, MODIS, Sentinel, and the National Agriculture Imagery Program (NAIP). Earth Engine enables users to analyze Earth observation data and apply machine learning techniques through its web-based JavaScript Code Editor and Python API, facilitating the development of complex geospatial workflows. The platform's integration with Google Cloud allows for large-scale parallel processing, empowering users to conduct comprehensive analyses and visualize Earth data efficiently. Additionally, Earth Engine offers interoperability with BigQuery.
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GeoPandas
GeoPandas is an open-source project to make working with geospatial data in python easier. GeoPandas extends the datatypes used by pandas to allow spatial operations on geometric types. Geometric operations are performed by shapely. Geopandas further depends on fiona for file access and matplotlib for plotting. The goal of GeoPandas is to make working with geospatial data in python easier. It combines the capabilities of pandas and shapely, providing geospatial operations in pandas and a high-level interface to multiple geometries to shapely. GeoPandas enables you to easily do operations in python that would otherwise require a spatial database such as PostGIS. GeoPandas is a community-led project written, used and supported by a wide range of people from all around of world of a large variety of backgrounds. GeoPandas will always be 100% open source software, free for all to use and released under the liberal terms of the BSD-3-Clause license.
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