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GDAL is an MIT-licensed open source translator library that provides a unified abstract data model for reading and writing geospatial raster and vector data across hundreds of file formats. It serves as a foundational geospatial data translation library, enabling access to diverse geospatial data formats through a single, consistent interface. The library exposes its core functionality through command-line utilities that allow users to translate, convert, and process geospatial data between formats. A coordinate transformation engine handles conversions between spatial reference systems, whil
Hub is a multimodal AI data lake and vector database designed for storing and querying embeddings, text, audio, and images. It functions as a dataset version control system and a machine learning data streaming engine to support large-scale model training. The system utilizes a serverless PostgreSQL vector store to index high-dimensional embeddings for semantic search. It provides a visual interface for inspecting multimodal datasets and viewing annotations such as bounding boxes and masks. The platform handles cloud-agnostic storage synchronization and implements lazy, compressed data strea
AWS Lambda Container Image with Python Rasterio for querying Cloud Optimised GeoTiffs.
The main features of addresscloud/aws-lambda-docker-rasterio are: Data Management, Data Management and Catalogues, Geospatial Data Tools, Raster and Vector Processing.
Projects with overlapping indexed features include: osgeo/gdal — GDAL is an MIT-licensed open source translator library that provides a unified abstract data model for reading and… azavea/stacframes — A Python library for working with STAC Catalogs via Pandas DataFrames. azavea/franklin — A STAC/OGC API Features Web Service. airbusgeo/godal — golang wrapper for github.com/OSGEO/gdal. activeloopai/hub — Hub is a multimodal AI data lake and vector database designed for storing and querying embeddings, text, audio, and… azavea/loam — A wrapper for running GDAL in the browser using gdal-js.