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geospatial-analysis

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Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.

  • Updated Jul 25, 2019
  • Jupyter Notebook

Actinia Core is an open source REST API for scalable, distributed, high performance processing of geographical data that uses mainly GRASS GIS for computational tasks (DOI: https://doi.org/10.5281/zenodo.5879231) | Tutorial: https://actinia-org.github.io/actinia-core/ | Docker: https://hub.docker.com/r/mundialis/actinia-core

  • Updated Dec 18, 2024
  • Python
aitlas-arena

An open-source benchmark framework for evaluating state-of-the-art deep learning approaches for image classification in Earth Observation (EO)

  • Updated Apr 19, 2024

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