# Azure/pixel\_level\_land\_classification

<https://repo.masters-masters.com/en/repos/Azure/pixel_level_land_classification>

[Home](https://repo.masters-masters.com/en) › [Azure](https://repo.masters-masters.com/en/owners/Azure) › [pixel\_level\_land\_classification](https://repo.masters-masters.com/en/repos/Azure/pixel_level_land_classification)

Azure/pixel\_level\_land\_classification has 281 stars on GitHub. Its RepoMasters card: Alpha tier. Tutorial demonstrating how to create a semantic segmentation…

| Field | Value |
| --- | --- |
| Owner | [Azure](https://repo.masters-masters.com/en/owners/Azure) |
| Stars | 281 |
| Forks | 96 |
| Language | [Jupyter Notebook](https://repo.masters-masters.com/en/languages/jupyter-notebook) |
| License | MIT |
| Created | 2018 |
| Topics | neural-networks, image-classification, image-segmentation, geospatial-data, geospatial-analysis, land-cover, land-use, cntk, cntk-model, azure-batchai |
| Tier | [Alpha (250 to 699 ★)](https://repo.masters-masters.com/en/ranks/b_movie) |
| View on GitHub | [Azure/pixel\_level\_land\_classification](https://github.com/Azure/pixel_level_land_classification) |

## Repository

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.

Archived: this repository is read-only

The tier for repos with 250 to 699 ★. Published odds per card pulled: 20%. Rarity: Uncommon.

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Data: GitHub · Illustration drawn by RepoMasters from the repo’s ID and its language color.