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Awesome-forests is a curated list of ground-truth/validation/in situ forest datasets for the forest-interested machine learning community. The list targets data-based biodiversity, carbon, wildfire, ecosystem service, you name it! analysis. The list does NOT contain data products, such as, algorithm-generated global maps.

Getting started with data science in forests is TOUGH. The lack of organized datasets is one reason why. So, this list of datasets intends to get you started with building machine learning models for analysing your forests.

This is a wide open and inclusive community. We would very much appreciate if you add your favorite datasets via a pull request or (emailing (lutjens at mit [dot] edu).

<img src="figures/header_img_jamie_street_unsplash_dog_forest.jpg" alt="Happy dog in a forest by Jamie street on Unsplash" width="50%"> Photo of a dog in a forest, by [**Jamie Street**](https://unsplash.com/@jamie452) on [Unsplash](https://unsplash.com/?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText)

Content

Tree species classification

Processed

Raw

Tree detection

Processed

Raw

Tree damage and health classification

Navigation in forests

Biodiversity flora

Aboveground carbon quantification

Processed

Raw

Belowground carbon quantification

Tree crown segmentation

Processed

Raw

Forest type and land cover classification

Change detection and deforestation

Wildfire

Wildlife

Bioacoustics

Raw geospatial imagery

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Excluded data products

These datasets were excluded, because we could not find a source for the validation dataset. If you know the source please create an issue or pull request.

Attributions