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awesome-forests Awesome

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.

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).

Happy dog in a forest by Jamie street on Unsplash

Photo of a dog in a forest, by [**Jamie Street**]( on [Unsplash](


Tree species classification



Tree detection


  • DeepForest WeEcology NEON (Weecology, NEON, UofFlorida, 2018)
    A tree detection dataset from β‰ˆ22 National Forest sites, USA with >15k labeled and >400k unlabeled trees with airborne RGB, Hyperspectral, and Lidar imagery.

  • Kaggle Aerial Cactus Identification (CONACYT, 2019)
    A cactus detection dataset from Mexiko with 17k cacti with airborne RGB imagery.

  • Swedish National Forest Data Lab: Forest Damages – Larch Casebearer 1.0. (Swedish Forest Agency 2021)
    A tree detection and classification dataset from 10 sites with RGB drone imagery. In total ~ 102k annotated bounding boxes labeled "Lark" or "other", of which ~ 44,5k are also labeled describing tree damage in four categories.

  • Norlab – PercepTree (Northern laboratory, 2022)
    This repository contains two datasets: 43k synthetic forest images and 100 real image dataset. Both include high-definition RGB images with depth information, bounding box, instance segmentation masks and keypoints annotation.


Tree damage / health classification

  • Forest Damages – Larch Casebearer (Swedish Forest Agency, 2021)
    A tree damage classification dataset from 5 areas in Sweden with 1.5k images with >100k labeled trees with airborne RGB

Biodiversity flora

  • Kaggle iNaturalist (iNaturalist, FGVC8, 2021)
    A flora and fauna species classification dataset from global sites with 2.7M labeled images of 10k species with smartphone imagery.

  • Kaggle GeoLifeCLEF 2021 (ImageCLEF, 2021)
    A flora and fauna location-based species recommendation dataset from France with 1.9M labeled images of 31k species with satellite imagery and cartographic variables.

Aboveground carbon quantification



Belowground carbon quantification

  • todo: add ground-truth datasets on belowground carbon inventories

Tree crown segmentation


  • todo. To get started, see Tree Detection for rectangular bounding boxes of tree crowns.


Forest type / land cover classification

  • BigEarthNet: large-scale Sentinel-2 benchmark (TU Berlin, 2019)
    A landcover multi-classification dataset from 10 European countries with β‰ˆ600k labeled images with CORINE land cover labels with Sentinel-2 L2A (10m res.) satellite imagery.

  • Chesapeake land cover (Chesapeake Conservancy, Microsoft, NAIP, USGS, 2013-2017)
    A land cover classification dataset from the Chesapeake Bay, USA, of a 6x7kmΒ² area with high- and low-resolution (NLCD) land cover labels with high- (NAIP, RGB-NIR) and low-resolution (Landsat 8, 13-band) satellite imagery.

  • Kaggle Planet: Understanding the Amazon from Space (SCCON, Planet, 2017)
    A land cover classification dataset from the Amazon with deforestation, mining, cloud labels with RGB-NIR (5m res.) satellite imagery.

  • WiDS Datathon 2019: detection of oil palm plantations (Global WiDS Team & West Big Data Innovation Hub, 2019)
    Binary palm oil plantation classification with 20k images with Planet RGB (3m res.) satellite imagery

  • UC Merced land use dataset(UC Merced, 2010)
    A small land cover classification dataset with 2100 images and 21 balanced classes with airborne (0.3m res.) imagery.

  • See Awesome satellite imagery datasets for more satellite imagery datasets.

  • See SustainBench for more UN SDG -related satellite imagery datasets.

Change detection (i.e., deforestation)

  • Dynamic EarthNet challenge (Planet, DLR, TUM, 2021)
    A time-series prediction and multi-class change detection dataset of Europe over 2-years with 75 image time-series with 7 land-cover labels and weekly Planet RGB (3m res.) imagery.

  • Semantic change detection dataset (SECOND) (Yang et al., 2020)
    A land cover change detection dataset in over cities and suburbs in China with β‰ˆ5k image-pairs with 6 land cover classes and airborne imagery.

  • ForestNet deforestation driver (Jeremy Irvin, Hao Sheng et al., 2020)
    A dataset that consists of 2,756 LANDSAT-8 satellite images of forest loss events with deforestation driver annotations. The driver annotations were grouped into Plantation, Smallholder Agriculture, Grassland/shrubland, and Other.

  • Global Forest Change (University of Maryland, 2013)
    Different layers of global forest loss, extracted from Landsat satellite imagery, todo: this is a data product, find ground-truth data

  • Awesome remote sensing change detection
    A list with more change detection datasets.


  • todo: add datasets for fire detection, fuel moisture quantification, wildfire spread prediction, etc.


  • iWildCam A species classification dataset from 414 global locations with >200k labeled images with wildlife camera trap imagery, Landsat-8 multispectral imagery, and GPS coordinates.

  • iNaturalist Multiple species classification datasets from global imagery of animals and plants with >2.7M from 10k species.

  • See for more processed conservation datasets

  • See Awesome-deep-ecology for more ecology datasets


  • todo: add bioacoustics datasets

Raw geospatial imagery




🌳 A curated list of ground-truth forest datasets for the machine learning and forestry community.








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