DeepForest
Python Package for Tree Crown Detection in Airborne RGB imagery.
https://github.com/weecology/DeepForest
Category: Biosphere
Sub Category: Forest Remote Sensing
Keywords from Contributors
environmental-monitoring
Last synced: about 18 hours ago
JSON representation
Repository metadata
Python Package for Airborne RGB machine learning
- Host: GitHub
- URL: https://github.com/weecology/DeepForest
- Owner: weecology
- License: mit
- Created: 2018-03-07T20:22:58.000Z (about 7 years ago)
- Default Branch: main
- Last Pushed: 2025-04-18T16:37:23.000Z (9 days ago)
- Last Synced: 2025-04-18T21:21:42.990Z (8 days ago)
- Language: Python
- Homepage: https://deepforest.readthedocs.io/
- Size: 997 MB
- Stars: 598
- Watchers: 17
- Forks: 204
- Open Issues: 91
- Releases: 4
-
Metadata Files:
- Readme: README.md
- Changelog: HISTORY.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Citation: CITATION.cff
- Authors: AUTHORS.md
- Zenodo: .zenodo.json
README.md
DeepForest
Conda-forge build status
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What is DeepForest?
DeepForest is a python package for training and predicting ecological objects in airborne imagery. DeepForest currently comes with a tree crown object detection model and a bird detection model. Both are single class modules that can be extended to species classification based on new data. Users can extend these models by annotating and training custom models.
Documentation
DeepForest is documented on readthedocs
How does deepforest work?
DeepForest uses deep learning object detection networks to predict bounding boxes corresponding to individual trees in RGB imagery.
DeepForest is built on the object detection module from the torchvision package and designed to make training models for detection simpler.
For more about the motivation behind DeepForest, see some recent talks we have given on computer vision for ecology and practical applications to machine learning in environmental monitoring.
Where can I get help, learn from others, and report bugs?
Given the enormous array of forest types and image acquisition environments, it is unlikely that your image will be perfectly predicted by a prebuilt model. Below are some tips and some general guidelines to improve predictions.
Get suggestions on how to improve a model by using the discussion board. Please be aware that only feature requests or bug reports should be posted on the issues page.
Developer Guidelines
We welcome pull requests for any issue or extension of the models. Please follow the developers guide.
License
Free software: MIT license
Why DeepForest?
Remote sensing can transform the speed, scale, and cost of biodiversity and forestry surveys. Data acquisition currently outpaces the ability to identify individual organisms in high-resolution imagery. Individual crown delineation has been a long-standing challenge in remote sensing, and available algorithms produce mixed results. DeepForest is the first open-source implementation of a deep learning model for crown detection. Deep learning has made enormous strides in a range of computer vision tasks but requires significant amounts of training data. By including a trained model, we hope to simplify the process of retraining deep learning models for a range of forests, sensors, and spatial resolutions.
Citation
Owner metadata
- Name: Weecology
- Login: weecology
- Email:
- Kind: organization
- Description:
- Website: http://weecology.org
- Location:
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/1156696?v=4
- Repositories: 93
- Last ynced at: 2023-03-11T03:45:49.249Z
- Profile URL: https://github.com/weecology
GitHub Events
Total
- Create event: 43
- Commit comment event: 4
- Issues event: 158
- Watch event: 98
- Delete event: 45
- Issue comment event: 416
- Push event: 150
- Pull request review comment event: 61
- Pull request review event: 177
- Pull request event: 237
- Fork event: 36
Last Year
- Create event: 43
- Commit comment event: 4
- Issues event: 158
- Watch event: 98
- Delete event: 45
- Issue comment event: 416
- Push event: 150
- Pull request review comment event: 61
- Pull request review event: 177
- Pull request event: 237
- Fork event: 36
Committers metadata
Last synced: 6 days ago
Total Commits: 850
Total Committers: 30
Avg Commits per committer: 28.333
Development Distribution Score (DDS): 0.533
Commits in past year: 219
Committers in past year: 19
Avg Commits per committer in past year: 11.526
Development Distribution Score (DDS) in past year: 0.63
| Name | Commits | |
|---|---|---|
| Ben Weinstein | b****n@B****l | 397 |
| Benjamin Weinstein | b****z | 204 |
| henrykironde | h****e@g****m | 81 |
| Ethan White | e****n@w****g | 75 |
| Om Doiphode | o****1@g****m | 22 |
| Mu-Magdy | m****5@g****m | 15 |
| Dingyi Fang | d****9@c****u | 11 |
| Abhishek-Dimri | a****5@g****m | 7 |
| Samia Haque Tisha | s****5@g****m | 6 |
| Nakshatra | 1****2 | 5 |
| Cas Perl | e****n@g****m | 2 |
| Abhishek-kumar0503 | a****2@g****m | 2 |
| Satyam Sinha | s****4@g****m | 2 |
| Dwaipayan Munshi | 5****5 | 2 |
| Rhydham | 1****h | 2 |
| Karwot | 3****t | 2 |
| Keerthi Reddy | r****i@g****m | 2 |
| Pedro Cuenca | p****o@h****o | 1 |
| RohitP2005 | 1****5 | 1 |
| Rushiraj Gadhvi | g****j@g****m | 1 |
| cbudac-fwig | 8****g | 1 |
| davidnyberg | d****5@g****m | 1 |
| elliot | e****l@g****m | 1 |
| slurpinpuffs | s****x@g****m | 1 |
| Nikolai Poliarnyi | P****9@g****m | 1 |
| Bhavya Mehta | 1****4 | 1 |
| Dhiraj BM | m****3@g****m | 1 |
| Gourav Dey | g****6@h****n | 1 |
| Hesam Ossanloo (Personal) | h****m@o****m | 1 |
| JSpencerPittman | 1****n | 1 |
Committer domains:
- ossanloo.com: 1
- heritageit.edu.in: 1
- huggingface.co: 1
- columbia.edu: 1
- weecology.org: 1
Issue and Pull Request metadata
Last synced: 2 days ago
Total issues: 547
Total pull requests: 479
Average time to close issues: 2 months
Average time to close pull requests: 14 days
Total issue authors: 129
Total pull request authors: 46
Average comments per issue: 2.7
Average comments per pull request: 1.52
Merged pull request: 335
Bot issues: 0
Bot pull requests: 7
Past year issues: 151
Past year pull requests: 233
Past year average time to close issues: about 1 month
Past year average time to close pull requests: 9 days
Past year issue authors: 22
Past year pull request authors: 22
Past year average comments per issue: 2.14
Past year average comments per pull request: 1.64
Past year merged pull request: 168
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- bw4sz (251)
- ethanwhite (55)
- Smart133594 (14)
- Mu-Magdy (11)
- henrykironde (10)
- easz (7)
- EdwardHiscoke (6)
- aguirrejuan (6)
- naxatra2 (6)
- gislain22 (5)
- NickHarnau (5)
- landkwon94 (4)
- PravinM83 (4)
- TSAI-ChengEn (4)
- camappel (4)
Top Pull Request Authors
- bw4sz (143)
- henrykironde (95)
- ethanwhite (69)
- Mu-Magdy (25)
- Om-Doiphode (21)
- naxatra2 (11)
- Abhishek-Dimri (10)
- reddykkeerthi (8)
- Samia35-2973 (8)
- malayjoshi13 (7)
- dependabot[bot] (7)
- rhydham-nith (6)
- dassaniansh (6)
- ayeankit (6)
- satsin06 (5)
Top Issue Labels
- good first issue (64)
- API (27)
- Docs (24)
- enhancement (17)
- question (17)
- bug (15)
- Machine Learning (14)
- Performance (14)
- Feature Request (13)
- installation (12)
- Ideas for Machine Learning! (11)
- dependencies (10)
- stale (7)
- wontfix (6)
- help wanted (5)
- tensorflow (4)
- Google Summer of Code (2)
- demo (2)
- duplicate (1)
- image (1)
- To be documented (1)
Top Pull Request Labels
- dependencies (8)
- API (5)
- Awaiting author contribution (4)
- invalid (2)
- test&review (1)
- bug (1)
Package metadata
- Total packages: 2
-
Total downloads:
- pypi: 10,971 last-month
- Total dependent packages: 0 (may contain duplicates)
- Total dependent repositories: 12 (may contain duplicates)
- Total versions: 60
- Total maintainers: 3
pypi.org: deepforest
Tree crown prediction using deep learning retinanets
- Homepage:
- Documentation: http://deepforest.readthedocs.io/en/latest/
- Licenses: MIT
- Latest release: 1.5.2 (published 3 months ago)
- Last Synced: 2025-04-26T13:03:11.525Z (about 18 hours ago)
- Versions: 50
- Dependent Packages: 0
- Dependent Repositories: 12
- Downloads: 10,971 Last month
-
Rankings:
- Stargazers count: 3.291%
- Forks count: 4.048%
- Downloads: 4.115%
- Dependent repos count: 4.24%
- Average: 4.601%
- Dependent packages count: 7.31%
- Maintainers (3)
conda-forge.org: deepforest
DeepForest is a python package for training and predicting individual tree crowns from airborne RGB imagery. DeepForest comes with a prebuilt model trained on data from the National Ecological Observation Network. Users can extend this model by annotating and training custom models starting from the prebuilt model. DeepForest es un paquete de python para la predicción de coronas de árboles individuales basada en modelos entrenados con imágenes remotas RVA ( RGB, por sus siglas en inglés). DeepForest viene con un modelo entrenado con datos proveídos por la Red Nacional de Observatorios Ecológicos (NEON, por sus siglas en inglés). Los usuarios pueden ampliar este modelo pre-construido por anotación de etiquetas y entrenamiento con datos locales. La documentación de DeepForest está escrita en inglés, sin embargo, agradeceríamos contribuciones con fin de hacerla accesible en otros idiomas.
- Homepage: https://github.com/weecology/DeepForest
- Licenses: MIT
- Latest release: 1.2.1 (published about 3 years ago)
- Last Synced: 2025-04-26T13:03:16.424Z (about 18 hours ago)
- Versions: 10
- Dependent Packages: 0
- Dependent Repositories: 0
-
Rankings:
- Forks count: 15.019%
- Stargazers count: 20.15%
- Average: 30.092%
- Dependent repos count: 34.025%
- Dependent packages count: 51.175%
Dependencies
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Score: 19.240815666578378