Software & Research Using DeepForest#

Software and research projects using DeepForest or data products generated by DeepForest.




Afsar, M.M., Bakhshi, A.D., Hussain, E. et al. A deep learning-based framework for object recognition in ecological environments with dense focal loss and occlusion. Neural Comput & Applic (2024).

Ventura, Pawlak, Honsberger, Gonsalves, Rice, Love, Han, Nguyen, Sugano, Doremus, G. Fricker, Yost, Ritter. Individual tree detection in large-scale urban environments using high-resolution multispectral imagery, International Journal of Applied Earth Observation and Geoinformation, Volume 130, 2024,


Jemaa, H., W. Bouachir, B. Leblon, A. LaRocque, A. Haddadi, and N. Bouguila. 2023. UAV-Based Computer Vision System for Orchard Apple Tree Detection and Health Assessment. Remote Sensing 15:3558. Uses DeepForest as baseline detection model

Gan, Y., Q. Wang, and A. Iio. 2023. Tree Crown Detection and Delineation in a Temperate Deciduous Forest from UAV RGB Imagery Using Deep Learning Approaches: Effects of Spatial Resolution and Species Characteristics. Remote Sensing 15:778.

Wang, C., D. Jia, S. Lei, I. Numata, and L. Tian. 2023. Accuracy Assessment and Impact Factor Analysis of GEDI Leaf Area Index Product in Temperate Forest. Remote Sensing 15:1535.

Kapil, R.; Castilla, G.; Marvasti-Zadeh, S.M.; Goodsman, D.; Erbilgin, N.; Ray, N. Orthomosaicking Thermal Drone Images of Forests via Simultaneously Acquired RGB Images. Remote Sens. 2023, 15, 2653.

Kapil, R. Marvasti-Zadeh, S, Erbilgin, N, Ray, N. 2023. ShadowSense: Unsupervised Domain Adaptation and Feature Fusion for Shadow-Agnostic Tree Crown Detection from RGB-Thermal Drone Imagery.

Jamie Tolan, Hung-I Yang, Benjamin Nosarzewski, Guillaume Couairon, Huy V. Vo, John Brandt, Justine Spore, Sayantan Majumdar, Daniel Haziza, Janaki Vamaraju, Theo Moutakanni, Piotr Bojanowski, Tracy Johns, Brian White, Tobias Tiecke, Camille Couprie, Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar, Remote Sensing of Environment, Volume 300, 2024, 113888, ISSN 0034-4257, In RSE, and on arxiv.

Kwon, Ryoungseob, et al. “Merging multiple sensing platforms and deep learning empowers individual tree mapping and species detection at the city scale.” ISPRS Journal of Photogrammetry and Remote Sensing 206 (2023): 201-221.

Velasquez-Camacho, Luisa, Maddi Etxegarai, and Sergio de-Miguel. “Implementing Deep Learning algorithms for urban tree detection and geolocation with high-resolution aerial, satellite, and ground-level images.” Computers, Environment and Urban Systems 105 (2023): 102025.

Marvasti-Zadeh, Seyed Mojtaba, et al. “Crown-CAM: Interpretable Visual Explanations for Tree Crown Detection in Aerial Images.” IEEE Geoscience and Remote Sensing Letters (2023).


Babu Saheer, L., A. Bhasy, M. Maktabdar, and J. Zarrin. 2022. Data-Driven Framework for Understanding and Predicting Air Quality in Urban Areas. Frontiers in Big Data 5.

Kapil, R., S. M. Marvasti-Zadeh, D. Goodsman, N. Ray, and N. Erbilgin. 2022. Classification of Bark Beetle-Induced Forest Tree Mortality using Deep Learning. arXiv.

Marin, I., S. Gotovac, and V. Papić. 2022. Individual Olive Tree Detection in RGB Images. Pages 1–6 2022 International Conference on Software, Telecommunications and Computer Networks (SoftCOM).

Marvasti-Zadeh, S. M., D. Goodsman, N. Ray, and N. Erbilgin. 2022, November 23. Crown-CAM: Reliable Visual Explanations for Tree Crown Detection in Aerial Images. arXiv.

Sivanandam, P., and A. Lucieer. 2022. Tree Detection and Species Classification in a Mixed Species Forest Using Unoccupied Aircraft System (UAS) RGB and Multispectral Imagery. Remote Sensing 14:4963.

Reiersen, G., D. Dao, B. Lütjens, K. Klemmer, K. Amara, A. Steinegger, C. Zhang, and X. Zhu. 2022. ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery. Proceedings of the AAAI Conference on Artificial Intelligence 36:12119–12125.

Weinstein, B. G., L. Garner, V. R. Saccomanno, A. Steinkraus, A. Ortega, K. Brush, G. Yenni, A. E. McKellar, R. Converse, C. D. Lippitt, A. Wegmann, N. D. Holmes, A. J. Edney, T. Hart, M. J. Jessopp, R. H. Clarke, D. Marchowski, H. Senyondo, R. Dotson, E. P. White, P. Frederick, and S. K. M. Ernest. 2022. A general deep learning model for bird detection in high-resolution airborne imagery. Ecological Applications 32:e2694.

Weinstein, B. G., S. Marconi, S. J. Graves, A. Zare, A. Singh, S. A. Bohlman, L. Magee, D. J. Johnson, P. A. Townsend, and E. P. White. 2022, December 11. Capturing long-tailed individual tree diversity using an airborne multi-temporal hierarchical model. bioRxiv.

Zhang, Y., Y. Wang, Z. Tang, Z. Zhai, Y. Shang, and R. Viegut. 2022. Deep Learning Methods for Tree Detection and Classification. Pages 148–155 2022 IEEE 4th International Conference on Cognitive Machine Intelligence (CogMI).


Saheer, Lakshmi Babu, and Mohamed Shahawy. “Self-supervised approach for urban tree recognition on aerial images.” IFIP International Conference on Artificial Intelligence Applications and Innovations. Cham: Springer International Publishing, 2021.

Weinstein, B. G., S. J. Graves, S. Marconi, A. Singh, A. Zare, D. Stewart, S. A. Bohlman, and E. P. White. 2021. A benchmark dataset for canopy crown detection and delineation in co-registered airborne RGB, LiDAR and hyperspectral imagery from the National Ecological Observation Network. PLOS Computational Biology 17:e1009180.

Weinstein, B. G., S. Marconi, S. A. Bohlman, A. Zare, A. Singh, S. J. Graves, and E. P. White. 2021. A remote sensing derived data set of 100 million individual tree crowns for the National Ecological Observatory Network. eLife 10:e62922.

Aubry-Kientz, Mélaine, et al. “Multisensor data fusion for improved segmentation of individual tree crowns in dense tropical forests.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021): 3927-3936.



Weinstein, B. G., S. Marconi, S. Bohlman, A. Zare, and E. White. 2019. Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks. Remote Sensing 11:1309.