Software & Research Using DeepForest#

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




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.


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).


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.



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.