# Software & Research Using DeepForest Software and research projects using DeepForest or data products generated by DeepForest. ## Software - [ArcGIS - Tree Detection](https://www.arcgis.com/home/item.html?id=4af356858b1044908d9204f8b79ced99) - [ArcGIS - updated](https://doc.arcgis.com/en/pretrained-models/latest/imagery/introduction-to-tree-detection.htm) - [Agisoft - Automatic detection of objects on orthomosaics](https://agisoft.freshdesk.com/support/solutions/articles/31000162552-automatic-detection-of-objects-on-orthomosaic) - [TreeEyed: A QGIS plugin for tree monitoring in silvopastoral systems using state of the art AI models](https://www.sciencedirect.com/science/article/pii/S235271102500038X), [GitHub](https://github.com/afruizh/TreeEyed) ## Research ### 2025 Xu, T., Wang, T., & Skidmore, A. K. (2025). Fusing aerial photographs and airborne LiDAR data to improve the accuracy of detecting individual trees in urban and peri-urban areas. Urban Forestry & Urban Greening, 105, 128696. [https://doi.org/10.1016/j.ufug.2025.128696](https://doi.org/10.1016/j.ufug.2025.128696) ### 2024 Kurbanov, R. K., Dalevich, A. N., Dorokhov, A. S., Zakharova, N. I., Rebouh, N. Y., Kucher, D. E., Litvinov, M. A., & Ali, A. M. (2024). Monitoring of *Heracleum sosnowskyi* Manden Using UAV Multisensors: Case Study in Moscow Region, Russia. *Agronomy, 14(10), 2451.* [https://doi.org/10.3390/agronomy14102451](https://doi.org/10.3390/agronomy14102451) 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).* [https://doi.org/10.1007/s00521-024-09582-5](https://doi.org/10.1007/s00521-024-09582-5) 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,* [https://doi.org/10.1016/j.jag.2024.103848](https://doi.org/10.1016/j.jag.2024.103848) ### 2023 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.* [https://doi.org/10.3390/rs15143558](https://doi.org/10.3390/rs15143558) **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.* [https://doi.org/10.3390/rs15030778](https://doi.org/10.3390/rs15030778) 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.* [https://doi.org/10.3390/rs15061535](https://doi.org/10.3390/rs15061535) 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.* [https://doi.org/10.3390/rs15102653](https://doi.org/10.3390/rs15102653) [https://www.mdpi.com/2072-4292/15/10/2653](https://www.mdpi.com/2072-4292/15/10/2653) 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. [https://doi.org/10.48550/arXiv.2310.16212](https://doi.org/10.48550/arXiv.2310.16212) [https://arxiv.org/abs/2310.16212](https://arxiv.org/abs/2310.16212) 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.* [https://doi.org/10.1016/j.rse.2023.113888](https://doi.org/10.1016/j.rse.2023.113888) Published in [RSE](https://www.sciencedirect.com/science/article/pii/S003442572300439X), and on [arXiv](https://arxiv.org/abs/2304.07213). 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). ### 2022 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. [https://doi.org/10.3389/fdata.2022.822573](https://doi.org/10.3389/fdata.2022.822573) 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. [https://doi.org/10.48550/arXiv.2207.07241](https://doi.org/10.48550/arXiv.2207.07241) 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). [https://doi.org/10.23919/SoftCOM55329.2022.9911397](https://doi.org/10.23919/SoftCOM55329.2022.9911397) 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. [https://doi.org/10.48550/arXiv.2211.13126](https://doi.org/10.48550/arXiv.2211.13126) 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. [https://doi.org/10.3390/rs14194963](https://doi.org/10.3390/rs14194963) 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. [https://doi.org/10.1609/aaai.v36i11.21471](https://doi.org/10.1609/aaai.v36i11.21471) 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. [https://doi.org/10.1002/eap.2694](https://doi.org/10.1002/eap.2694) 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. [https://doi.org/10.1101/2022.12.07.519493](https://doi.org/10.1101/2022.12.07.519493) 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). [https://doi.org/10.1109/CogMI56440.2022.00030](https://doi.org/10.1109/CogMI56440.2022.00030) ### 2021 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. [https://doi.org/10.1371/journal.pcbi.1009180](https://doi.org/10.1371/journal.pcbi.1009180) 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. [https://doi.org/10.7554/eLife.62922](https://doi.org/10.7554/eLife.62922) 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. ### 2020 ### 2019 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. [https://doi.org/10.3390/rs11111309](https://doi.org/10.3390/rs11111309)