Software & Research Using DeepForest

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

Software

• ArcGIS - Tree Detection

• ArcGIS - updated

• Agisoft - Automatic detection of objects on orthomosaics

Research

2024

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

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 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

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

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://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://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, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2023.113888. 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).

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

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

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

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

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

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

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

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

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

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

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

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