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YUTO Tree5000: A Large-Scale Airborne LiDAR Dataset for Single Tree Detection

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Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges (ICPR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13645))

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Abstract

Despite the computer vision community showing great interest in deep learning-based object detection, the adaptation to tree detection has been rare. There is a notable absence of proper datasets for automatic tree detection with deep convolutional neural networks to create and update tree inventories using LiDAR information. There are some publicly accessible benchmark datasets, but the domains are mostly forest. This paper introduces YUTO (York University Teledyne Optech) Tree5000, a novel LiDAR benchmark dataset for nearly 5000 individual trees in an urban context. It represents 142 species at York University in Toronto, Ontario, Canada. Semi-automatic techniques were applied to construct 3D bounding boxes for each tree using publicly available data such as Google Earth (GE) images and Google Street View (GSV), field-collected data, and airborne LiDAR. This dataset includes (1) a manually verified LiDAR dataset with four segmentation classes, (2) field-collected tree inventory data with specifications covering nearly 4.3 km2, (3) manually adjusted GPS treetop locations, (4) semi-automatically generated 2D tree boundary information, and (5) 3D tree bounding boxes information. Unlike other research that utilized 2D images of their dataset to evaluate with a 2D detection algorithm, we use 3D LiDAR point cloud to detect trees. We evaluated the performance of the following eight algorithms on the benchmark dataset and analyzed the results: second, PointPillars, PointRCNN, VoxelRCNN, PVRCNN, PartA2, PyramidRCNN, and PVRCNN++. Mainly, we discuss the utilization of airborne LiDAR and existing 3D detection networks when developing new algorithms.

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References

  1. Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? In: The KITTI vision benchmark suite. 2012 IEEE CVPR, pp. 3354–3361 (2012)

    Google Scholar 

  2. Patil, A., Malla, S., Gang, H., Chen, Y.-T.: The H3D dataset for full-surround 3D multi-object detection and tracking in crowded urban scenes. arXiv:1903.01568. [Cs] (2019)

  3. Sun, P., et al.: Scalability in perception for autonomous driving: Waymo open dataset. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2443–2451 (2020)

    Google Scholar 

  4. Caesar, H., et al.: nuScenes: a multimodal dataset for autonomous driving. arXiv:1903.11027. [Cs, Stat] (2020)

  5. Munoz, D., Bagnell, J.A., Vandapel, N., Hebert, M.: Contextual classification with functional max-margin Markov networks. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 975–982 (2009)

    Google Scholar 

  6. Rottensteiner, F., et al.: The ISPRS benchmark on urban object classification and 3D building reconstruction. ISPRS Ann. Photogramm., Remote Sens. Spat. Inf. Sci. I–3, 293–298 (2012)

    Google Scholar 

  7. Hackel, T., Savinov, N., Ladicky, L., Wegner, J.D., Schindler, K., Pollefeys, M.: Semantic3D.Net: a new large-scale point cloud classification benchmark. arXiv:1704.03847. [Cs] (2017)

  8. Roynard, X., Deschaud, J.-E., Goulette, F.: Paris-Lille-3D: a large and high-quality ground-truth urban point cloud dataset for automatic segmentation and classification. Int. J. Robot. Res. 37(6), 545–557 (2018)

    Article  Google Scholar 

  9. Behley, J., et al.: SemanticKITTI: a dataset for semantic scene understanding of LiDAR sequences. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9296–9306 (2019)

    Google Scholar 

  10. Tan, W., et al.: Toronto-3D: a large-scale mobile LiDAR dataset for semantic segmentation of urban roadways. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 797–806 (2020)

    Google Scholar 

  11. Kaartinen, H., et al.: An international comparison of individual tree detection and extraction using airborne laser scanning. Remote Sens. 4(4), 950–974 (2012)

    Article  Google Scholar 

  12. Kaartinen, H., Hyyppä, J.: european spatial data research tree extraction official publication, no. 53 (2008). http://www.eurosdr.net/sites/default/files/uploaded_files/eurosdr_publication_ndeg_53.pdf

  13. Eysn, L., et al.: A benchmark of Lidar-based single tree detection methods using heterogeneous forest data from the alpine space. Forests 6(5), 1721–1747 (2015)

    Article  Google Scholar 

  14. Vauhkonen, J., et al.: Comparative testing of single-tree detection algorithms under different types of forest. Forestry 85(1), 27–40 (2011)

    Article  Google Scholar 

  15. Liang, X., et al.: International benchmarking of terrestrial laser scanning approaches for forest inventories. ISPRS J. Photogramm. Remote. Sens. 144, 137–179 (2018)

    Article  Google Scholar 

  16. Wang, Y., et al.: International benchmarking of the individual tree detection methods for modeling 3-D canopy structure for Silviculture and forest ecology using airborne laser scanning. IEEE Trans. Geosci. Remote Sens. 54(9), 5011–5027 (2016)

    Article  Google Scholar 

  17. Weinstein, B.G., Marconi, S., Aubry-Kientz, M., Vincent, G., Senyondo, H., White, E.P.: DeepForest: a Python package for RGB deep learning tree crown delineation. Methods Ecol. Evol. 11(12), 1743–1751 (2020)

    Article  Google Scholar 

  18. Schmohl, S., Narváez Vallejo, A., Soergel, U.: Individual tree detection in Urban ALS point clouds with 3D convolutional networks. Remote Sens. 14(6), 1317 (2022). https://doi.org/10.3390/rs14061317

    Article  Google Scholar 

  19. Open Data Dataset. Open.toronto.ca. https://open.toronto.ca/dataset/street-tree-data/. Accessed11 Mar 2022

  20. Forest Resources Inventory. ontario.ca. https://www.ontario.ca/page/forest-resources-inventory. Accessed11 Mar 2022

  21. Street trees in Guelph. City of Guelph. https://guelph.ca/living/environment/trees/street-tree-ownership. Accessed11 Mar 2022

  22. City-owned Tree Inventory. Data.mississauga.ca. https://data.mississauga.ca/datasets/city-owned-tree-inventory/explore?location=43.609902%2C-79.674385%2C11.90. Accessed11 Mar 2022

  23. Street Trees. Data-markham.opendata.arcgis.com. https://data-mark-ham.opendata.arcgis.com/datasets/293d80c24bf54a4f8ab66bddaeaab184_0/about. Accessed11 Mar 2022

  24. Wegner, J.D., Branson, S., Hall, D., Schindler, K., Perona, P.: Cataloging public objects using aerial and street-level images—Urban trees. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6014–6023 (2016)

    Google Scholar 

  25. Branson, S., Wegner, J.D., Hall, D., Lang, N., Schindler, K., Perona, P.: From google maps to a fine-grained catalog of street trees. ISPRS J. Photogramm. Remote. Sens. 135, 13–30 (2018)

    Article  Google Scholar 

  26. Matasci, G., Coops, N.C., Williams, D.A.R., Page, N.: Mapping tree canopies in urban environments using airborne laser scanning (ALS): a vancouver case study. Forest Ecosyst. 5(1), 31 (2018)

    Article  Google Scholar 

  27. Xie, Q., Li, D., Yu, Z., Zhou, J., Wang, J.: Detecting trees in street images via deep learning with attention module. IEEE Trans. Instrum. Meas. 69(8), 5395–5406 (2020)

    Article  Google Scholar 

  28. Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Occlusion-aware R-CNN: detecting pedestrians in a crowd. arXiv:1807.08407. [Cs] (2018)

  29. Lumnitz, S., Devisscher, T., Mayaud, J.R., Radic, V., Coops, N.C., Griess, V.C.: Mapping trees along urban street networks with deep learning and street-level imagery. ISPRS J. Photogramm. Remote. Sens. 175, 144–157 (2021)

    Article  Google Scholar 

  30. Yan, Y., Mao, Y., Li, B.: SECOND: sparsely embedded convolutional detection. Sensors 18(10), 3337 (2018)

    Article  Google Scholar 

  31. Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: PointPillars: fast encoders for object detection from point clouds. arXiv:1812.05784. [Cs, Stat] (2019)

  32. Shi, S., Wang, X., Li, H.: PointRCNN: 3D object proposal generation and detection from point cloud. arXiv:1812.04244. [Cs] (2019)

  33. Shi, S., Wang, Z., Shi, J., Wang, X., Li, H.: From points to parts: 3D object detection from point cloud with part-aware and part-aggregation network. arXiv:1907.03670. [Cs] (2020)

  34. Shi, S., et al.: PV-RCNN: point-voxel feature set abstraction for 3D object detection. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10526–10535 (2020)

    Google Scholar 

  35. Mao, J., Niu, M., Bai, H., Liang, X., Xu, H., Xu, C.: Pyramid R-CNN: towards better performance and adaptability for 3D object detection. arXiv:2109.02499. [Cs] (2021)

  36. Deng, J., Shi, S., Li, P., Zhou, W., Zhang, Y., Li, H.: Voxel R-CNN: towards high performance voxel-based 3D object detection. arXiv:2012.15712. [Cs] (2021)

  37. Shi, S., et al.: PV-RCNN++: point-voxel feature set abstraction with local vector representation for 3D object detection. arXiv:2102.00463. [Cs] (2022)

  38. LAStools. rapidlasso GmbH. https://rapidlasso.com/LAStools/. Accessed11 Mar 2022

  39. TerraScan - Terrasolid. https://terrasolid.com/products/terrascan/. Accessed11 Mar 2022

  40. DFT - Tutorial 2. Mparkan.github.io. https://mparkan.github.io/Digital-Forestry-Toolbox/tutorial-2.html. Accessed11 Mar 2022

  41. GitHub - open-mmlab/OpenPCDet: OpenPCDet Toolbox for LiDAR-based 3D Object Detection. (2022). https://github.com/open-mmlab/OpenPCDet. Accessed11 Mar 2022

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Acknowledgement

This research project has been supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) ‘s Collaborative Research and Development Grant (CRD) – 3D Mobility Mapping Artificial Intelligence (3DMMAI) and Teledyne Geospatial Inc. We’d like to thank Andrew Sit (Product Manager), Burns Forster (Innovation Manager) and Chris Verheggen (SVP R&D) for supporting ALS data acquisition and postprocessing.

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Authors and Affiliations

  1. Department of Earth and Space Science and Engineering, Lassonde School of Engineering, York University Toronto, Toronto, ON, M2M 2P5, Canada

    Connie Ko, Yeonjeong Jeong, Hyungju Lee & Gunho Sohn

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  1. Connie Ko
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  2. Yeonjeong Jeong
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  3. Hyungju Lee
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  4. Gunho Sohn
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Correspondence to Gunho Sohn .

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Editors and Affiliations

  1. York University, Toronto, ON, Canada

    Jean-Jacques Rousseau

  2. University of Ontario Institute of Technology, Oshawa, ON, Canada

    Bill Kapralos

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Ko, C., Jeong, Y., Lee, H., Sohn, G. (2023). YUTO Tree5000: A Large-Scale Airborne LiDAR Dataset for Single Tree Detection. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13645. Springer, Cham. https://doi.org/10.1007/978-3-031-37731-0_28

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  • DOI: https://doi.org/10.1007/978-3-031-37731-0_28

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