Abstract
Trees are essential for climate change adaptation or even mitigation to some extent. To leverage their potential, effective forest and urban tree management is required. Automated tree detection, localisation, and species classification are crucial to any forest and urban tree management plan. Over the last decade, many studies aimed at tree species classification using aerial imagery yet due to several environmental challenges results were sub-optimal. This study aims to contribute to this domain by first, generating a labelled tree species dataset using Google Maps static API to supply aerial images and Trees In Camden inventory to supply species information, GPS coordinates (Latitude and Longitude), and tree diameter. Furthermore, this study investigates how state-of-the-art deep Convolutional Neural Network models including VGG19, ResNet50, DenseNet121, and InceptionV3 can handle the species classification problem of the urban trees using aerial images. Experimental results show our best model, InceptionV3 achieves an average accuracy of 73.54 over 6 tree species.
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References
Baeten, L., Bruelheide, H.: Identifying the tree species compositions that maximize ecosystem functioning in European forests. J. Appl. Ecol. 56(3), 733–744 (2018)
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)
Cao, K., Zhang, X.: An improved Res-UNet model for tree species classification using airborne high-resolution images. Remote Sens. 12(7), 1128 (2020)
Chandler, K., Stevens, C., Binley, A., Keith, A.: Influence of tree species and forest land use on soil hydraulic conductivity and implications for surface runoff generation. Geoderma 310, 120–127 (2017)
London Borough of Camden Council. Trees in Camden: open data portal (May 2021)
Dalponte, M., Frizzera, L., Gianelle, D.: Individual tree crown delineation and tree species classification with hyperspectral and lidar data. PeerJ 6, e6227 (2019)
Donovan, G.H., Landry, S., Winter, C.: Urban trees, house price, and redevelopment pressure in Tampa, Florida. Urban Forest. Urban Greening 38, 330–336 (2019)
Fricker, G.A., Ventura, J.D., Wolf, J.A., North, M.P., Davis, F.W., Franklin, J.: A convolutional neural network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery. Remote Sens. 11(19), 2326 (2019)
Gamfeldt, L., et al.: Higher levels of multiple ecosystem services are found in forests with more tree species. Nat. Commun. 4(1), 1340 (2013). https://doi.org/10.1038/ncomms2328
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Van Hulse, J., Khoshgoftaar, T.M., Napolitano, A.: Experimental perspectives on learning from imbalanced data. In: Proceedings of the 24th International Conference on Machine Learning, ICML 2007, pp. 935–942 (2007). https://doi.org/10.1145/1273496.1273614
Kim, S., Schreuder, G., Mcgaughey, R., Andersen, H.E.: Individual tree species identification using LiDAR intensity data. In: ASPRS 2008 Annual Conference, Portland (2008)
Koch, B., Heyder, U., Weinacker, H.: Detection of individual tree crowns in airborne LiDAR data. Photogram. Eng. Remote Sens. 72(4), 357–363 (2006)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
Lang, N.: Deep learning and google maps for tree monitoring (2020)
Li, H., Hu, B., Li, Q., Jing, L.: CNN-based tree species classification using airborne LiDAR data and high-resolution satellite image. In: 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020, pp. 2679–2682. IEEE (2020)
Manickathan, L., Defraeye, T., Allegrini, J., Derome, D., Carmeliet, J.: Parametric study of the influence of environmental factors and tree properties on the transpirative cooling effect of trees. Agric. For. Meteorol. 248, 259–274 (2017)
Maschler, J., Atzberger, C., Immitzer, M.: Individual tree crown segmentation and classification of 13 tree species using airborne hyperspectral data. Remote Sens. 10(8), 1218 (2018)
Natesan, S., Armenakis, C., Vepakomma, U.: ResNet-based tree species classification using UAV images. Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. XLII–2/W13, 475–481 (2019)
Natesan, S., Armenakis, C., Vepakomma, U.: Individual tree species identification using dense convolutional network (DenseNet) on multitemporal RGB images from UAV. J. Unmanned Veh. Syst. 8(4), 310–333 (2020)
Nezami, S., Khoramshahi, E., Nevalainen, O., Pölönen, I., Honkavaara, E.: Tree species classification of drone hyperspectral and RGB imagery with deep learning convolutional neural networks. Remote Sens. 12(7), 1070 (2020)
Nilsson, M.: Estimation of tree heights and stand volume using an airborne LiDAR system. Remote Sens. Environ. 56(1), 1–7 (1996)
Onishi, M., Ise, T.: Automatic classification of trees using a UAV onboard camera and deep learning. arXiv preprint arXiv:1804.10390 (2018)
Rezatec: Satellites vs. Lidar for forestry management? (2020)
Rust, S.: Tree inventory, risk assessment and management. In: Roloff, A. (ed.) Urban Tree Management: For the Sustainable Development of Green Cities, pp. 178–210. Wiley, Gottingen (2016)
Safonova, A., Tabik, S., Alcaraz-Segura, D., Rubtsov, A., Maglinets, Y., Herrera, F.: Detection of fir trees (Abies sibirica) damaged by the bark beetle in unmanned aerial vehicle images with deep learning. Remote Sens. 11(6), 643 (2019)
Saheer, L.B., Shahawy, M.: Self-supervised approach for urban tree recognition on aerial images. In: Proceedings of the 17th International conference on Artificial Intelligence Applications and Innovations (2021)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv arXiv:1409.1556 (2014)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Wegner, J.D.: Cataloging public objects using aerial and street-level images - urban trees. Accessed 1 May 2020
Wegner, J.D., Branson, S., Hall, D., Schindler, K., Perona, P.: Cataloging public objects using aerial and street-level images-urban trees. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6014–6023 (2016)
Wilkes, P., Disney, M., Vicari, M.B., Calders, K., Burt, A.: Estimating urban above ground biomass with multi-scale LiDAR. Carbon Balance Manage. 13(1), 1–20 (2018). https://doi.org/10.1186/s13021-018-0098-0
Wolf, K.L.: Business district streetscapes, trees, and consumer response. J. Forest. 103(8), 396–400 (2005)
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929 (2016)
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Oghaz, M.M.D., Saheer, L.B., Zarrin, J. (2022). Urban Tree Detection and Species Classification Using Aerial Imagery. In: Arai, K. (eds) Intelligent Computing. SAI 2022. Lecture Notes in Networks and Systems, vol 507. Springer, Cham. https://doi.org/10.1007/978-3-031-10464-0_32
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DOI: https://doi.org/10.1007/978-3-031-10464-0_32
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