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We introduce a novel deep learning method for detection of individual trees in urban environments using high-resolution multispectral aerial imagery.
Dual path multi-scale fusion networks with attention for crowd counting
Zhu, L., Zhao, Z., Lu, C., Lin, Y., Peng, Y., Yao, T., 2019 · 1902
Earlier work this paper cites.
Preprocessing transformations and their effects on multispectral recognition, in: Proceedings of the 6th International Symposium on Remote Sensing of Environment, pp. 97–131
Kriegler, F., Malila, W., Nalepka, R., Richardson, W., 1969 · 1969
Earlier work this paper cites.
Monitoring vegetation systems in the great plains with ERTS, in: Proceedings, 3rd Earth Resource Technology Satellite (ERTS) Symposium, pp. 48–62
Rouse, J., Haas, R., J.A. Scheel, J., Deering, D., 1974 · 1974
Earlier work this paper cites.
Assessing the benefits and costs of the urban forest
Dwyer, J.F., McPherson, E.G., Schroeder, H.W., Rowntree, R.A., 1992 · 1992
Earlier work this paper cites.
A comparison of municipal forest benefits and costs in modesto and santa monica, california, usa
McPherson, E.G., Simpson, J.R., 2002 · 2002
Earlier work this paper cites.
Using AVIRIS data and multiple-masking techniques to map urban forest tree species
Xiao, Q., Ustin, S.L., McPherson, E.G., 2004 · 2004
Earlier work this paper cites.
Openstreetmap: User-generated street maps
Haklay, M., Weber, P., 2008 · 2008
Earlier work this paper cites.
2d tree detection in large urban landscapes using aerial lidar data, in: 2009 16th IEEE International Conference on Image Processing (ICIP), pp. 1693–1696
Chen, G., Zakhor, A., 2009 · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database, in: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L., 2009 · 2009
Earlier work this paper cites.
Tree detection from aerial imagery, in: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 131–137
Yang, L., Wu, X., Praun, E., Ma, X., 2009 · 2009
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks, in: Teh, Y.W., Titterington, M. (Eds.), Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, PMLR, Chia Laguna Resort, Sardinia, Italy. pp. 249–256
Glorot, X., Bengio, Y., 2010 · 2010
Earlier work this paper cites.
Learning to count objects in images, in: Lafferty, J., Williams, C., Shawe-Taylor, J., Zemel, R., Culotta, A. (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc
Lempitsky, V., Zisserman, A., 2010 · 2010
Earlier work this paper cites.
Selecting reference cities for i-Tree Streets
McPherson, E.G., 2010 · 2010
Earlier work this paper cites.
Classification of urban tree species using hyperspectral imagery
Jensen, R.R., Hardin, P.J., Hardin, A.J., 2012 · 2011
Earlier work this paper cites.
Identifying Santa Barbara’s urban tree species from AVIRIS imagery using canonical discriminant analysis
Alonzo, M., Roth, K., Roberts, D., 2013 · 2013
Earlier work this paper cites.
The luxury of vegetation and the legacy of tree biodiversity in Los Angeles, CA
Clarke, L.W., Jenerette, G.D., Davila, A., 2013 · 2013
Earlier work this paper cites.
Classification of australian native forest species using hyperspectral remote sensing and machine-learning classification algorithms
Shang, X., Chisholm, L.A., 2014 · 2013
Earlier work this paper cites.
Land use - 2014 - land iq [ds2677]
Land IQ, L., 2017 · 2014
Earlier work this paper cites.
Review of urban tree inventory methods used to collect data at single-tree level
Nielsen, A.B., Östberg, J., Delshammar, T., et al., 2014 · 2014
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: International Conference on Machine Learning, PMLR. pp. 448–456
Ioffe, S., Szegedy, C., 2015 · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization, in: International Conference on Learning Representations
Kingma, D.P., Ba, J., 2015 · 2015
Earlier work this paper cites.
A novel transferable individual tree crown delineation model based on Fishing Net Dragging and boundary classification
Liu, T., Im, J., Quackenbush, L.J., 2015 · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition, in: Bengio, Y., LeCun, Y. (Eds.), 3rd International Conference on Learning Representations, ICLR
Simonyan, K., Zisserman, A., 2015 · 2015
Cited alongside, same era.
Tensorflow: A system for large-scale machine learning, in: Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation, USENIX Association, USA. p. 265–283
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D.G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., Zheng, X., 2016 · 2016
Cited alongside, same era.
The urban forest and ecosystem services: Impacts on urban water, heat, and pollution cycles at the tree, street, and city scale
Livesley, S.J., McPherson, E.G., Calfapietra, C., 2016 · 2016
Cited alongside, same era.
lidR: An R package for analysis of airborne laser scanning (ALS) data
Roussel, J.R., Auty, D., Coops, N.C., Tompalski, P., Goodbody, T.R., Meador, A.S., Bourdon, J.F., De Boissieu, F., Achim, A., 2020 · 2020
Later among the works it cites.
Decennial census population
U.S. Census Bureau, 2020 · 2020
Later among the works it cites.
Individual tree crown segmentation directly from UAV-borne LiDAR data using the PointNet of deep learning
Chen, X., Jiang, K., Zhu, Y., Wang, X., Yun, T., 2021 · 2021
Later among the works it cites.
Deep learning-based tree species mapping in a highly diverse tropical urban setting
Martins, G.B., La Rosa, L.E.C., Happ, P.N., Filho, L.C.T.C., Santos, C.J.F., Feitosa, R.Q., Ferreira, M.P., 2021 · 2021
Later among the works it cites.
Counting trees in a subtropical mega city using the instance segmentation method
Sun, Y., Li, Z., He, H., Guo, L., Zhang, X., Xin, Q., 2022 · 2021
Later among the works it cites.
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Structure, function and value of street trees in California, USA
McPherson, E.G., van Doorn, N., de Goede, J., 2016 · 2016
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
Shelhamer, E., Long, J., Darrell, T., 2017 · 2016
Cited alongside, same era.
Imputation of individual longleaf pine (Pinus palustris Mill.) tree attributes from field and LiDAR data
Silva, C.A., Hudak, A.T., Vierling, L.A., Loudermilk, E.L., O’Brien, J.J., Hiers, J.K., Jack, S.B., Gonzalez-Benecke, C., Lee, H., Falkowski, M.J., Khosravipour, A., 2016 · 2016
Cited alongside, same era.
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
Wegner, J.D., Branson, S., Hall, D., Schindler, K., Perona, P., 2016 · 2016
Cited alongside, same era.
Individual tree crown delineation using localized contour tree method and airborne LiDAR data in coniferous forests
Wu, B., Yu, B., Wu, Q., Huang, Y., Chen, Z., Wu, J., 2016 · 2016
Cited alongside, same era.
From Google Maps to a fine-grained catalog of street trees
Branson, S., Wegner, J.D., Hall, D., Lang, N., Schindler, K., Perona, P., 2018 · 2017
Cited alongside, same era.
The structure, function and value of urban forests in california communities
McPherson, E.G., Xiao, Q., van Doorn, N.S., de Goede, J., Bjorkman, J., Hollander, A., Boynton, R.M., Quinn, J.F., Thorne, J.H., 2017 · 2017
Cited alongside, same era.
The use of three-dimensional convolutional neural networks to interpret lidar for forest inventory
Ayrey, E., Hayes, D.J., 2018 · 2018
Cited alongside, same era.
A crown morphology-based approach to individual tree detection in subtropical mixed broadleaf urban forests using UAV LiDAR data
Xu, W., Deng, S., Liang, D., Cheng, X., 2021 · 2021
Later among the works it cites.
Benchmarking anchor-based and anchor-free state-of-the-art deep learning methods for individual tree detection in rgb high-resolution images
Zamboni, P., Junior, J.M., Silva, J.d.A., Miyoshi, G.T., Matsubara, E.T., Nogueira, K., Gonçalves, W.N., 2021 · 2021
Later among the works it cites.
The Auto Arborist Dataset: A large-scale benchmark for multiview urban forest monitoring under domain shift, in: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21262–21275
Beery, S., Wu, G., Edwards, T., Pavetic, F., Majewski, B., Mukherjee, S., Chan, S., Morgan, J., Rathod, V., Huang, J., 2022 · 2022
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Transformer for tree counting in aerial images
Chen, G., Shang, Y., 2022 · 2022
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GeoAI to implement an individual tree inventory: Framework and application of heat mitigation
Das, S., Sun, Q., Zhou, H., 2022 · 2022
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Individual tree crown delineation in high-resolution remote sensing images based on u-net
Freudenberg, M., Magdon, P., Nölke, N., 2022 · 2022
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Diversity and structure in california’s urban forest: What over six million data points tell us about one of the world’s largest urban forests
Love, N.L., Nguyen, V., Pawlak, C., Pineda, A., Reimer, J.L., Yost, J.M., Fricker, G.A., Ventura, J.D., Doremus, J.M., Crow, T., Ritter, M.K., 2022 · 2022
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Mapping the urban forest in detail: From lidar point clouds to 3d tree models
Münzinger, M., Prechtel, N., Behnisch, M., 2022 · 2022
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Detecting and mapping tree crowns based on convolutional neural network and google earth images
Yang, M., Mou, Y., Liu, S., Meng, Y., Liu, Z., Li, P., Xiang, W., Zhou, X., Peng, C., 2022 · 2022
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Individual tree detection based on high-resolution RGB images for urban forestry applications
Zhang, L., Lin, H., Wang, F., 2022 · 2022
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Accurate delineation of individual tree crowns in tropical forests from aerial rgb imagery using mask r-cnn
Ball, J.G., Hickman, S.H., Jackson, T.D., Koay, X.J., Hirst, J., Jay, W., Archer, M., Aubry-Kientz, M., Vincent, G., Coomes, D.A., 2023 · 2023
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Individual tree-crown detection and species identification in heterogeneous forests using aerial rgb imagery and deep learning
Beloiu, M., Heinzmann, L., Rehush, N., Gessler, A., Griess, V.C., 2023 · 2023
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Arcgis pro
ESRI, 2023 · 2023
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Results: Individual tree detection in large-scale urban environments using high-resolution multispectral imagery
Ventura, J., Pawlak, C., Honsberger, M., Gonsalves, C., Rice, J., Love, N., Han, S., Nguyen, V., Sugano, K., Doremus, J., Fricker, G.A., Yost, J., Ritter, M., 2024 · 2024
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Tree-centric mapping of forest carbon density from airborne laser scanning and hyperspectral data
Dalponte, M., Coomes, D.A., 2016 · 2041
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