[1]

Lu C, Guo Z, Miao L. 2025. The hot topics and trends about research on the relationship between air pollution and urban green space based on ecosystem services perspective. Air Quality, Atmosphere & Health 18(12):4059−4075

doi: 10.1007/s11869-025-01844-y
[2]

Liu HM, Qian WQ, Tang LJ, Wu YH, Zeng JY. 2024. Assessment of land use change of ecological green wedge and cooling island effect: a case study of Wuhan, China. Chinese Journal of Applied Ecology 35(2):480−488

doi: 10.13287/j.1001-9332.202402.019
[3]

Kim JE, Eum JH, Son JM. 2026. Analysis of air temperature reduction and indirect carbon savings by strategies of urban green space creation. Scientific Reports 16(1):5110

doi: 10.1038/s41598-026-35702-3
[4]

Elshabshiri A, Aly M, Alharbat R, Abdalla MJ, Alsyouf L, et al. 2025. Enhancing the microclimate of outdoor campus spaces in hot humid climates: the example of the University of Sharjah. Computational Urban Science 5(1):35

doi: 10.1007/s43762-025-00196-8
[5]

Alfaridzi MS, Harahap SD, Bayanuddin AA, Rangga IA. 2026. Resource review: remote sensing of urban green space. Progress in Physical Geography: Earth and Environment 50(1):161−164

doi: 10.1177/03091333251365850
[6]

Kasikam N, Yarnvudhi A, Leksungnoen N, Näsholm T, Tor-Ngern P. 2026. Effects of long term canopy change on regulating ecosystem services in a tropical urban park. Scientific Reports 16(1):5077

doi: 10.1038/s41598-026-36098-w
[7]

Liu B, Huang H, Su Y, Chen S, Li Z, et al. 2022. Tree species classification using ground-based LiDAR data by various point cloud deep learning methods. Remote Sensing 14(22):5733

doi: 10.3390/rs14225733
[8]

Zhang H, Liu B, Yang B, Guo J, Hu Z, et al. 2025. Efficient tree species classification using machine and deep learning algorithms based on UAV-LiDAR data in North China. Frontiers in Forests and Global Change 8:1431603

doi: 10.3389/ffgc.2025.1431603
[9]

Chen X, Shen X, Cao L. 2023. Tree species classification in subtropical natural forests using high-resolution UAV RGB and SuperView-1 multispectral imageries based on deep learning network approaches: a case study within the Baima snow mountain national nature reserve, China. Remote Sensing 15(10):2697

doi: 10.3390/rs15102697
[10]

Reisi Gahrouei O, Côté JF, Bournival P, Giguère P, Béland M. 2024. Comparison of deep and machine learning approaches for Quebec tree species classification using a combination of multispectral and LiDAR data. Canadian Journal of Remote Sensing 50:2359433

doi: 10.1080/07038992.2024.2359433
[11]

Cai Y, Gao L, Jia C, Liu X, Wang G, et al. 2025. Coniferous tree species classification based on DMCA-Unet network model with UAV multispectral imagery. Trees, Forests and People 22:101026

doi: 10.1016/j.tfp.2025.101026
[12]

Zhang M, Li W, Liu H, Zhao X, Tao R. 2023. Classification of hyperspectral forest tree species based on morphological transform and spatial logical integration. Acta Geodaetica et Cartographica Sinica 52(7):1202−1211

doi: 10.11947/j.AGCS.2023.20220492
[13]

Hong D, Han Z, Yao J, Gao L, Zhang B, et al. 2022. SpectralFormer: rethinking hyperspectral image classification with transformers. IEEE Transactions on Geoscience and Remote Sensing 60:5518615

doi: 10.1109/TGRS.2021.3130716
[14]

Man Q, Yang X, Liu H, Zhang B, Dong P, et al. 2025. Comparison of UAV-based LiDAR and photogrammetric point cloud for individual tree species classification of urban areas. Remote Sensing 17(7):1212

doi: 10.3390/rs17071212
[15]

Vahrenhold JR, Brandmeier M, Müller MS. 2025. MMTSCNet: multimodal tree species classification network for classification of multi-source, single-tree LiDAR point clouds. Remote Sensing 17(7):1304

doi: 10.3390/rs17071304
[16]

Hakula A, Ruoppa L, Lehtomäki M, Yu X, Kukko A, et al. 2023. Individual tree segmentation and species classification using high-density close-range multispectral laser scanning data. ISPRS Open Journal of Photogrammetry and Remote Sensing 9:100039

doi: 10.1016/j.ophoto.2023.100039
[17]

Wang L, Lu D, Xu L, Robinson DT, Tan W, et al. 2024. Individual tree species classification using low-density airborne multispectral LiDAR data via attribute-aware cross-branch transformer. Remote Sensing of Environment 315:114456

doi: 10.1016/j.rse.2024.114456
[18]

Njimi H, Chehata N, Revers F. 2024. Fusion of dense airborne LiDAR and multispectral sentinel-2 and Pleiades satellite imagery for mapping riparian forest species biodiversity at tree level. Sensors 24(6):1753

doi: 10.3390/s24061753
[19]

Marrs J, Ni-Meister W. 2019. Machine learning techniques for tree species classification using co-registered LiDAR and hyperspectral data. Remote Sensing 11(7):819

doi: 10.3390/rs11070819
[20]

Roffey M, Wang J. 2020. Evaluation of features derived from high-resolution multispectral imagery and LiDAR data for object-based support vector machine classification of tree species. Canadian Journal of Remote Sensing 46(4):473−488

doi: 10.1080/07038992.2020.1809363
[21]

Shi Y, Wang T, Skidmore AK, Heurich M. 2020. Improving LiDAR-based tree species mapping in Central European mixed forests using multi-temporal digital aerial colour-infrared photographs. International Journal of Applied Earth Observation and Geoinformation 84:101970

doi: 10.1016/j.jag.2019.101970
[22]

Mäyrä J, Keski-Saari S, Kivinen S, Tanhuanpää T, Hurskainen P, et al. 2021. Tree species classification from airborne hyperspectral and LiDAR data using 3D convolutional neural networks. Remote Sensing of Environment 256:112322

doi: 10.1016/j.rse.2021.112322
[23]

Chen K, Guan H, Wang L, Yu Y, Zang Y, et al. 2024. A feature perturbation weakly supervised learning network for airborne multispectral LiDAR pointcloud classification. International Journal of Applied Earth Observation and Geoinformation 127:103683

doi: 10.1016/j.jag.2024.103683
[24]

Chen K, Wang Y, Huang C, Wang J, Li SL, et al. 2025. GreenNet: a dual-encoder network for urban green space classification using high-resolution remotely sensed images. International Journal of Applied Earth Observation and Geoinformation 142:104709

doi: 10.1016/j.jag.2025.104709
[25]

You H, Feng Y, Zhao X, Zou C, Ji R, et al. 2019. PVRNet: point-view relation neural network for 3D shape recognition. Proceedings of the AAAI Conference on Artificial Intelligence 33(1):9119−9126

doi: 10.1609/aaai.v33i01.33019119
[26]

He K, Zhang X, Ren S, Sun J. 2016. Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE. pp. 770−778 doi: 10.1109/CVPR.2016.90
[27]

Thomas H, Qi CR, Deschaud JE, Marcotegui B, Goulette F, et al. 2019. KPConv: flexible and deformable convolution for point clouds. 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul: IEEE. pp. 6410−6419 doi: 10.1109/iccv.2019.00651
[28]

Hell M, Brandmeier M, Briechle S, Krzystek P. 2022. Classification of tree species and standing dead trees with lidar point clouds using two deep neural networks: PointCNN and 3DmFV-net. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science 90(2):103−121

doi: 10.1007/s41064-022-00200-4
[29]

Briechle S, Krzystek P, Vosselman G. 2020. Classification of tree species and standing dead trees by fusing uav-based lidar data and multispectral imagery in the 3d deep neural network pointnet++. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V(2):203−210

doi: 10.5194/isprs-annals-v-2-2020-203-2020
[30]

Chen Y, Liu X, Xiao Y, Zhao Q, Wan S. 2021. Three-dimensional urban land cover classification by prior-level fusion of LiDAR point cloud and optical imagery. Remote Sensing 13(23):4928

doi: 10.3390/rs13234928
[31]

Hu Q, Yang B, Xie L, Rosa S, Guo Y, et al. 2020. RandLA-net: efficient semantic segmentation of large-scale point clouds. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Seattle: IEEE. pp. 11105−11114 doi: 10.1109/cvpr42600.2020.01112
[32]

Li X, Wang L, Guan H, Chen K, Zang Y, et al. 2023. Urban tree species classification using UAV-based multispectral images and LiDAR point clouds. Journal of Geovisualization and Spatial Analysis 8(1):5

doi: 10.1007/s41651-023-00167-9
[33]

Hui Z, Jin S, Xia Y, Nie Y, Xie X, et al. 2021. A mean shift segmentation morphological filter for airborne LiDAR DTM extraction under forest canopy. Optics & Laser Technology 136:106728

doi: 10.1016/j.optlastec.2020.106728
[34]

Silva CA, Klauberg C, Hentz ÂMK, Dalla Corte AP, Ribeiro U, et al. 2018. Comparing the performance of ground filtering algorithms for terrain modeling in a forest environment using airborne LiDAR data. Floresta e Ambiente 25(2):e20160150

doi: 10.1590/2179-8087.015016
[35]

Qiu S, Anwar S, Barnes N. 2021. Semantic segmentation for real point cloud scenes via bilateral augmentation and adaptive fusion. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville: IEEE. pp. 1757−1767 doi: 10.1109/CVPR46437.2021.00180
[36]

Qian G, Li Y, Peng H, Mai J, Hammoud H, et al. 2022. PointNeXt: revisiting PointNet++ with improved training and scaling strategies. Advances in Neural Information Processing Systems 35. New Orleans: Neural Information Processing Systems Foundation, Inc. (NeurIPS). pp. 23192−23204 doi: 10.52202/068431-1685
[37]

Fan S, Dong Q, Zhu F, Lv Y, Ye P, et al. 2021. SCF-net: learning spatial contextual features for large-scale point cloud segmentation. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Nashville: IEEE. pp. 14499−14508 doi: 10.1109/cvpr46437.2021.01427
[38]

Shuai H, Xu X, Liu Q. 2021. Backward attentive fusing network with local aggregation classifier for 3D point cloud semantic segmentation. IEEE Transactions on Image Processing 30:4973−4984

doi: 10.1109/TIP.2021.3073660
[39]

Vu T, Kim K, Luu TM, Nguyen T, Yoo CD. 2022. SoftGroup for 3D instance segmentation on point clouds. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans: IEEE. pp. 2698−2707 doi: 10.1109/cvpr52688.2022.00273
[40]

Chen M, Hu Q, Yu Z, Thomas H, Feng A, et al. 2022. STPLS3D: a large-scale synthetic and real aerial photogrammetry 3D point cloud dataset. Proceedings of the 33rd British Machine Vision Conference (BMVC 2022). London, United Kingdom. https://arxiv.org/abs/2203.09065