Urban Trees, Individual Trees, Street Trees

Chapter Contents (Back)
Trees. Urban Trees. Street Trees.
See also Trees, Individual Trees.
See also Trees, Forest, Stem Volume, Aboveground Biomass Measurements.

Zhang, Y.[Yun],
Texture-Integrated Classification of Urban Treed Areas in High-Resolution Color-Infrared Imagery,
PhEngRS(67), No. 12, December 2001, pp. 1359-1366. To effectively extract tree textural features and eliminate noise, use conditional variance detection. It consists of a directional variance detection and a local variance detection.
WWW Link. 0201

Ouma, Y.O.[Yashon O.], Tateishi, R.,
Urban-trees extraction from Quickbird imagery using multiscale spectex-filtering and non-parametric classification,
PandRS(63), No. 3, May 2008, pp. 333-351.
Elsevier DOI 0711
Quickbird; Urban-trees; Multiscale texture; Multiscale spectex-filtering; Non-parametric classification BibRef

Ardila, J.P.[Juan P.], Tolpekin, V.A.[Valentyn A.], Bijker, W.[Wietske], Stein, A.[Alfred],
Markov-random-field-based super-resolution mapping for identification of urban trees in VHR images,
PandRS(66), No. 6, November 2011, pp. 762-775.
Elsevier DOI 1112
Image classification; Markov random field; Super resolution mapping; Urban trees; Contextual classification BibRef

Ardila, J.P.[Juan P.], Bijker, W.[Wietske], Tolpekin, V.A.[Valentyn A.], Stein, A.[Alfred],
Quantification of crown changes and change uncertainty of trees in an urban environment,
PandRS(74), No. 1, November 2012, pp. 41-55.
Elsevier DOI 1212
Change detection; Fuzzy change; Object change detection; Tree crown detection; Urban trees BibRef

Höfle, B.[Bernhard], Hollaus, M.[Markus], Hagenauer, J.[Julian],
Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data,
PandRS(67), No. 1, January 2012, pp. 134-147.
Elsevier DOI 1202
Laser scanning; LiDAR; Calibration; Vegetation; Object based image analysis; Full-waveform BibRef

Shrestha, R., Wynne, R.,
Estimating Biophysical Parameters of Individual Trees in an Urban Environment Using Small Footprint Discrete-Return Imaging Lidar,
RS(4), No. 2, February 2012, pp. 484-508.
DOI Link 1203

Zhang, K., Hu, B.,
Individual Urban Tree Species Classification Using Very High Spatial Resolution Airborne Multi-Spectral Imagery Using Longitudinal Profiles,
RS(4), No. 6, June 2012, pp. 1741-1757.
DOI Link 1208

Agarwal, S., Vailshery, L., Jaganmohan, M., Nagendra, H.,
Mapping Urban Tree Species Using Very High Resolution Satellite Imagery: Comparing Pixel-Based and Object-Based Approaches,
IJGI(2), No. 1, 2013, pp. 220-236.
DOI Link 1303

Wu, B., Yu, B., Yue, W., Shu, S., Tan, W., Hu, C., Huang, Y., Wu, J., Liu, H.,
A Voxel-Based Method for Automated Identification and Morphological Parameters Estimation of Individual Street Trees from Mobile Laser Scanning Data,
RS(5), No. 2, February 2013, pp. 584-611.
DOI Link 1303

Zhou, J.H.[Jian-Hua], Yu, B.[Bailang], Qin, J.[Jun],
Multi-Level Spatial Analysis for Change Detection of Urban Vegetation at Individual Tree Scale,
RS(6), No. 9, 2014, pp. 9086-9103.
DOI Link 1410

Zhang, C.Y.[Cai-Yun], Zhou, Y.H.[Yu-Hong], Qiu, F.[Fang],
Individual Tree Segmentation from LiDAR Point Clouds for Urban Forest Inventory,
RS(7), No. 6, 2015, pp. 7892.
DOI Link 1507

Li, D.[Dan], Ke, Y.H.[Ying-Hai], Gong, H.[Huili], Li, X.J.[Xiao-Juan],
Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images,
RS(7), No. 12, 2015, pp. 15861.
DOI Link 1601

Li, L.[Lin], Li, D.[Dalin], Zhu, H.H.[Hai-Hong], Li, Y.[You],
A dual growing method for the automatic extraction of individual trees from mobile laser scanning data,
PandRS(120), No. 1, 2016, pp. 37-52.
Elsevier DOI 1610
Individual tree BibRef

Guan, H.Y.[Hai-Yan], Cao, S., Yu, Y.T.[Yong-Tao], Li, J.[Jonathan], Liu, N., Chen, P., Li, Y.,
Street-Scene Tree Segmentation from Mobile Laser Scanning Data,
ISPRS16(B3: 221-225).
DOI Link 1610

Li, Y.[You], Li, L.[Lin], Li, D.[Dalin], Yang, F.[Fan], Liu, Y.[Yu],
A Density-Based Clustering Method for Urban Scene Mobile Laser Scanning Data Segmentation,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link 1705

Yu, Y.T.[Yong-Tao], Li, J.[Jonathan], Guan, H.Y.[Hai-Yan], Wang, C.[Cheng], Wen, C.,
Bag of Contextual-Visual Words for Road Scene Object Detection From Mobile Laser Scanning Data,
ITS(17), No. 12, December 2016, pp. 3391-3406.
Automobiles BibRef

Yu, Y.T.[Yong-Tao], Li, J.[Jonathan], Guan, H.Y.[Hai-Yan], Wang, C.[Cheng], Cheng, M.[Ming],
A Marked Point Process for Automated Tree Detection from Mobile Laser Scanning Point Cloud Data,

See also Automated Detection of Three-Dimensional Cars in Mobile Laser Scanning Point Clouds Using DBM-Hough-Forests.
See also Traffic Sign Occlusion Detection Using Mobile Laser Scanning Point Clouds. BibRef

Yu, Y.T.[Yong-Tao], Li, J., Guan, H.Y.[Hai-Yan], Zai, D., Wang, C.,
Automated Extraction of 3D Trees from Mobile LiDAR Point Clouds,
DOI Link 1411

Le Louarn, M.[Marine], Clergeau, P.[Philippe], Briche, E.[Elodie], Deschamps-Cottin, M.[Magali],
'Kill Two Birds with One Stone': Urban Tree Species Classification Using Bi-Temporal Pléiades Images to Study Nesting Preferences of an Invasive Bird,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711

Branson, S.[Steve], Wegner, J.D.[Jan Dirk], Hall, D.[David], Lang, N.[Nico], Schindler, K.[Konrad], Perona, P.[Pietro],
From Google Maps to a fine-grained catalog of street trees,
PandRS(135), No. Supplement C, 2018, pp. 13-30.
Elsevier DOI 1712
Award, U.V. Helava, ISPRS. Deep learning, Image interpretation, Urban areas, Street trees, Very high resolution BibRef

Herfort, B.[Benjamin], Höfle, B.[Bernhard], Klonner, C.[Carolin],
3D micro-mapping: Towards assessing the quality of crowdsourcing to support 3D point cloud analysis,
PandRS(137), 2018, pp. 73-83.
Elsevier DOI 1802
LiDAR, Urban trees, Crowdsourcing, Point cloud classification, Quality BibRef

Zhang, Y.L.[Yong-Lin], Dong, R.C.[Ren-Cai],
Impacts of Street-Visible Greenery on Housing Prices: Evidence from a Hedonic Price Model and a Massive Street View Image Dataset in Beijing,
IJGI(7), No. 3, 2018, pp. xx-yy.
DOI Link 1804

Adeline, K.R.M., Briottet, X., Ceamanos, X., Dartigalongue, T., Gastellu-Etchegorry, J.P.,
ICARE-VEG: A 3D physics-based atmospheric correction method for tree shadows in urban areas,
PandRS(142), 2018, pp. 311-327.
Elsevier DOI 1807
Atmospheric correction, Radiative transfer, Hyperspectral, High spatial resolution, Tree shadows, Urban areas BibRef

Singh, K.K.[Kunwar K.], Chen, Y.H.[Yin-Hsuen], Smart, L.[Lindsey], Gray, J.[Josh], Meentemeyer, R.K.[Ross K.],
Intra-annual phenology for detecting understory plant invasion in urban forests,
PandRS(142), 2018, pp. 151-161.
Elsevier DOI 1807
Biological invasion, Vegetation indices, Vegetation phenology, Normalized difference vegetation index, , Chinese privet, Random forest BibRef

Vahidi, H.[Hossein], Klinkenberg, B.[Brian], Johnson, B.A.[Brian A.], Moskal, L.M.[L. Monika], Yan, W.L.[Wang-Lin],
Mapping the Individual Trees in Urban Orchards by Incorporating Volunteered Geographic Information and Very High Resolution Optical Remotely Sensed Data: A Template Matching-Based Approach,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808

Hu, R.H.[Rong-Hai], Bournez, E.[Elena], Cheng, S.Y.[Shi-Yu], Jiang, H.[Hailan], Nerry, F.[Françoise], Landes, T.[Tania], Saudreau, M.[Marc], Kastendeuch, P.[Pierre], Najjar, G.[Georges], Colin, J.[Jérôme], Yan, G.J.[Guang-Jian],
Estimating the leaf area of an individual tree in urban areas using terrestrial laser scanner and path length distribution model,
PandRS(144), 2018, pp. 357-368.
Elsevier DOI 1809
Individual tree, Leaf area, Foliage area volume density, Terrestrial laser scanner, Urban areas, Path length distribution BibRef

Wu, J.W.[Jian-Wei], Yao, W.[Wei], Polewski, P.[Przemyslaw],
Mapping Individual Tree Species and Vitality along Urban Road Corridors with LiDAR and Imaging Sensors: Point Density versus View Perspective,
RS(10), No. 9, 2018, pp. xx-yy.
DOI Link 1810

Mozgeris, G.[Gintautas], Juodkiene, V.[Vytaute], Jonikavicius, D.[Donatas], Straigyte, L.[Lina], Gadal, S.[Sébastien], Ouerghemmi, W.[Walid],
Ultra-Light Aircraft-Based Hyperspectral and Colour-Infrared Imaging to Identify Deciduous Tree Species in an Urban Environment,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811

Aval, J.[Josselin], Demuynck, J.[Jean], Zenou, E.[Emmanuel], Fabre, S.[Sophie], Sheeren, D.[David], Fauvel, M.[Mathieu], Adeline, K.[Karine], Briottet, X.[Xavier],
Detection of individual trees in urban alignment from airborne data and contextual information: A marked point process approach,
PandRS(146), 2018, pp. 197-210.
Elsevier DOI 1812
Street tree, Urban remote sensing, Airborne data, Geographic information system, Marked point process. BibRef

Zhang, R.[Rong], Chen, J.Q.[Ji-Quan], Park, H.[Hogeun], Zhou, X.[Xuhui], Yang, X.C.[Xu-Chao], Fan, P.[Peilei], Shao, C.L.[Chang-Liang], Ouyang, Z.[Zutao],
Spatial Accessibility of Urban Forests in the Pearl River Delta (PRD), China,
RS(11), No. 6, 2019, pp. xx-yy.
DOI Link 1903

Li, X.[Xun], Chen, W.Y.[Wendy Y.], Sanesi, G.[Giovanni], Lafortezza, R.[Raffaele],
Remote Sensing in Urban Forestry: Recent Applications and Future Directions,
RS(11), No. 10, 2019, pp. xx-yy.
DOI Link 1906

Brabant, C.[Charlotte], Alvarez-Vanhard, E.[Emilien], Laribi, A.[Achour], Morin, G.[Gwénaël], Nguyen, K.T.[Kim Thanh], Thomas, A.[Alban], Houet, T.[Thomas],
Comparison of Hyperspectral Techniques for Urban Tree Diversity Classification,
RS(11), No. 11, 2019, pp. xx-yy.
DOI Link 1906

Stubbings, P.[Philip], Peskett, J.[Joe], Rowe, F.[Francisco], Arribas-Bel, D.[Dani],
A Hierarchical Urban Forest Index Using Street-Level Imagery and Deep Learning,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907

Sanesi, G.[Giovanni], Giannico, V.[Vincenzo], Elia, M.[Mario], Lafortezza, R.[Raffaele],
Remote Sensing of Urban Forests,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910

Kranjcic, N.[Nikola], Medak, D.[Damir], Župan, R.[Robert], Rezo, M.[Milan],
Machine Learning Methods for Classification of the Green Infrastructure in City Areas,
IJGI(8), No. 10, 2019, pp. xx-yy.
DOI Link 1910

Gupta, A., Byrne, J., Moloney, D., Watson, S., Yin, H.,
Tree Annotations in LiDAR Data Using Point Densities and Convolutional Neural Networks,
GeoRS(58), No. 2, February 2020, pp. 971-981.
Vegetation, Laser radar, Urban areas, Forestry, Training, Feature extraction, Airborne LiDAR, voxelization BibRef

Barbierato, E.[Elena], Bernetti, I.[Iacopo], Capecchi, I.[Irene], Saragosa, C.[Claudio],
Integrating Remote Sensing and Street View Images to Quantify Urban Forest Ecosystem Services,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001

Shen, W.J.[Wen-Juan], Mao, X.P.[Xu-Peng], He, J.Y.[Jia-Ying], Dong, J.[Jinwei], Huang, C.Q.[Cheng-Quan], Li, M.S.[Ming-Shi],
Understanding Current and Future Fragmentation Dynamics of Urban Forest Cover in the Nanjing Laoshan Region of Jiangsu, China,
RS(12), No. 1, 2020, pp. xx-yy.
DOI Link 2001

Laumer, D.[Daniel], Lang, N.[Nico], van Doorn, N.[Natalie], Mac Aodha, O.[Oisin], Perona, P.[Pietro], Wegner, J.D.[Jan Dirk],
Geocoding of trees from street addresses and street-level images,
PandRS(162), 2020, pp. 125-136.
Elsevier DOI 2004
Geocoding, Global optimization, Deep learning, Image interpretation, Object detection, Faster R-CNN, Google Street View BibRef

Wegner, J.D., Branson, S., Hall, D., Schindler, K., Perona, P.,
Cataloging Public Objects Using Aerial and Street-Level Images: Urban Trees,

Blackman, R.[Raoul], Yuan, F.[Fei],
Detecting Long-Term Urban Forest Cover Change and Impacts of Natural Disasters Using High-Resolution Aerial Images and LiDAR Data,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006

Maksimainen, M.[Mikko], Vaaja, M.T.[Matti T.], Kurkela, M.[Matti], Virtanen, J.P.[Juho-Pekka], Julin, A.[Arttu], Jaalama, K.[Kaisa], Hyyppä, H.[Hannu],
Nighttime Mobile Laser Scanning and 3D Luminance Measurement: Verifying the Outcome of Roadside Tree Pruning with Mobile Measurement of the Road Environment,
IJGI(9), No. 7, 2020, pp. xx-yy.
DOI Link 2007

Xu, J.Z.[Jing-Zhong], Shan, J.[Jie], Wang, G.[Ge],
Hierarchical Modeling of Street Trees Using Mobile Laser Scanning,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007

Katz, D.S.W.[Daniel S. W.], Batterman, S.A.[Stuart A.], Brines, S.J.[Shannon J.],
Improved Classification of Urban Trees Using a Widespread Multi-Temporal Aerial Image Dataset,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008

Chi, D.[Dengkai], Degerickx, J.[Jeroen], Yu, K.[Kang], Somers, B.[Ben],
Urban Tree Health Classification Across Tree Species by Combining Airborne Laser Scanning and Imaging Spectroscopy,
RS(12), No. 15, 2020, pp. xx-yy.
DOI Link 2008

Man, Q.X.[Qi-Xia], Dong, P.L.[Pin-Liang], Yang, X.M.[Xin-Ming], Wu, Q.Y.[Quan-Yuan], Han, R.Q.[Rong-Qing],
Automatic Extraction of Grasses and Individual Trees in Urban Areas Based on Airborne Hyperspectral and LiDAR Data,
RS(12), No. 17, 2020, pp. xx-yy.
DOI Link 2009

Timilsina, S.[Shirisa], Aryal, J.[Jagannath], Kirkpatrick, J.B.[Jamie B.],
Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN),
RS(12), No. 18, 2020, pp. xx-yy.
DOI Link 2009

Krucek, M.[Martin], Král, K.[Kamil], Cushman, K.[KC], Missarov, A.[Azim], Kellner, J.R.[James R.],
Supervised Segmentation of Ultra-High-Density Drone Lidar for Large-Area Mapping of Individual Trees,
RS(12), No. 19, 2020, pp. xx-yy.
DOI Link 2010

Wang, Y.J.[Yong-Jun], Jiang, T.P.[Teng-Ping], Liu, J.[Jing], Li, X.R.[Xiao-Rui], Liang, C.[Chong],
Hierarchical Instance Recognition of Individual Roadside Trees in Environmentally Complex Urban Areas from UAV Laser Scanning Point Clouds,
IJGI(9), No. 10, 2020, pp. xx-yy.
DOI Link 2010

He, S.B.[Shao-Bai], Du, H.Q.[Hua-Qiang], Zhou, G.[Guomo], Li, X.J.[Xue-Jian], Mao, F.J.[Fang-Jie], Zhu, D.[Di'en], Xu, Y.X.[Yan-Xin], Zhang, M.[Meng], Huang, Z.[Zihao], Liu, H.[Hua], Luo, X.[Xin],
Intelligent Mapping of Urban Forests from High-Resolution Remotely Sensed Imagery Using Object-Based U-Net-DenseNet-Coupled Network,
RS(12), No. 23, 2020, pp. xx-yy.
DOI Link 2012

Wang, Y.T.[Yu-Tang], Wang, J.[Jia], Chang, S.P.[Shu-Ping], Sun, L.[Lu], An, L.[Likun], Chen, Y.H.[Yu-Han], Xu, J.Q.[Jiang-Qi],
Classification of Street Tree Species Using UAV Tilt Photogrammetry,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101

Safaie, A.H.[Amir Hossein], Rastiveis, H.[Heidar], Shams, A.[Alireza], Sarasua, W.A.[Wayne A.], Li, J.[Jonathan],
Automated Street Tree Inventory Using Mobile LiDAR Point Clouds Based on Hough Transform and Active Contours,
PandRS(174), 2021, pp. 19-34.
Elsevier DOI 2103
Trees inventory, Mobile LiDAR, Point clouds, Hough transform, Active contour, Road safety BibRef

Przewozna, P.[Patrycja], Hawrylo, P.[Pawel], Zieba-Kulawik, K.[Karolina], Inglot, A.[Adam], Maczka, K.[Krzysztof], Wezyk, P.[Piotr], Matczak, P.[Piotr],
Use of Bi-Temporal ALS Point Clouds for Tree Removal Detection on Private Property in Racibórz, Poland,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103

Luo, H.F.[Hai-Feng], Khoshelham, K.[Kourosh], Chen, C.C.[Chong-Cheng], He, H.X.[Han-Xian],
Individual Tree Extraction from Urban Mobile Laser Scanning Point Clouds Using Deep Pointwise Direction Embedding,
PandRS(175), 2021, pp. 326-339.
Elsevier DOI 2105
Mobile laser scanning point clouds, Individual tree extraction, Semantic segmentation, Deep learning
See also Automatic Extraction of Roadside Traffic Facilities From Mobile Laser Scanning Point Clouds Based on Deep Belief Network. BibRef

Lumnitz, S.[Stefanie], Devisscher, T.[Tahia], Mayaud, J.R.[Jerome R.], Radic, V.[Valentina], Coops, N.C.[Nicholas C.], Griess, V.C.[Verena C.],
Mapping trees along urban street networks with deep learning and street-level imagery,
PandRS(175), 2021, pp. 144-157.
Elsevier DOI 2105
Deep learning, Instance segmentation, Monocular depth estimation, Street-level images, Urban forest management BibRef

Wang, Z.[Zhe], Fan, C.[Chao], Xian, M.[Min],
Application and Evaluation of a Deep Learning Architecture to Urban Tree Canopy Mapping,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105

Kim, A.R.[A Reum], Lim, C.H.[Chi Hong], Lim, B.S.[Bong Soon], Seol, J.W.[Jae-Won], Lee, C.S.[Chang Seok],
Phenological Changes of Mongolian Oak Depending on the Micro-Climate Changes Due to Urbanization,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105

Abbas, S.[Sawaid], Peng, Q.[Qian], Wong, M.S.[Man Sing], Li, Z.L.[Zhi-Lin], Wang, J.[Jicheng], Ng, K.T.K.[Kathy Tze Kwun], Kwok, C.Y.T.[Coco Yin Tung], Hui, K.K.W.[Karena Ka Wai],
Characterizing and classifying urban tree species using bi-monthly terrestrial hyperspectral images in Hong Kong,
PandRS(177), 2021, pp. 204-216.
Elsevier DOI 2106
Urban tree, Hyperspectral library, Tree species, Seasonality, Deep learning, SPECIM-IQ BibRef

Jiang, F.[Fugen], Chen, C.[Chuanshi], Li, C.J.[Cheng-Jie], Kutia, M.[Mykola], Sun, H.[Hua],
A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107

Martins, J.A.C.[José Augusto Correa], Nogueira, K.[Keiller], Osco, L.P.[Lucas Prado], Gomes, F.D.G.[Felipe David Georges], Furuya, D.E.G.[Danielle Elis Garcia], Gonçalves, W.N.[Wesley Nunes], Sant'Ana, D.A.[Diego André], Ramos, A.P.M.[Ana Paula Marques], Liesenberg, V.[Veraldo], dos Santos, J.A.[Jefersson Alex], de Oliveira, P.T.S.[Paulo Tarso Sanches], Junior, J.M.[José Marcato],
Semantic Segmentation of Tree-Canopy in Urban Environment with Pixel-Wise Deep Learning,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109

You, H.K.[Hang-Kai], Li, S.H.[Shi-Hua], Xu, Y.F.[Yi-Fan], He, Z.[Ze], Wang, D.[Di],
Tree Extraction from Airborne Laser Scanning Data in Urban Areas,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109

Mngadi, M.[Mthembeni], Odindi, J.[John], Mutanga, O.[Onisimo],
The Utility of Sentinel-2 Spectral Data in Quantifying Above-Ground Carbon Stock in an Urban Reforested Landscape,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112

Velasquez-Camacho, L.[Luisa], Cardil, A.[Adrián], Mohan, M.[Midhun], Etxegarai, M.[Maddi], Anzaldi, G.[Gabriel], de-Miguel, S.[Sergio],
Remotely Sensed Tree Characterization in Urban Areas: A Review,
RS(13), No. 23, 2021, pp. xx-yy.
DOI Link 2112

Hu, T.Y.[Tian-Yu], Wei, D.J.[Deng-Jie], Su, Y.J.[Yan-Jun], Wang, X.D.[Xu-Dong], Zhang, J.[Jing], Sun, X.L.[Xi-Liang], Liu, Y.[Yu], Guo, Q.H.[Qing-Hua],
Quantifying the shape of urban street trees and evaluating its influence on their aesthetic functions based on mobile lidar data,
PandRS(184), 2022, pp. 203-214.
Elsevier DOI 2202
Mobile mapping system, Street tree, Shape, Aesthetical value, Greenness BibRef

Schmohl, S.[Stefan], Vallejo, A.N.[Alejandra Narváez], Soergel, U.[Uwe],
Individual Tree Detection in Urban ALS Point Clouds with 3D Convolutional Networks,
RS(14), No. 6, 2022, pp. xx-yy.
DOI Link 2204

Yue, N.[Ning], Zhang, Z.X.[Zhen-Xin], Jiang, S.[Shan], Chen, S.[Siyun],
Deep Feature Migration for Real-Time Mapping of Urban Street Shading Coverage Index Based on Street-Level Panorama Images,
RS(14), No. 8, 2022, pp. xx-yy.
DOI Link 2205

Qin, L.[Longjun], Mao, P.[Peng], Xu, Z.B.[Zhen-Bang], He, Y.[Yang], Yan, C.H.[Chun-Hua], Hayat, M.[Muhammad], Qiu, G.Y.[Guo-Yu],
Accurate Measurement and Assessment of Typhoon-Related Damage to Roadside Trees and Urban Forests Using the Unmanned Aerial Vehicle,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link 2205

Guo, Q.[Qian], Zhang, J.[Jian], Guo, S.J.[Shi-Jie], Ye, Z.X.[Zhang-Xi], Deng, H.[Hui], Hou, X.L.[Xiao-Long], Zhang, H.[Houxi],
Urban Tree Classification Based on Object-Oriented Approach and Random Forest Algorithm Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208

Li, X.C.[Xiang-Cai], Tian, J.Y.[Jin-Yan], Li, X.J.[Xiao-Juan], Wang, L.[Le], Gong, H.[Huili], Shi, C.[Chen], Nie, S.[Sheng], Zhu, L.[Lin], Chen, B.B.[Bei-Bei], Pan, Y.[Yun], He, J.[Jijun], Ni, R.G.[Rong-Guang], Diao, C.Y.[Chun-Yuan],
Developing a sub-meter phenological spectral feature for mapping poplars and willows in urban environment,
PandRS(193), 2022, pp. 77-89.
Elsevier DOI 2210
Urban, Tree species classification, Phenology, Sub-meter, Multi-scale, Deep learning BibRef

Yan, J.[Jin], Chen, Y.Y.[Yuan-Yuan], Zheng, J.Z.[Jia-Zhu], Guo, L.[Lin], Zheng, S.Q.[Si-Qi], Zhang, R.C.[Rong-Chun],
Multi-Source Time Series Remote Sensing Feature Selection and Urban Forest Extraction Based on Improved Artificial Bee Colony,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210

Hua, Z.[Zhouyang], Xu, S.[Sheng], Liu, Y.G.[Yin-Gan],
Individual Tree Segmentation from Side-View LiDAR Point Clouds of Street Trees Using Shadow-Cut,
RS(14), No. 22, 2022, pp. xx-yy.
DOI Link 2212

Li, Z.Y.[Zhi-Yuan], Wang, J.[Jian], Zhang, Z.Y.[Zhen-Yu], Jin, F.X.[Feng-Xiang], Yang, J.T.[Jun-Tao], Sun, W.X.[Wen-Xiao], Cao, Y.[Yi],
A Method Based on Improved iForest for Trunk Extraction and Denoising of Individual Street Trees,
RS(15), No. 1, 2023, pp. xx-yy.
DOI Link 2301

Liu, Y.[Yang], Zhang, H.Q.[Huai-Qing], Cui, Z.[Zeyu], Lei, K.[Kexin], Zuo, Y.Q.[Yuan-Qing], Wang, J.[Jiansen], Hu, X.T.[Xing-Tao], Qiu, H.Q.[Han-Qing],
Very High Resolution Images and Superpixel-Enhanced Deep Neural Forest Promote Urban Tree Canopy Detection,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301

Shi, S.[Shuo], Tang, X.T.[Xing-Tao], Chen, B.[Bowen], Chen, B.[Biwu], Xu, Q.[Qian], Bi, S.[Sifu], Gong, W.[Wei],
Point Cloud Data Processing Optimization in Spectral and Spatial Dimensions Based on Multispectral Lidar for Urban Single-Wood Extraction,
IJGI(12), No. 3, 2023, pp. xx-yy.
DOI Link 2303
LiDAR processing to deal with large height variations with the tree. BibRef

Guo, J.H.[Jian-Hua], Xu, Q.S.[Qing-Song], Zeng, Y.[Yue], Liu, Z.H.[Zhi-Heng], Zhu, X.X.[Xiao Xiang],
Nationwide urban tree canopy mapping and coverage assessment in Brazil from high-resolution remote sensing images using deep learning,
PandRS(198), 2023, pp. 1-15.
Elsevier DOI 2304
Urban tree canopy, Brazil, Remote sensing, Semi-supervised learning, Urban ecosystem services BibRef

Hyyppä, E.[Eric], Manninen, P.[Petri], Maanpää, J.[Jyri], Taher, J.[Josef], Litkey, P.[Paula], Hyyti, H.[Heikki], Kukko, A.[Antero], Kaartinen, H.[Harri], Ahokas, E.[Eero], Yu, X.W.[Xiao-Wei], Muhojoki, J.[Jesse], Lehtomäki, M.[Matti], Virtanen, J.P.[Juho-Pekka], Hyyppä, J.[Juha],
Can the Perception Data of Autonomous Vehicles Be Used to Replace Mobile Mapping Surveys: A Case Study Surveying Roadside City Trees,
RS(15), No. 7, 2023, pp. 1790.
DOI Link 2304

Yankovich, E.P.[Elena Petrovna], Yankovich, K.S.[Ksenia Stanislavovna], Baranovskiy, N.V.[Nikolay Viktorovich],
Dynamics of Forest Vegetation in an Urban Agglomeration Based on Landsat Remote Sensing Data for the Period 1990-2022: A Case Study,
RS(15), No. 7, 2023, pp. 1935.
DOI Link 2304

Wang, P.C.[Peng-Cheng], Tang, Y.[Yong], Liao, Z.[Zefan], Yan, Y.[Yao], Dai, L.[Lei], Liu, S.[Shan], Jiang, T.[Tengping],
Road-Side Individual Tree Segmentation from Urban MLS Point Clouds Using Metric Learning,
RS(15), No. 8, 2023, pp. 1992.
DOI Link 2305

Jiang, T.P.[Teng-Ping], Wang, Y.J.[Yong-Jun], Liu, S.[Shan], Zhang, Q.[Qinyu], Zhao, L.[Lin], Sun, J.[Jian],
Instance recognition of street trees from urban point clouds using a three-stage neural network,
PandRS(199), 2023, pp. 305-334.
Elsevier DOI 2305
Urban point cloud, Semi-supervised semantic segmentation, Individual tree segmentation, Tree modeling, Deep learning BibRef

Wang, M.[Meilian], Wong, M.S.[Man Sing],
Exploring Influences of Leaves on Urban Species Identification Using Handheld Laser Scanning Point Cloud: A Case Study in Hong Kong,
RS(15), No. 11, 2023, pp. 2826.
DOI Link 2306

Wang, X.[Xuan], Xiang, H.Y.[Han-Yu], Niu, W.Y.[Wen-Yuan], Mao, Z.[Zhu], Huang, X.F.[Xian-Feng], Zhang, F.[Fan],
Oblique photogrammetry supporting procedural tree modeling in urban areas,
PandRS(200), 2023, pp. 120-137.
Elsevier DOI 2306
Oblique photogrammetry, Procedural modeling, Inverse procedural modeling, L-system, Parametric model, Metropolis-Hastings BibRef

Qin, H.M.[Hai-Ming], Wang, W.M.[Wei-Min], Yao, Y.[Yang], Qian, Y.G.[Yu-Guo], Xiong, X.Y.[Xiang-Yun], Zhou, W.Q.[Wei-Qi],
First Experience with Zhuhai-1 Hyperspectral Data for Urban Dominant Tree Species Classification in Shenzhen, China,
RS(15), No. 12, 2023, pp. xx-yy.
DOI Link 2307

Wu, H.[Hui], Zhuang, M.H.[Ming-Hao], Chen, Y.C.[Yuan-Chi], Meng, C.[Chen], Wu, C.Y.[Cai-Yan], Ouyang, L.[Linke], Liu, Y.H.[Yu-Han], Shu, Y.[Yi], Tao, Y.Z.[Yu-Zhong], Qiu, T.[Tong], Li, J.X.[Jun-Xiang],
Urban Treetop Detection and Tree-Height Estimation from Unmanned-Aerial-Vehicle Images,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link 2308

An, S.M.[Seung Man],
A Study on Urban-Scale Building, Tree Canopy Footprint Identification and Sky View Factor Analysis with Airborne LiDAR Remote Sensing Data,
RS(15), No. 15, 2023, pp. xx-yy.
DOI Link 2308

Javed, A.[Aisha], Kim, T.[Taeheon], Lee, C.[Changhui], Oh, J.[Jaehong], Han, Y.[Youkyung],
Deep Learning-Based Detection of Urban Forest Cover Change along with Overall Urban Changes Using Very-High-Resolution Satellite Images,
RS(15), No. 17, 2023, pp. 4285.
DOI Link 2310

Fan, X.H.[Xiang-Hua], Chen, Z.W.[Zhi-Wei], Liu, P.[Peilin], Pan, W.B.[Wen-Bo],
Simultaneous Vehicle Localization and Roadside Tree Inventory Using Integrated LiDAR-Inertial-GNSS System,
RS(15), No. 20, 2023, pp. 5057.
DOI Link 2310

Gong, H.Y.[Hao-Yu], Sun, Q.[Qian], Fang, C.[Chenrong], Sun, L.[Le], Su, R.[Ran],
TreeDetector: Using Deep Learning for the Localization and Reconstruction of Urban Trees from High-Resolution Remote Sensing Images,
RS(16), No. 3, 2024, pp. 524.
DOI Link 2402

Yang, Y.N.[Yi-Ning], Shen, X.[Xin], Cao, L.[Lin],
Estimation of the Living Vegetation Volume (LVV) for Individual Urban Street Trees Based on Vehicle-Mounted LiDAR Data,
RS(16), No. 10, 2024, pp. 1662.
DOI Link 2405

Balestra, M.[Mattia], Choudhury, M.A.M.[MD Abdul Mueed], Pierdicca, R.[Roberto], Chiappini, S.[Stefano], Marcheggiani, E.[Ernesto],
UAV-Spherical Data Fusion Approach to Estimate Individual Tree Carbon Stock for Urban Green Planning and Management,
RS(16), No. 12, 2024, pp. 2110.
DOI Link 2406

Zandler, H.[Harald], Samimi, C.[Cyrus],
Cooling Potential of Urban Tree Species during Extreme Heat and Drought: A Thermal Remote Sensing Assessment,
RS(16), No. 12, 2024, pp. 2059.
DOI Link 2406

Kyaw, T.Y.[Thu Ya], Alonzo, M.[Michael], Baker, M.E.[Matthew E.], Eisenman, S.W.[Sasha W.], Caplan, J.S.[Joshua S.],
Predicting Urban Trees' Functional Trait Responses to Heat Using Reflectance Spectroscopy,
RS(16), No. 13, 2024, pp. 2291.
DOI Link 2407

Wang, H.[Hexiang], Gong, F.Y.[Fang-Ying],
Quantifying City- and Street-Scale Urban Tree Phenology from Landsat-8, Sentinel-2, and PlanetScope Images: A Case Study in Downtown Beijing,
RS(16), No. 13, 2024, pp. 2351.
DOI Link 2407

Stuart, W.[William], Azad Hossain, A.K.M., Hunt, N.[Nyssa], Mix, C.[Charles], Qin, H.[Hong],
Spatiotemporal Analysis of Urban Forest in Chattanooga, Tennessee from 1984 to 2021 Using Landsat Satellite Imagery,
RS(16), No. 13, 2024, pp. 2419.
DOI Link 2407

Oliveira, A.A.A.M.[Artur André A. M.], Wang, Z.Y.[Zhang-Yang], Hirata, R.[Roberto],
Locating Urban Trees near Electric Wires using Google Street View Photos: A New Dataset and A Semi-Supervised Learning Approach in the Wild,
Training, Protocols, Wires, Neural networks, Vegetation mapping, Vegetation, Cost function BibRef

Hirt, P.R., Hoegner, L., Stilla, U.,
A Concept for the Segmentation of Individual Urban Trees From Dense Mls Point Clouds,
ISPRS21(B2-2021: 171-178).
DOI Link 2201

Schmohl, S.[Stefan], ölle, M.[Michael], Frolow, R.[Rudolf], Soergel, U.[Uwe],
Towards Urban Tree Recognition in Airborne Point Clouds with Deep 3d Single-shot Detectors,
PRRS20 (521-535).
Springer DOI 2103

Li, Y.Q., Liu, H.Y., Liu, Y.K., Zhao, S.B., Li, P.P., Xiao, W.,
Street Tree Information Extraction and Dynamics Analysis From Mobile Lidar Point Cloud,
DOI Link 2012

Alpan, K., Sekeroglu, B.,
Tree Inventory Registration System,
DOI Link 2012

Tokunaga, M.,
Extraction of Debilitated Trees Along the Road By Blocked NDVI,
DOI Link 2012

Fan, W., Yang, B., Liang, F., Dong, Z.,
Using Mobile Laser Scanning Point Clouds to Extract Urban Roadside Trees for Ecological Benefits Estimation,
DOI Link 2012

Dogon-Yaro, M.A., Kumar, P., Abdul Rahman, A., Buyuksalih, G.,
Semi-Automated Approach for Mapping Urban Trees from Integrated Aerial Lidar Point Cloud and Digital Imagery Datasets,
DOI Link 1612

Böhm, J., Bredif, M., Gierlinger, T., Krämer, M., Lindenberg, R., Liu, K., Michel, F., Sirmacek, B.,
The Iqmulus Urban Showcase: Automatic Tree Classification And Identification In Huge Mobile Mapping Point Clouds,
ISPRS16(B3: 301-307).
DOI Link 1610

Moradi, A., Satari, M., Momeni, M.,
Individual Tree Of Urban Forest Extraction From Very High Density Lidar Data,
ISPRS16(B3: 337-343).
DOI Link 1610

Lindenbergh, R.C., Berthold, D., Sirmacek, B., Herrero-Huerta, M., Wang, J., Ebersbach, D.,
Automated Large Scale Parameter Extraction of Road-Side Trees Sampled by a Laser Mobile Mapping System,
DOI Link 1602

Monnier, F., Vallet, B., Soheilian, B.,
Trees Detection From Laser Point Clouds Acquired In Dense Urban Areas By A Mobile Mapping System,
AnnalsPRS(I-3), No. 2012, pp. 245-250.
DOI Link 1209

Liberge, S.[Sterenn], Soheilian, B.[Bahman], Chehata, N.[Nesrine], Paparoditis, N.[Nicolas],
Extraction of vertical posts in 3D laser point clouds acquired in dense urban areas by a Mobile Mapping System,
PDF File. 1009

Kramer, H., Oldengarm, J.,
URBTREE: A Tree Growth Model for the Urban Environment,
PDF File. 1007

van der Sande, C.J.,
Automatic Object Recognition and Change Detection of Urban Trees,
PDF File. 1007

Ardila, J.P.[Juan Pablo], Tolpekin, V.A.[Valentyn A.], Bijker, W.[Wietske],
Context-Sensitive Extraction of Tree Crown Objects in Urban Areas Using VHR Satellite Images,
PDF File. 1007

Tolpekin, V.A.[Valentyn A.], Ardila, J.P.[Juan Pablo], Bijker, W.[Wietske],
Super-Resolution Mapping for Extraction of Urban Tree Crown Objects from VHR Satellite Images,
PDF File. 1007

Bijker, W.[Wietske], Ardila, J.P.[Juan Pablo], Tolpekin, V.A.[Valentyn A.],
Change Detection and Uncertainty in Fuzzy Tree Crown Objects in an Urban Environment,
PDF File. 1007

Yang, Y.[Yun], Lin, Y.[Ying],
A Novel Deformable Model for Urban Vegetation Detection Using LiDAR Data,

Huang, H.[Hai],
Terrestrial Image Based 3D Extraction of Urban Unfoliaged Trees of Different Branching Types,
ISPRS08(B3a: 253 ff).
PDF File. 0807

Huang, H.[Hai], Mayer, H.[Helmut],
Extraction of 3D Unfoliaged Trees from Image Sequences Via a Generative Statistical Approach,
Springer DOI 0709

Carlberg, M.[Matthew], Gao, P.R.[Pei-Ran], Chen, G.[George], Zakhor, A.[Avideh],
Classifying urban landscape in aerial LiDAR using 3D shape analysis,

Chen, G.[George], Zakhor, A.[Avideh],
2D tree detection in large urban landscapes using aerial LiDAR data,

Secord, J., Zakhor, A.,
Tree Detection in Aerial LiDar and Image Data,
Tree detection in LiDAR data,

Chapter on Cartography, Aerial Images, Buildings, Roads, Terrain, Forests, Trees, ATR continues in
Trees, Forest Canopy Analysis .

Last update:Jul 13, 2024 at 15:27:21