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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
Li, L.[Lin],
Li, D.[Dalin],
Zhu, H.[Haihong],
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
BibRef
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
BibRef
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.
IEEE DOI
1612
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,
CVRS12(140-145).
IEEE DOI
1302
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.[Haiyan],
Zai, D.,
Wang, C.,
Automated Extraction of 3D Trees from Mobile LiDAR Point Clouds,
CloseRange14(629-632).
DOI Link
1411
BibRef
Louarn, M.L.[Marine Le],
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
BibRef
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
BibRef
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
BibRef
Hu, R.H.[Rong-Hai],
Bournez, E.[Elena],
Cheng, S.[Shiyu],
Jiang, H.[Hailan],
Nerry, F.[Françoise],
Landes, T.[Tania],
Saudreau, M.[Marc],
Kastendeuch, P.[Pierre],
Najjar, G.[Georges],
Colin, J.[Jérôme],
Yan, G.[Guangjian],
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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.
IEEE DOI
2001
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
BibRef
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
BibRef
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,
CVPR16(6014-6023)
IEEE DOI
1612
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
Man, Q.[Qixia],
Dong, P.[Pinliang],
Yang, X.[Xinming],
Wu, Q.[Quanyuan],
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
BibRef
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
BibRef
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,
GGT16(127-134).
DOI Link
1612
BibRef
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
BibRef
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
BibRef
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.
HTML Version.
1209
BibRef
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,
PCVIA10(B:126).
PDF File.
1009
BibRef
Kramer, H.,
Oldengarm, J.,
URBTREE: A Tree Growth Model for the Urban Environment,
GEOBIA10(xx-yy).
PDF File.
1007
BibRef
van der Sande, C.J.,
Automatic Object Recognition and Change Detection of Urban Trees,
GEOBIA10(xx-yy).
PDF File.
1007
BibRef
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,
GEOBIA10(xx-yy).
PDF File.
1007
BibRef
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,
GEOBIA10(xx-yy).
PDF File.
1007
BibRef
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,
GEOBIA10(xx-yy).
PDF File.
1007
BibRef
Yang, Y.[Yun],
Lin, Y.[Ying],
A Novel Deformable Model for Urban Vegetation Detection Using LiDAR
Data,
CISP09(1-5).
IEEE DOI
0910
BibRef
Huang, H.[Hai],
Terrestrial Image Based 3D Extraction of Urban Unfoliaged Trees of
Different Branching Types,
ISPRS08(B3a: 253 ff).
PDF File.
0807
BibRef
Huang, H.[Hai],
Mayer, H.[Helmut],
Extraction of 3D Unfoliaged Trees from Image Sequences Via a Generative
Statistical Approach,
DAGM07(385-394).
Springer DOI
0709
BibRef
Carlberg, M.[Matthew],
Gao, P.R.[Pei-Ran],
Chen, G.[George],
Zakhor, A.[Avideh],
Classifying urban landscape in aerial LiDAR using 3D shape analysis,
ICIP09(1701-1704).
IEEE DOI
0911
BibRef
Chen, G.[George],
Zakhor, A.[Avideh],
2D tree detection in large urban landscapes using aerial LiDAR data,
ICIP09(1693-1696).
IEEE DOI
0911
BibRef
Secord, J.,
Zakhor, A.,
Tree Detection in Aerial LiDar and Image Data,
ICIP06(2317-2320).
IEEE DOI
0610
BibRef
And:
Tree detection in LiDAR data,
Southwest06(86-90).
IEEE DOI
0603
BibRef
Chapter on Remote Sensing, Cartography, Aerial Images, Buildings, Roads, Terrain, ATR continues in
Trees, Forest Canopy Analysis .