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One-Class Classification of Airborne LiDAR Data in Urban Areas Using a Presence and Background Learning Algorithm

Zurui Ao, Yanjun Su, Wenkai Li, Qinghua Guo, Jing Zhang
2017 Remote Sensing  
In this study, we investigated the possibility of using a novel one-class classification algorithm, i.e., the presence and background learning (PBL) algorithm, to classify LiDAR data in an urban scenario  ...  Recently, Li et al. (2011) [30] proposed a new presence and background learning (PBL) algorithm that has great advantage in one-class classification.  ...  Acknowledgments: We acknowledge the University of Maryland and the Sonoma County Vegetation Mapping and LiDAR Program for providing the LiDAR data.  ... 
doi:10.3390/rs9101001 fatcat:hvu2klsnijeqpgjy5d4d66hd2i

Mapping of Urban Vegetation with High-Resolution Remote Sensing: A Review

Robbe Neyns, Frank Canters
2022 Remote Sensing  
Finally, unsupervised learning techniques and active learning, so far rarely applied in urban vegetation mapping, are also areas where significant progress can be expected.  ...  Based on a comprehensive survey of the literature, this review article provides an overview of the main approaches proposed to map urban vegetation from high-resolution remotely sensed data.  ...  Fusion of LiDAR Data and Spectral Imagery Combining spectral imagery with LiDAR has become a common strategy for highresolution vegetation mapping in urban areas.  ... 
doi:10.3390/rs14041031 fatcat:axd33lzzljh2fgoggchujmts6y


Z. Nordin, H. Z. M. Shafri, A. F. Abdullah, S. J. Hashim
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
coverage and weather independent images, which in turn provides faster turnaround times for creation of large area geospatial data.  ...  Deep learning is a subset of the machine learning algorithm. Recently, Deep Learning has been proposed to solve traditional artificial intelligent problems.  ...  ., & Rastiveis, (2017) conducted the research on an automatic urban objects extraction using airborne Remote Sensing data to process and efficiently interpret the vast amount of airborne imagery and LIDAR  ... 
doi:10.5194/isprs-archives-xlii-4-w16-489-2019 fatcat:xhlrmiru5reo5fbqkhafxucl6a


B. Aissou, A. Belhadj Aissa
2020 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
The ALS data used is characterized by a low density (4–6 points/m2), and is covering an urban area, located in residential parts of Vaihingen city in southern Germany.  ...  Light Detection And Ranging (LiDAR) is an active remote sensing technology used for several applications.  ...  ACKNOWLEDGMENTS Authors aim to thank the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) (Cramer, 2010) for the furnished Vaihingen city data.  ... 
doi:10.5194/isprs-archives-xliii-b2-2020-191-2020 fatcat:rxzaw5q5wngzzjgxvzz2xwqrdu

Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective

Ruiliang Pu
2021 Journal of Remote Sensing  
They include the following: (1) A large group of studies on the topic were using high-resolution satellite, airborne multi-/hyperspectral imagery, and airborne LiDAR data. (2) A trend of "multiple" method  ...  The review will provide an overview on various optical and light detection and ranging (LiDAR) sensors; present and assess current various techniques/methods for, and a general trend of method development  ...  Acknowledgments This work was supported by the University of South Florida, USA.  ... 
doi:10.34133/2021/9812624 fatcat:f3lmtkaaejepbif4j5lhcllz4u


E. Widyaningrum, B. G. H. Gorte
2017 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
Considering further needs of spatial data sharing and integration, the one stop processing of LiDAR data in a GIS environment is considered a powerful and efficient approach for the base map provision.  ...  LiDAR data acquisition is recognized as one of the fastest solutions to provide basis data for large-scale topographical base maps worldwide.  ...  ACKNOWLEDGEMENTS The authors appreciate for the data support by the Indonesian Geospatial Information Agency (BIG) as well as the GIS software support by the QCoherent (for LP360) and ESRI (ArcGIS, ArcPro  ... 
doi:10.5194/isprs-archives-xlii-1-w1-365-2017 fatcat:r2ta5qrszrhitjve6woeoouniq

Urban tree health assessment using airborne hyperspectral and LiDAR imagery

J. Degerickx, D.A. Roberts, J.P. McFadden, M. Hermy, B. Somers
2018 International Journal of Applied Earth Observation and Geoinformation  
In addition, the authors would like to thank dr. Mike Alonzo for his guidance on the use of LiDAR data and his valuable suggestions. Special thanks also to prof.  ...  for providing us insights in the needs of urban green managers.  ...  LAI from LiDAR As an alternative to the use of spectral data, many studies have focused on extracting LAI from airborne LiDAR data via laser penetration metrics, including in urban areas (Alonzo et al  ... 
doi:10.1016/j.jag.2018.05.021 fatcat:d5w4vvoarrep3c2gjl6ahc5jfm

Airborne LiDAR for Detection and Characterization of Urban Objects and Traffic Dynamics [chapter]

Wei Yao, Jianwei Wu
2021 The Urban Book Series  
AbstractIn this chapter, we present an advanced machine learning strategy to detect objects and characterize traffic dynamics in complex urban areas by airborne LiDAR.  ...  from airborne LiDAR data based on selected features was validated and achieved.  ...  The experimental data set over Vaihingen for urban objects detection was provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation (DGPF) (Cramer 2010) :  ... 
doi:10.1007/978-981-15-8983-6_22 fatcat:xq7xezfndndjhkwutuvc2fimba

Tree Annotations in LiDAR Data Using Point Densities and Convolutional Neural Networks

Ananya Gupta, Jonathan Byrne, David Moloney, Simon Watson, Hujun Yin
2019 IEEE Transactions on Geoscience and Remote Sensing  
Manual annotation of such data is time consuming, tedious and error prone, and hence in this paper we present three automatic methods for annotating trees in LiDAR data.  ...  The second method uses a voxel-based 3D Convolutional Neural Network on low density LiDAR datasets and is able to identify most large trees accurately but struggles with smaller ones due to the voxelisation  ...  Index Terms-Deep Learning, Airborne LiDAR, Urban Areas, Tree Segmentation, Voxelization I.  ... 
doi:10.1109/tgrs.2019.2942201 fatcat:sbwb4enm3nf5riin2te2buonlq

Urban Tree Species Classification Using a WorldView-2/3 and LiDAR Data Fusion Approach and Deep Learning

Sean Hartling, Vasit Sagan, Paheding Sidike, Maitiniyazi Maimaitijiang, Joshua Carron
2019 Sensors  
Urban areas feature complex and heterogeneous land covers which create challenging issues for tree species classification.  ...  The objective of this study was to examine the potential of a novel deep learning method, Dense Convolutional Network (DenseNet), to identify dominant individual tree species in a complex urban environment  ...  Acknowledgments: Worldview multispectral imagery was obtained through a grant from the Digital Globe Foundation. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s19061284 fatcat:pnmjni3hhbdypmbf5ntwmie7ki


J. C. Trinder, M. Salah
2012 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In order to detect and evaluate changes in buildings, LiDAR-derived DEMs and aerial images from two epochs were used, showing changes in urban buildings due to construction and demolition.  ...  Potential applications of airborne LiDAR for disaster monitoring include flood prediction and assessment, monitoring of the growth of volcanoes and assistance in the prediction of eruptions, assessment  ...  ACKNOWLEDGEMENTS The authors wish to thank NSW Department of Land and Property Information for the LiDAR and image data.  ... 
doi:10.5194/isprsannals-i-4-227-2012 fatcat:bmq5o4do55gv3cwta5emasnpaq

Classification of Expansive Grassland Species in Different Growth Stages Based on Hyperspectral and LiDAR Data

Adriana Marcinkowska-Ochtyra, Anna Jarocińska, Katarzyna Bzdęga, Barbara Tokarska-Guzik
2018 Remote Sensing  
Classifications were performed using a Random Forest algorithm.  ...  Field work was carried out during late spring, summer and early autumn, in parallel with remote sensing data acquisition. Airborne 1-m resolution HySpex images and LiDAR data were used.  ...  of Life Sciences and University of Lodz for joining us in defining the concept of species classification as part of the WP6 task, to Warsaw University of Technology for processing of LiDAR data, to Adam  ... 
doi:10.3390/rs10122019 fatcat:oufqu565jvhbjitm6ueqzibvqa

Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends

Qihao Weng
2012 Remote Sensing of Environment  
Therefore, remote sensing of impervious surfaces in the urban areas has recently attracted unprecedented attention.  ...  The advent of high spatial resolution satellite images, spaceborne hyperspectral images, and LiDAR data is stimulating new research idea, and is driving the future research trends with new models and algorithms  ...  Xuefei Hu, Dengsheng Lu, and Mrs. Jing Han for their assistance in research, which contributes to this review. Dr.  ... 
doi:10.1016/j.rse.2011.02.030 fatcat:x3pcns7lcfebvcyltt2ungfd7y

2014 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 7

2014 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., Bronstert, A., and Foerster, S  ...  ., +, JSTARS April 2014 1314-1330 A Two-Phase Classification of Urban Vegetation Using Airborne LiDAR Data and Aerial Photography.  ...  ., +, JSTARS Oct. 2014 4255-4265 A Two-Phase Classification of Urban Vegetation Using Airborne LiDAR Data and Aerial Photography.  ... 
doi:10.1109/jstars.2015.2397347 fatcat:ib3tjwsjsnd6ri6kkklq5ov37a

Categorizing Grassland Vegetation with Full-Waveform Airborne Laser Scanning: A Feasibility Study for Detecting Natura 2000 Habitat Types

András Zlinszky, Anke Schroiff, Adam Kania, Balázs Deák, Werner Mücke, Ágnes Vári, Balázs Székely, Norbert Pfeifer
2014 Remote Sensing  
Random forest machine learning was used for classifying this dataset. Habitat type, dominant plant species and other features of interest were noted in a set of 140 field plots.  ...  Two sets of categories were used: five classes focusing on meadow identification and the location of lowland hay meadows, and 10 classes, including eight different grassland vegetation categories.  ...  Acknowledgements The studies carried out and the data used in this study have been acquired within the ChangeHabitats2 project, an IAPP Marie Curie project of the Seventh Framework Programme of the European  ... 
doi:10.3390/rs6098056 fatcat:wynvvrkilzgltjosfcxn3ynwyq
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