Wavelet based buildings segmentation in airborne laser scanning data set

Vegetation mapping in urban environments plays an important role in biological research and urban management. Airborne laser scanning provides detailed 3D geodata, which allows to classify single trees into different taxa. Until now, research dealing with tree classification focused on forest environments. This study investigates the object-based classification of urban trees at taxonomic family level, using full-waveform airborne laser scanning data captured in the city centre of Vienna (Austria). The data set is characterised by a variety of taxa, including deciduous trees (beeches, mallows, plane trees and soapberries) and the coniferous pine species. A workflow for tree object classification is presented using geometric and radiometric features. The derived features are related to point density, crown shape and radiometric characteristics. For the derivation of crown features, a prior detection of the crown base is performed. The effects of interfering objects (e.g. fences and cars which are typical in urban areas) on the feature characteristics and the subsequent classification accuracy are investigated. The applicability of the features is evaluated by Random Forest classification and exploratory analysis. The most reliable classification is achieved by using the combination of geometric and radiometric features, resulting in 87.5% overall accuracy. By using radiometric features only, a reliable classification with accuracy of 86.3% can be achieved. The influence of interfering objects on feature characteristics is identified, in particular for the radiometric features. The results indicate the potential of using radiometric features in urban tree classification and show its limitations due to anthropogenic influences at the same time.

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EcoDes-DK15: High-resolution ecological descriptors of vegetation and terrain derived from Denmark's national airborne laser scanning data set

10.5194/essd-2021-222 ◽

2021 ◽

Author(s):

Jakob J. Assmann ◽

Jesper E. Moeslund ◽

Urs A. Treier ◽

Signe Normand

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Laser Scanning ◽

Three Dimensional ◽

Airborne Laser Scanning ◽

Data Sets ◽

Ecosystem Structure ◽

Data Set ◽

Vegetation Height ◽

Airborne Laser

Abstract. Biodiversity studies could strongly benefit from three-dimensional data on ecosystem structure derived from contemporary remote sensing technologies, such as Light Detection and Ranging (LiDAR). Despite the increasing availability of such data at regional and national scales, the average ecologist has been limited in accessing them due to high requirements on computing power and remote-sensing knowledge. We processed Denmark's publicly available national Airborne Laser Scanning (ALS) data set acquired in 2014/15 together with the accompanying elevation model to compute 70 rasterized descriptors of interest for ecological studies. With a grain size of 10 m, these data products provide a snapshot of high-resolution measures including vegetation height, structure and density, as well as topographic descriptors including elevation, aspect, slope and wetness across more than forty thousand square kilometres covering almost all of Denmark's terrestrial surface. The resulting data set is comparatively small (~ 87 GB, compressed 16.4 GB) and the raster data can be readily integrated into analytical workflows in software familiar to many ecologists (GIS software, R, Python). Source code and documentation for the processing workflow are openly available via a code repository, allowing for transfer to other ALS data sets, as well as modification or re-calculation of future instances of Denmark’s national ALS data set. We hope that our high-resolution ecological vegetation and terrain descriptors (EcoDes-DK15) will serve as an inspiration for the publication of further such data sets covering other countries and regions and that our rasterized data set will provide a baseline of the ecosystem structure for current and future studies of biodiversity, within Denmark and beyond.

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URBAN TREE CLASSIFICATION USING FULL-WAVEFORM AIRBORNE LASER SCANNING

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iii-3-185-2016 ◽

2016 ◽

Vol III-3 ◽

pp. 185-192

Author(s):

Zs. Koma ◽

K. Koenig ◽

B. Höfle

Keyword(s):

Laser Scanning ◽

Airborne Laser Scanning ◽

City Centre ◽

Biological Research ◽

Data Set ◽

Urban Tree ◽

Full Waveform ◽

Airborne Laser ◽

Crown Shape ◽

Reliable Classification

Vegetation mapping in urban environments plays an important role in biological research and urban management. Airborne laser scanning provides detailed 3D geodata, which allows to classify single trees into different taxa. Until now, research dealing with tree classification focused on forest environments. This study investigates the object-based classification of urban trees at taxonomic family level, using full-waveform airborne laser scanning data captured in the city centre of Vienna (Austria). The data set is characterised by a variety of taxa, including deciduous trees (beeches, mallows, plane trees and soapberries) and the coniferous pine species. A workflow for tree object classification is presented using geometric and radiometric features. The derived features are related to point density, crown shape and radiometric characteristics. For the derivation of crown features, a prior detection of the crown base is performed. The effects of interfering objects (e.g. fences and cars which are typical in urban areas) on the feature characteristics and the subsequent classification accuracy are investigated. The applicability of the features is evaluated by Random Forest classification and exploratory analysis. The most reliable classification is achieved by using the combination of geometric and radiometric features, resulting in 87.5% overall accuracy. By using radiometric features only, a reliable classification with accuracy of 86.3% can be achieved. The influence of interfering objects on feature characteristics is identified, in particular for the radiometric features. The results indicate the potential of using radiometric features in urban tree classification and show its limitations due to anthropogenic influences at the same time.

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Reﬁning and evaluating a Horvitz– Thompson-like stand density estimator in individual tree detection based on airborne laser scanning

Canadian Journal of Forest Research ◽

10.1139/cjfr-2021-0123 ◽

2021 ◽

Author(s):

Kasper Kansanen ◽

Petteri Packalen ◽

Timo Lähivaara ◽

Aku Seppänen ◽

Jari Vauhkonen ◽

...

Keyword(s):

Laser Scanning ◽

Stand Density ◽

Airborne Laser Scanning ◽

Tuning Parameter ◽

Density Estimator ◽

Validation Data ◽

Data Set ◽

Individual Tree ◽

Tree Detection ◽

Airborne Laser

Horvitz--Thompson-like stand density estimation is a method for estimating the stand density from tree crown objects extracted from airborne laser scanning data through individual tree detection. The estimator is based on stochastic geometry and mathematical morphology of the (planar) set formed by the detected tree crowns. This set is used to approximate the detection probabilities of trees. These probabilities are then used to calculate the estimate. The method includes a tuning parameter, which needs to be known to apply the method. We present a refinement of the method to allow more general detection conditions than the previous papers and present and discuss the methods for estimating the tuning parameter of the estimator using a functional $k$-nearest neighbors method. We test the model fitting and prediction in two spatially separate data sets and examine the plot-level accuracy of estimation. The estimator produced a $13$\% lower RMSE than the benchmark method in an external validation data set. We also analyze the effects of similarity and dissimilarity of training and validation data to the results.

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COMPARISON OF SUBAQUATIC DIGITAL ELEVATION MODELS FROM AIRBORNE LASER SCANNING AND IMAGERY

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-v-2-2020-671-2020 ◽

2020 ◽

Vol V-2-2020 ◽

pp. 671-677

Author(s):

C. Mulsow ◽

G. Mandlburger ◽

H.-G. Maas

Keyword(s):

Spatial Resolution ◽

Laser Scanning ◽

Digital Elevation Models ◽

Image Data ◽

Airborne Laser Scanning ◽

Stereo Images ◽

Artificial Lake ◽

Data Set ◽

Airborne Laser ◽

Digital Elevation

Abstract. The paper describes and compares the workflows and results of generating digital elevation models (DEMs) of underwater areas from airborne laser scanning and aerial stereo images. Based on a combined laser scanning/image data set of an artificial lake, both methods are described and pros/cons are highlighted. The authors focus on the final results, especially on accuracy, completeness and spatial resolution of the underwater DEM’s. Further, practical aspects of processing and complexity of both methods are highlighted too.

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Detecting Building Changes between Airborne Laser Scanning and Photogrammetric Data

Remote Sensing ◽

10.3390/rs11202417 ◽

2019 ◽

Vol 11 (20) ◽

pp. 2417 ◽

Cited By ~ 7

Author(s):

Zhenchao Zhang ◽

George Vosselman ◽

Markus Gerke ◽

Claudio Persello ◽

Devis Tuia ◽

...

Keyword(s):

Change Detection ◽

Laser Scanning ◽

Remote Sensing Data ◽

Point Clouds ◽

Recall Rate ◽

Airborne Laser Scanning ◽

Data Set ◽

Multimodal Data ◽

Airborne Laser ◽

Dense Matching

Detecting topographic changes in an urban environment and keeping city-level point clouds up-to-date are important tasks for urban planning and monitoring. In practice, remote sensing data are often available only in different modalities for two epochs. Change detection between airborne laser scanning data and photogrammetric data is challenging due to the multi-modality of the input data and dense matching errors. This paper proposes a method to detect building changes between multimodal acquisitions. The multimodal inputs are converted and fed into a light-weighted pseudo-Siamese convolutional neural network (PSI-CNN) for change detection. Different network configurations and fusion strategies are compared. Our experiments on a large urban data set demonstrate the effectiveness of the proposed method. Our change map achieves a recall rate of 86.17%, a precision rate of 68.16%, and an F1-score of 76.13%. The comparison between Siamese architecture and feed-forward architecture brings many interesting findings and suggestions to the design of networks for multimodal data processing.

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Combining tree height samples produced by airborne laser scanning and stand management records to estimate plot volume in Eucalyptus plantations

Canadian Journal of Forest Research ◽

10.1139/x11-083 ◽

2011 ◽

Vol 41 (8) ◽

pp. 1649-1658 ◽

Cited By ~ 18

Author(s):

Jari Vauhkonen ◽

Lauri MehtÄtalo ◽

Petteri Packalén

Keyword(s):

Laser Scanning ◽

Tree Height ◽

Small Sample ◽

Airborne Laser Scanning ◽

Tree Level ◽

Stem Volume ◽

Data Set ◽

Stand Management ◽

Tree Detection ◽

Airborne Laser

Regular stand structure and availability of precise silvicultural management data produce a special situation regarding remote sensing based assessments of plantation forests. This study tested the use of stand management records to improve single-tree detection in a Eucalyptus plantation. Combined airborne laser scanning (ALS) and planting distance data were used to detect trees and extract their heights. The extracted heights were used as an input for volume estimation using both existing plot-level functions and new tree-level models. The accuracies were evaluated in a test data set of 191 field reference plots in which the diameters of the Eucalyptus urograndis (E. grandis (Hill) Maiden × E. urophylla S.T. Blake) trees varied from 6 to 41 cm and tree heights varied from 12 to 41 m. The constructed mixed-effects model that predicted stem volume from tree height resulted in a root mean squared error (RMSE) of 68 dm3 (15%) in a cross validation of the modeling data. The tree detection produced estimates of stem number with low bias (i.e., average difference between measured and estimated) and an RMSE of 6% of the mean, whereas plot-level mean and dominant heights were estimated with RMSEs of 1.5 m (5%) and 2 m (6%), respectively, using ALS data alone. The difference of about 60 cm observed between the ALS-based and field-measured dominant height was most likely caused by the penetration of the laser pulses through the canopy. A system of plot-level models that employed a small sample of calibration field data gave RMSEs of 1 m (3%) and 2.2 m2/ha (9%) for site index and basal area, respectively. The plot volume was estimated with an RMSE of 44 m3/ha (12%) at best. A similar residual variation was observed in the volume estimates of an area-based method applied to the same data set. The combined results suggest the feasibility of the proposed methodology in a plantation inventory using ALS data with a density of only 1.5 pulses/m2.

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A BIG DATA APPROACH FOR COMPREHENSIVE URBAN SHADOW ANALYSIS FROM AIRBORNE LASER SCANNING POINT CLOUDS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-w8-131-2019 ◽

2019 ◽

Vol IV-4/W8 ◽

pp. 131-137

Author(s):

A. V. Vo ◽

D. F. Laefer

Keyword(s):

Point Cloud ◽

Input Data ◽

Laser Scanning ◽

Point Clouds ◽

Airborne Laser Scanning ◽

Data Set ◽

Cloud Data ◽

Airborne Laser ◽

Alternative Approach ◽

Hadoop Cluster

<p><strong>Abstract.</strong> Because of the importance of access to sunlight, shadow analysis is a common consideration in urban design, especially for dense urban developments. As shadow computation is computationally expensive, most urban shadow analysis tools have to date circumvented the high computational costs by representing urban complexity only through simplified geometric models. The simplification process removes details and adversely affects the level of realism of the ultimate results. In this paper, an alternative approach is presented by utilizing the highest level of detail and resolution captured in the geometric input data source, which is an extremely high-resolution airborne laser scanning point cloud (300 points/m2). To cope with the high computational demand caused by the use of this dense and detailed input data set, the Comprehensive Urban Shadow algorithm is introduced to distribute the computation for parallel processing on a Hadoop cluster. The proposed comprehensive urban shadow analysis solution is scalable, reasonably fast, and capable of preserving the original resolution and geometric detail of the original point cloud data.</p>

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