Characterization of environmental loads related concrete pavement deflection behavior using Light Detection and Ranging technology

Lane markings are one of the essential elements of road information, which is useful for a wide range of transportation applications. Several studies have been conducted to extract lane markings through intensity thresholding of Light Detection and Ranging (LiDAR) point clouds acquired by mobile mapping systems (MMS). This paper proposes an intensity thresholding strategy using unsupervised intensity normalization and a deep learning strategy using automatically labeled training data for lane marking extraction. For comparative evaluation, original intensity thresholding and deep learning using manually established labels strategies are also implemented. A pavement surface-based assessment of lane marking extraction by the four strategies is conducted in asphalt and concrete pavement areas covered by MMS equipped with multiple LiDAR scanners. Additionally, the extracted lane markings are used for lane width estimation and reporting lane marking gaps along various highways. The normalized intensity thresholding leads to a better lane marking extraction with an F1-score of 78.9% in comparison to the original intensity thresholding with an F1-score of 72.3%. On the other hand, the deep learning model trained with automatically generated labels achieves a higher F1-score of 85.9% than the one trained on manually established labels with an F1-score of 75.1%. In concrete pavement area, the normalized intensity thresholding and both deep learning strategies obtain better lane marking extraction (i.e., lane markings along longer segments of the highway have been extracted) than the original intensity thresholding approach. For the lane width results, more estimates are observed, especially in areas with poor edge lane marking, using the two deep learning models when compared with the intensity thresholding strategies due to the higher recall rates for the former. The outcome of the proposed strategies is used to develop a framework for reporting lane marking gap regions, which can be subsequently visualized in RGB imagery to identify their cause.

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Characterization of vegetation structural changes using multi-temporal LiDAR and its relationship with severity in Calcena wildfire

Ecosistemas ◽

10.7818/ecos.2103 ◽

2021 ◽

Vol 30 (2) ◽

pp. 1-10

Author(s):

Dario Domingo ◽

Maria Teresa Lamelas ◽

Maria Begoña García

Keyword(s):

Structural Changes ◽

Light Detection And Ranging ◽

Light Detection ◽

Differenced Normalized Burn Ratio ◽

Landsat 7 ◽

Multi Temporal ◽

Normalized Burn Ratio

La caracterización de los cambios estructurales y presencia de huecos tras el fuego puede proporcionar información muy relevante para comprender los efectos ecológicos de los incendios en ecosistemas mediterráneos. En el presente estudio se caracterizan estas variables tras el incendio de Calcena en masas forestales de pinar y encinar, y su relación con la severidad del mismo. Dicho incendio calcinó 4.573 hectáreas en 2012 afectando de forma parcial al Parque Natural de la Dehesa del Moncayo localizado en Aragón (España). Para ello se hace uso de información multi-temporal Light Detection and Ranging (LiDAR) de las coberturas de 2011 y 2016 del Plan Nacional de Ortofotografía Aérea (PNOA), así como imágenes Landsat 7 para estimar la severidad del incendio mediante el índice differenced Normalized Burn Ratio (dNBR). Se evalúan los cambios estructurales producidos utilizando métricas LiDAR pre y post-incendio, así como la distribución de los huecos en el dosel forestal, su tamaño, número y frecuencia, y se analizan sus correlaciones con la severidad del incendio. La severidad fue predominantemente baja (42.32 %) o mediabaja (30.38 %), y produjo una disminución de la altura, de la densidad del dosel forestal y de la diversidad estructural. El tamaño de los huecos se incrementó tras el incendio, reduciéndose el número de huecos pequeños e incrementándose aquellos de tamaño intermedio en torno a 0.2 ha. Los cambios en las métricas LiDAR relacionadas con la altura, variabilidad de la altura en el perfil vertical, y densidad del dosel forestal presentaron las mayores correlaciones, indicando que son las que sufren mayores modificaciones. Los resultados muestran el interés de utilizar los datos LiDAR para caracterizar cambios estructurales y apoyar decisiones en la gestión silvícola.

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Wireless Sensor material Fusion based Single photon indium phosphide on silicon chip for frequency-modulated continuous-wave light detection and ranging

Journal of Physics Conference Series ◽

10.1088/1742-6596/1804/1/012184 ◽

2021 ◽

Vol 1804 (1) ◽

pp. 012184

Author(s):

R Selvakumar ◽

Ngangbam Phalguni Singh ◽

Shruti Suman

Keyword(s):

Indium Phosphide ◽

Continuous Wave ◽

Single Photon ◽

Light Detection And Ranging ◽

Wireless Sensor ◽

Light Detection ◽

Sensor Material ◽

Continuous Wave Light

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Using Airborne Light Detection and Ranging (LIDAR) to Characterize Forest Stand Condition on the Kenai Peninsula of Alaska

Western Journal of Applied Forestry ◽

10.1093/wjaf/24.2.95 ◽

2009 ◽

Vol 24 (2) ◽

pp. 95-102 ◽

Cited By ~ 13

Author(s):

Hans-Erik Andersen

Keyword(s):

Forest Inventory ◽

Laser Scanning ◽

Three Dimensional ◽

Grid Cell ◽

Forest Stand ◽

Light Detection And Ranging ◽

Inventory Analysis ◽

Light Detection ◽

Individual Tree ◽

Kenai Peninsula

Abstract Airborne laser scanning (also known as light detection and ranging or LIDAR) data were used to estimate three fundamental forest stand condition classes (forest stand size, land cover type, and canopy closure) at 32 Forest Inventory Analysis (FIA) plots distributed over the Kenai Peninsula of Alaska. Individual tree crown segment attributes (height, area, and species type) were derived from the three-dimensional LIDAR point cloud, LIDAR-based canopy height models, and LIDAR return intensity information. The LIDAR-based crown segment and canopy cover information was then used to estimate condition classes at each 10-m grid cell on a 300 × 300-m area surrounding each FIA plot. A quantitative comparison of the LIDAR- and field-based condition classifications at the subplot centers indicates that LIDAR has potential as a useful sampling tool in an operational forest inventory program.

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Precision and shortcomings of yaw error estimation using spinner-based light detection and ranging

Wind Energy ◽

10.1002/we.1492 ◽

2012 ◽

Vol 16 (3) ◽

pp. 353-366 ◽

Cited By ~ 28

Author(s):

Knud A. Kragh ◽

Morten H. Hansen ◽

Torben Mikkelsen

Keyword(s):

Error Estimation ◽

Light Detection And Ranging ◽

Light Detection

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Hybrid Integrated Silicon NitridePolymer Optical Phased Array for Efficient Light Detection and Ranging

Journal of Lightwave Technology ◽

10.1109/jlt.2021.3070386 ◽

2021 ◽

pp. 1-1

Author(s):

Chul-Soon Im ◽

Sung-Moon Kim ◽

Kyeong-Pyo Lee ◽

Seong-Hyeon Ju ◽

Jung-Ho Hong ◽

...

Keyword(s):

Phased Array ◽

Light Detection And Ranging ◽

Light Detection ◽

Optical Phased Array

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Hardware-in-the-loop projector system for light detection and ranging sensor testing

Optical Engineering ◽

10.1117/1.oe.51.8.083609 ◽

2012 ◽

Vol 51 (8) ◽

pp. 083609-1 ◽

Cited By ~ 6

Author(s):

Hajin J. Kim ◽

Charles B. Naumann ◽

Michael C. Cornell

Keyword(s):

Light Detection And Ranging ◽

Hardware In The Loop ◽

Light Detection

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Integrating lidar and geophysical surveys at Falerii Novi and Falerii Veteres (Viterbo)

Papers of the British School at Rome ◽

10.1017/s0068246200000027 ◽

2009 ◽

Vol 77 ◽

pp. 1-27 ◽

Cited By ~ 1

Author(s):

Rachel Opitz

Keyword(s):

Sono State ◽

Light Detection And Ranging ◽

Light Detection ◽

Geophysical Surveys

La città romana di Falerii Novi e quella pre-romana di Falerii Veteres vengono riviste in questo articolo attraverso la combinazione di dati da ricognizione lidar (light detection and ranging) e geofisica. La ricognizione lidar fornisce per la prima volta infomiazioni dettagliate sui bordi topograficamente complessi di questi siti e ha permesso di identificare un certo numero di nuove strutture. Osservando tali strutture nel contesto dei dati topografici e geofisici, sono state esplorate le aree urbane periferiche sia come zone per movimento sia come facciate. Tramite questi esempi vengono considerati i potenziali contributi forniti dal lidar alla comprensione generale dell'urbanismo pre-romano e romano.

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