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Artykuły w czasopismach na temat „LiDAR; Classification; Modelling”

Autor: Grafiati

Data publikacji: 28 lipca 2024

Data aktualizacji: 31 lipca 2025

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1

Yastikli, N., and Z. Cetin. "CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 441–45. http://dx.doi.org/10.5194/isprs-archives-xli-b3-441-2016.

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LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recen

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Yastikli, N., and Z. Cetin. "CLASSIFICATION OF LiDAR DATA WITH POINT BASED CLASSIFICATION METHODS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 441–45. http://dx.doi.org/10.5194/isprsarchives-xli-b3-441-2016.

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LiDAR is one of the most effective systems for 3 dimensional (3D) data collection in wide areas. Nowadays, airborne LiDAR data is used frequently in various applications such as object extraction, 3D modelling, change detection and revision of maps with increasing point density and accuracy. The classification of the LiDAR points is the first step of LiDAR data processing chain and should be handled in proper way since the 3D city modelling, building extraction, DEM generation, etc. applications directly use the classified point clouds. The different classification methods can be seen in recen

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Yastikli, N., and Z. Cetin. "AUTOMATIC 3D BUILDING MODEL GENERATIONS WITH AIRBORNE LiDAR DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W4 (November 13, 2017): 411–14. http://dx.doi.org/10.5194/isprs-annals-iv-4-w4-411-2017.

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LiDAR systems become more and more popular because of the potential use for obtaining the point clouds of vegetation and man-made objects on the earth surface in an accurate and quick way. Nowadays, these airborne systems have been frequently used in wide range of applications such as DEM/DSM generation, topographic mapping, object extraction, vegetation mapping, 3 dimensional (3D) modelling and simulation, change detection, engineering works, revision of maps, coastal management and bathymetry. The 3D building model generation is the one of the most prominent applications of LiDAR system, whi

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Cassanelli, Davide, Stefano Cattini, Lorenzo Medici, Luca Ferrari, and Luigi Rovati. "A simple experimental method to estimate and benchmark automotive LIDARs performance in fog." Acta IMEKO 13, no. 4 (2024): 1–8. https://doi.org/10.21014/actaimeko.v13i4.1885.

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LiDARs hold promise for various automotive applications, but their performance in adverse weather conditions remains a severe limitation. Indeed, fog can compromise the ability to perform fundamental tasks such as detection, classification, and tracking. The success of these tasks depends on the quality of the data provided by the LiDAR, i.e., the point cloud, PC, and the algorithms used to analyse that PC. Some previous studies exploited large and sophisticated facilities filled with fog to analyse LiDARs in fog. However, such facilities are intrinsically highly complex and costly. To overcom

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Xu, Hong Gen, Ting Li, and Fang Wu. "Knowledge-Based Classification Method for Urban Area Objects Feature Extraction Based on LIDAR Points." Applied Mechanics and Materials 128-129 (October 2011): 1157–62. http://dx.doi.org/10.4028/www.scientific.net/amm.128-129.1157.

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Laser scanning technology can quickly capture a large area of high-precise 3D spatial data, and get the information of buildings, roads, vegetation and other urban objects from raw data. Based on this information general frame of these objects can be modelling. In this paper, an object-based classification method is proposed for urban objects based on LIDAR points: determine the contents of the objects contained in the scene; extract inherent features of different objects; establish objects feature knowledge database; combine and compare objects’ features and distribution of LIDAR points; deri

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El-Ashmawy, N., and A. Shaker. "Raster Vs. Point Cloud LiDAR Data Classification." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7 (September 19, 2014): 79–83. http://dx.doi.org/10.5194/isprsarchives-xl-7-79-2014.

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Airborne Laser Scanning systems with light detection and ranging (LiDAR) technology is one of the fast and accurate 3D point data acquisition techniques. Generating accurate digital terrain and/or surface models (DTM/DSM) is the main application of collecting LiDAR range data. Recently, LiDAR range and intensity data have been used for land cover classification applications. Data range and Intensity, (strength of the backscattered signals measured by the LiDAR systems), are affected by the flying height, the ground elevation, scanning angle and the physical characteristics of the objects surfa

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Muckenhuber, Stefan, Hannes Holzer, and Zrinka Bockaj. "Automotive Lidar Modelling Approach Based on Material Properties and Lidar Capabilities." Sensors 20, no. 11 (2020): 3309. http://dx.doi.org/10.3390/s20113309.

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Development and validation of reliable environment perception systems for automated driving functions requires the extension of conventional physical test drives with simulations in virtual test environments. In such a virtual test environment, a perception sensor is replaced by a sensor model. A major challenge for state-of-the-art sensor models is to represent the large variety of material properties of the surrounding objects in a realistic manner. Since lidar sensors are considered to play an essential role for upcoming automated vehicles, this paper presents a new lidar modelling approach

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El-Ashmawy, N., and A. Shaker. "COMBINED MULTIPLE CLASSIFIED DATASETS CLASSIFICATION APPROACH FOR POINT CLOUD LIDAR DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5 (May 29, 2019): 349–56. http://dx.doi.org/10.5194/isprs-annals-iv-2-w5-349-2019.

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<p><strong>Abstract.</strong> Airborne Laser scanners using the Light Detection And Ranging (LiDAR) technology is a powerful tool for 3D data acquisition that records the backscattered energy as well. LiDAR has been successfully used in various applications including 3D modelling, feature extraction, and land cover information extraction. Airborne LiDAR data are usually acquired from different flight trajectories producing data in different strips with significant overlapped areas. Combining these data is required to get benefit of the multiple strips’ data that acquired from

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Homainejad, N., S. Zlatanova, and N. Pfeifer. "A VOXEL-BASED METHOD FOR THE THREE-DIMENSIONAL MODELLING OF HEATHLAND FROM LIDAR POINT CLOUDS: FIRST RESULTS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-3-2022 (May 17, 2022): 697–704. http://dx.doi.org/10.5194/isprs-annals-v-3-2022-697-2022.

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Abstract. Bushfires are an intrinsic part of the New South Wales’ (NSW) environment in Australia, especially in the Blue Mountains region (11400km2), that is dominated by fire prone vegetation that includes heathland. Many of the Australian native plants in this region are fire-prone and combustible, and many species even require fire to regenerate. The classification of the lateral and vertical distribution of living vegetation is necessary to manage the complexity of bushfires. Currently, interpretation of aerial and satellite images is the prevalent method for the classification of vegetati

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Bellakaout, A., M. Cherkaoui, M. Ettarid, and A. Touzani. "Automatic 3D Extraction of Buildings, Vegetation and Roads from LIDAR Data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 173–80. http://dx.doi.org/10.5194/isprs-archives-xli-b3-173-2016.

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Aerial topographic surveys using Light Detection and Ranging (LiDAR) technology collect dense and accurate information from the surface or terrain; it is becoming one of the important tools in the geosciences for studying objects and earth surface. Classification of Lidar data for extracting ground, vegetation, and buildings is a very important step needed in numerous applications such as 3D city modelling, extraction of different derived data for geographical information systems (GIS), mapping, navigation, etc... Regardless of what the scan data will be used for, an automatic process is great

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Liu, Shunan, Zhengwei Cao, Jiajia Liu, Chunan Lv, and Guoqiang Zhong. "Fusion of Multiple Basic Element Features for Airborne LiDAR in-house Surveys." Journal of Combinatorial Mathematics and Combinatorial Computing 120, no. 1 (2024): 03–16. http://dx.doi.org/10.61091/jcmcc120-01.

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When using airborne LiDAR point clouds for city modelling and road extraction, point cloud classification is a crucial step. There are numerous ways for classifying point clouds, but there are still issues like redundant multi-dimensional feature vector data and poor point cloud classification in intricate situations. A point cloud classification method built on the fusing of multikernel feature vectors is suggested as a solution to these issues. The technique employs random forest to classify point cloud data by merging colour information, and it extracts feature vectors based on point primit

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Vicari, Matheus B., Mathias Disney, Phil Wilkes, Andrew Burt, Kim Calders, and William Woodgate. "Leaf and wood classification framework for terrestrial LiDAR point clouds." Methods in Ecology and Evolution 10, no. 5 (2019): 680–94. http://dx.doi.org/10.1111/2041-210x.13144.

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Govedarica, Miro, Gordana Jakovljevic, Igor Ruskoviski, and Vladimir Pajic. "UNS Geo: LiDAR Dataset for point cloud classification in urban areas." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W13-2025 (July 11, 2025): 135–41. https://doi.org/10.5194/isprs-archives-xlviii-4-w13-2025-135-2025.

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Abstract. The classification of the urban point cloud is an essential task for numerous applications, including mapping, 3D urban modelling, etc.. Although in the last few years, different methodologies and algorithms have been proposed, precise and detailed point cloud labelling is still challenging. Publicly available annotated benchmark datasets have become the standard for the evaluation of algorithms' performance; however, most focus on data acquired from mobile or terrestrial laser scanners. In this paper, we introduce UNS Geo, a dense Aerial Laser Scanning (ALS) point cloud dataset cons

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Spadavecchia, C., M. B. Campos, M. Piras, E. Puttonen, and A. Shcherbacheva. "WOOD-LEAF UNSUPERVISED CLASSIFICATION OF SILVER BIRCH TREES FOR BIOMASS ASSESSMENT USING OBLIQUE POINT CLOUDS." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 14, 2023): 1795–802. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-1795-2023.

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Abstract. Forests play a fundamental role in carbon stocking since about a third of the carbon dioxide produced by activities of human origin is absorbed by forests. Forest biomass is an essential indicator of carbon dioxide absorption, enabling an understanding the interaction between forest dynamics and climate change effects. However, biomass and wood material changes are challenging to quantify in forest stands. Nowadays, recent 3D remote sensing technologies, such as laser scanning systems, have allowed accurate measures of single trees. This study evaluates three approaches to classify w

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Bellakaout, A., M. Cherkaoui, M. Ettarid, and A. Touzani. "Automatic 3D Extraction of Buildings, Vegetation and Roads from LIDAR Data." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B3 (June 9, 2016): 173–80. http://dx.doi.org/10.5194/isprsarchives-xli-b3-173-2016.

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Aerial topographic surveys using Light Detection and Ranging (LiDAR) technology collect dense and accurate information from the surface or terrain; it is becoming one of the important tools in the geosciences for studying objects and earth surface. Classification of Lidar data for extracting ground, vegetation, and buildings is a very important step needed in numerous applications such as 3D city modelling, extraction of different derived data for geographical information systems (GIS), mapping, navigation, etc... Regardless of what the scan data will be used for, an automatic process is great

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Razli, Mohamad Rashidi bin, Ahmad Daniel bin Razali, Nik Muhammad Rusyairie bin N. Ruhadi, et al. "LiDAR’s role in advancing geological modelling and enhancing geotechnical design efficiency." IOP Conference Series: Earth and Environmental Science 1412, no. 1 (2024): 012013. https://doi.org/10.1088/1755-1315/1412/1/012013.

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Abstract In Malaysia’s tropical climate region, dense vegetation and intense weathering processes obstruct the identification of geological features critical for modelling and geotechnical design. This study examines the capabilities and limitations of LiDAR technology in capturing terrain information in tropical climate regions, addressing challenges such as canopy cover and atmospheric conditions. By employing low-altitude flight missions below 100 meters with sufficient overlap, one can acquired 2 cm Ground Sample Distance (GSD) using UAV LiDAR. Manual and automated data classification was

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Botequim, Brigite, Paulo M. Fernandes, José G. Borges, Eduardo González-Ferreiro, and Juan Guerra-Hernández. "Improving silvicultural practices for Mediterranean forests through fire behaviour modelling using LiDAR-derived canopy fuel characteristics." International Journal of Wildland Fire 28, no. 11 (2019): 823. http://dx.doi.org/10.1071/wf19001.

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Wildfires cause substantial environmental and socioeconomic impacts and threaten many Spanish forested landscapes. We describe how LiDAR-derived canopy fuel characteristics and spatial fire simulation can be integrated with stand metrics to derive models describing fire behaviour. We assessed the potential use of very-low-density airborne LiDAR (light detection and ranging) data to estimate canopy fuel characteristics in south-western Spain Mediterranean forests. Forest type-specific equations were used to estimate canopy fuel attributes, namely stand height, canopy base height, fuel load, bul

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18

Andersen, Mikkel Skovgaard, Áron Gergely, Zyad Al-Hamdani, Frank Steinbacher, Laurids Rolighed Larsen, and Verner Brandbyge Ernstsen. "Processing and performance of topobathymetric lidar data for geomorphometric and morphological classification in a high-energy tidal environment." Hydrology and Earth System Sciences 21, no. 1 (2017): 43–63. http://dx.doi.org/10.5194/hess-21-43-2017.

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Abstract. The transition zone between land and water is difficult to map with conventional geophysical systems due to shallow water depth and often challenging environmental conditions. The emerging technology of airborne topobathymetric light detection and ranging (lidar) is capable of providing both topographic and bathymetric elevation information, using only a single green laser, resulting in a seamless coverage of the land–water transition zone. However, there is no transparent and reproducible method for processing green topobathymetric lidar data into a digital elevation model (DEM). Th

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Vivek Nanda, Vishnu Mahesh, Perver Baran, Laura Tateosian, Stacy A. C. Nelson, and Jianxin Hu. "Classification of tree forms in aerial LiDAR point clouds using CNN for 3D tree modelling." International Journal of Remote Sensing 44, no. 22 (2023): 7156–86. http://dx.doi.org/10.1080/01431161.2023.2282405.

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Nelson, Kailyn, Laura Chasmer, and Chris Hopkinson. "Quantifying Lidar Elevation Accuracy: Parameterization and Wavelength Selection for Optimal Ground Classifications Based on Time since Fire/Disturbance." Remote Sensing 14, no. 20 (2022): 5080. http://dx.doi.org/10.3390/rs14205080.

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Pre- and post-fire airborne lidar data provide an opportunity to determine peat combustion/loss across broad spatial extents. However, lidar measurements of ground surface elevation are prone to uncertainties. Errors may be introduced in several ways, particularly associated with the timing of data collection and the classification of ground points. Ground elevation data must be accurate and precise when estimating relatively small elevation changes due to combustion and subsequent carbon losses. This study identifies the impact of post-fire vegetation regeneration on ground classification par

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Wei, X., and X. Yao. "A Hybrid GWR-Based Height Estimation Method for Building Detection in Urban Environments." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-2 (November 11, 2014): 23–29. http://dx.doi.org/10.5194/isprsannals-ii-2-23-2014.

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LiDAR has become important data sources in urban modelling. Traditional methods of LiDAR data processing for building detection require high spatial resolution data and sophisticated methods. The aerial photos, on the other hand, provide continuous spectral information of buildings. But the segmentation of the aerial photos cannot distinguish between the road surfaces and the building roof. This paper develops a geographically weighted regression (GWR)-based method to identify buildings. The method integrates characteristics derived from the sparse LiDAR data and from aerial photos. In the GWR

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Song, Huacui, and Maqun Zhang. "HR-Mamba: Unified Multi-Stream Attentive Mamba for Hyperspectral-LiDAR Land Cover Classification." Journal of Physics: Conference Series 3055, no. 1 (2025): 012013. https://doi.org/10.1088/1742-6596/3055/1/012013.

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Abstract Aiming at the problems of large cross-modal feature differences, long range channel-dependent modelling difficulties, insufficient local semantic feature extraction, low feature utilisation, scarcity of training data, and insufficient model generalisation capability, this paper proposes the Unified Multi-Stream Attentive Mamba for Hyperspectral-LiDAR Land Cover Classification. In this paper, the Cross-Modal Attentive Mamba Fusion (CMAM-Fusion) module is introduced. This module enhances the ability to capture channel features, achieves adaptive weighted fusion of multimodal features, e

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Varela-González, M., B. Riveiro, P. Arias-Sánchez, H. González-Jorge, and J. Martínez-Sánchez. "A CityGML extension for traffic-sign objects that guides the automatic processing of data collected using Mobile Mapping technology." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1 (November 7, 2014): 415–20. http://dx.doi.org/10.5194/isprsarchives-xl-1-415-2014.

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The rapid evolution of integral schemes, accounting for geometric and semantic data, has been importantly motivated by the advances in the last decade in mobile laser scanning technology; automation in data processing has also recently influenced the expansion of the new model concepts. This paper reviews some important issues involved in the new paradigms of city 3D modelling: an interoperable schema for city 3D modelling (cityGML) and mobile mapping technology to provide the features that composing the city model. This paper focuses in traffic signs, discussing their characterization using c

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Zaki, N. A. M., Z. A. Latif, M. N. Suratman, and M. Z. Zainal. "MODELLING THE CARBON STOCKS ESTIMATION OF THE TROPICAL LOWLAND DIPTEROCARP FOREST USING LIDAR AND REMOTELY SENSED DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 187–94. http://dx.doi.org/10.5194/isprsannals-iii-7-187-2016.

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Tropical forest embraces a large stock of carbon in the global carbon cycle and contributes to the enormous amount of above and below ground biomass. The carbon kept in the aboveground living biomass of trees is typically the largest pool and the most directly impacted by the anthropogenic factor such as deforestation and forest degradation. However, fewer studies had been proposed to model the carbon for tropical rain forest and the quantification still remain uncertainties. A multiple linear regression (MLR) is one of the methods to define the relationship between the field inventory measure

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Zaki, N. A. M., Z. A. Latif, M. N. Suratman, and M. Z. Zainal. "MODELLING THE CARBON STOCKS ESTIMATION OF THE TROPICAL LOWLAND DIPTEROCARP FOREST USING LIDAR AND REMOTELY SENSED DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7 (June 7, 2016): 187–94. http://dx.doi.org/10.5194/isprs-annals-iii-7-187-2016.

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Tropical forest embraces a large stock of carbon in the global carbon cycle and contributes to the enormous amount of above and below ground biomass. The carbon kept in the aboveground living biomass of trees is typically the largest pool and the most directly impacted by the anthropogenic factor such as deforestation and forest degradation. However, fewer studies had been proposed to model the carbon for tropical rain forest and the quantification still remain uncertainties. A multiple linear regression (MLR) is one of the methods to define the relationship between the field inventory measure

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Kippers, R. G., L. Moth, and S. J. Oude Elberink. "AUTOMATIC MODELLING OF 3D TREES USING AERIAL LIDAR POINT CLOUD DATA AND DEEP LEARNING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2021 (June 28, 2021): 179–84. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2021-179-2021.

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Abstract. 3D tree objects can be used in various applications, like estimation of physiological equivalent temperature (PET). During this project, a method is designed to extract 3D tree objects from a country-wide point cloud. To apply this method on large scale, the algorithm needs to be efficient. Extraction of trees is done in two steps: point-wise classification using the PointNet deep learning network, and Watershed segmentation to split points into individual trees. After that, 3D tree models are made. The method is evaluated on 3 areas, a park, city center and housing block in the city

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Peykova, Ana, Dessislava Petrova-Antonova, and Kaloyan Karamitov. "Data Processing and Enrichment of LiDAR-Derived Traffic Data." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W5-2024 (June 27, 2024): 263–70. http://dx.doi.org/10.5194/isprs-annals-x-4-w5-2024-263-2024.

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Abstract. With the escalating demand for efficient traffic management and the increasing complexity of traffic control, diverse sensor technologies have been implemented to measure traffic in real-time. The road-side LiDAR emerges as a novel technology addressing the data gap in multimodal traffic analyses. LiDAR sensing return time to precisely capture distance and reflectivity, generating point cloud data encompassing all traffic trajectory information. It overcomes challenges posed by illumination conditions like light, dust and fog, which often affect camera sensor performance. In addition

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Singh, Minerva, Damian Evans, Jean-Baptiste Chevance, et al. "Evaluating remote sensing datasets and machine learning algorithms for mapping plantations and successional forests in Phnom Kulen National Park of Cambodia." PeerJ 7 (October 22, 2019): e7841. http://dx.doi.org/10.7717/peerj.7841.

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This study develops a modelling framework by utilizing multi-sensor imagery for classifying different forest and land use types in the Phnom Kulen National Park (PKNP) in Cambodia. Three remote sensing datasets (Landsat optical data, ALOS L-band data and LiDAR derived Canopy Height Model (CHM)) were used in conjunction with three different machine learning (ML) regression techniques (Support Vector Machines (SVM), Random Forests (RF) and Artificial Neural Networks (ANN)). These ML methods were implemented on (a) Landsat spectral data, (b) Landsat spectral band & ALOS backscatter data, and

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Magosi, Zoltan Ferenc, Hexuan Li, Philipp Rosenberger, Li Wan, and Arno Eichberger. "A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving." Sensors 22, no. 15 (2022): 5693. http://dx.doi.org/10.3390/s22155693.

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Radar sensors were among the first perceptual sensors used for automated driving. Although several other technologies such as lidar, camera, and ultrasonic sensors are available, radar sensors have maintained and will continue to maintain their importance due to their reliability in adverse weather conditions. Virtual methods are being developed for verification and validation of automated driving functions to reduce the time and cost of testing. Due to the complexity of modelling high-frequency wave propagation and signal processing and perception algorithms, sensor models that seek a high de

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Mihu-Pintilie and Nicu. "GIS-based Landform Classification of Eneoli thic Archaeological Sites in the Plateau-plain Transition Zone (NE Romania): Habitation Practices vs. Flood Hazard Perception." Remote Sensing 11, no. 8 (2019): 915. http://dx.doi.org/10.3390/rs11080915.

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The landforms of the Earth’s surface ranging from large-scale features to local topography are factors that influence human behavior in terms of habitation practices. The ability to extract geomorphological settings using geoinformatic techniques is an important aspect of any environmental analysis and archaeological landscape approach. Morphological data derived from DEMs with high accuracies (e.g., LiDAR data), can provide valuable information related to landscape modelling and landform classification processes. This study applies the first landform classification and flood hazard vulnerabil

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Revilla, Sergio, María Lamelas, Darío Domingo, et al. "Assessing the Potential of the DART Model to Discrete Return LiDAR Simulation—Application to Fuel Type Mapping." Remote Sensing 13, no. 3 (2021): 342. http://dx.doi.org/10.3390/rs13030342.

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Fuel type is one of the key factors for analyzing the potential of fire ignition and propagation in agricultural and forest environments. The increase of three-dimensional datasets provided by active sensors, such as LiDAR (Light Detection and Ranging), has improved the classification of fuel types through empirical modelling. Empirical methods are site and sensor specific while Radiative Transfer Models (RTM) approaches provide broader universality. The aim of this work is to analyze the suitability of Discrete Anisotropic Radiative Transfer (DART) model to replicate low density small-footpri

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Yoshida, Keisuke, Shijun Pan, Junichi Taniguchi, Satoshi Nishiyama, Takashi Kojima, and Md Touhidul Islam. "Airborne LiDAR-assisted deep learning methodology for riparian land cover classification using aerial photographs and its application for flood modelling." Journal of Hydroinformatics 24, no. 1 (2022): 179–201. http://dx.doi.org/10.2166/hydro.2022.134.

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Abstract In response to challenges in land cover classification (LCC), many researchers have experimented recently with classification methods based on artificial intelligence techniques. For LCC mapping of the vegetated Asahi River in Japan, the current study uses deep learning (DL)-based DeepLabV3+ module for image segmentation of aerial photographs. We modified the existing model by concatenating data on its resultant output port to access the airborne laser bathymetry (ALB) dataset, including voxel-based laser points and vegetation height (i.e. digital surface model data minus digital terr

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Wei, Xuebin, and Xiaobai Yao. "3D Model Construction in an Urban Environment from Sparse LiDAR Points and Aerial Photos—a Statistical Approach." GEOMATICA 69, no. 3 (2015): 271–84. http://dx.doi.org/10.5623/cig2015-302.

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Light Detection and Ranging (LiDAR) has become an important data source in urban modelling. Traditional methods of LiDAR data processing for building detection require high spatial resolution data and sophisticated algorithms. The aerial photos, on the other hand, provide continuous spectral information on buildings. However, the accuracy of classified building boundaries from aerial photos is constrained when building roofs and their surroundings share analogous spectral characteristics. This paper develops a statistical approach that can integrate characteristic variables derived from sparse

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Wang, Di, Stéphane Momo Takoudjou, and Eric Casella. "LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR." Methods in Ecology and Evolution 11, no. 3 (2020): 376–89. http://dx.doi.org/10.1111/2041-210x.13342.

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Chebrolu, Nived, Philipp Lottes, Alexander Schaefer, Wera Winterhalter, Wolfram Burgard, and Cyrill Stachniss. "Agricultural robot dataset for plant classification, localization and mapping on sugar beet fields." International Journal of Robotics Research 36, no. 10 (2017): 1045–52. http://dx.doi.org/10.1177/0278364917720510.

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There is an increasing interest in agricultural robotics and precision farming. In such domains, relevant datasets are often hard to obtain, as dedicated fields need to be maintained and the timing of the data collection is critical. In this paper, we present a large-scale agricultural robot dataset for plant classification as well as localization and mapping that covers the relevant growth stages of plants for robotic intervention and weed control. We used a readily available agricultural field robot to record the dataset on a sugar beet farm near Bonn in Germany over a period of three months

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Trouvé, Raphael, Ruizhu Jiang, Melissa Fedrigo, et al. "Combining Environmental, Multispectral, and LiDAR Data Improves Forest Type Classification: A Case Study on Mapping Cool Temperate Rainforests and Mixed Forests." Remote Sensing 15, no. 1 (2022): 60. http://dx.doi.org/10.3390/rs15010060.

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Predictive vegetation mapping is an essential tool for managing and conserving high conservation-value forests. Cool temperate rainforests (Rainforest) and cool temperate mixed forests (Mixed Forest, i.e., rainforest spp. overtopped by large remnant Eucalyptus trees) are threatened forest types in the Central Highlands of Victoria. Logging of these forest types is prohibited; however, the surrounding native Eucalyptus forests can be logged in some areas of the landscape. This requires accurate mapping and delineation of these vegetation types. In this study, we combine niche modelling, multisp

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Mittelmeier, Niko, Julian Allin, Tomas Blodau, et al. "An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects." Wind Energy Science 2, no. 2 (2017): 477–90. http://dx.doi.org/10.5194/wes-2-477-2017.

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Abstract. For offshore wind farms, wake effects are among the largest sources of losses in energy production. At the same time, wake modelling is still associated with very high uncertainties. Therefore current research focusses on improving wake model predictions. It is known that atmospheric conditions, especially atmospheric stability, crucially influence the magnitude of those wake effects. The classification of atmospheric stability is usually based on measurements from met masts, buoys or lidar (light detection and ranging). In offshore conditions these measurements are expensive and sca

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Pirotti, F., A. Guarnieri, A. Masiero, A. Vettore, and E. Lingua. "Processing lidar waveform data for 3D visual assessment of forest environments." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-5 (June 6, 2014): 493–99. http://dx.doi.org/10.5194/isprsarchives-xl-5-493-2014.

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The objective of this report is to present and discuss a work-flow for extracting, from full-waveform (FW) lidar data, formats which are compatible with common information systems (GIS) and statistical software packages. Full-waveform, specifically for forestry, got attention from the scientific community because a more in-depth analysis can add valuable information for classification and modelling of related variables (e.g. biomass). In order to assess if this is feasible and if the results are useful, the end-user has to deal with raw datasets from lidar sensors. In this study case we propos

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Sicard, M., G. D'Amico, A. Comerón, et al. "EARLINET: potential operationality of a research network." Atmospheric Measurement Techniques Discussions 8, no. 7 (2015): 6599–659. http://dx.doi.org/10.5194/amtd-8-6599-2015.

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Abstract. In the framework of ACTRIS summer 2012 measurement campaign (8 June–17 July 2012), EARLINET organized and performed a controlled exercise of feasibility to demonstrate its potential to perform operational, coordinated measurements and deliver products in near-real time. Eleven lidar stations participated to the exercise which started on 9 July 2012 at 06:00 UT and ended 72 h later on 12 July at 06:00 UT. For the first time the Single-Calculus Chain (SCC), the common calculus chain developed within EARLINET for the automatic evaluation of lidar data from raw signals up to the final pr

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Wibisana, Maulana Ikram, Mila Koeva, Pirouz Nourian, Dessislava Petrova-Antonova, and Kaloyan Karamitov. "A LiDAR-Based Digital Twinning Workflow for Traffic Monitoring and Simulation." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4-2024 (October 18, 2024): 411–18. http://dx.doi.org/10.5194/isprs-annals-x-4-2024-411-2024.

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Abstract. The process of ensuring efficient and safe urban transportation is closely linked to urban planning, particularly through the aspects of transportation planning. Transportation planning is a pivotal concern for urban regions worldwide, reflecting the growing need to increase mobility while ensuring safety and sustainability in densely populated areas. This research focuses on developing a novel digital-twin-based approach for micro-traffic simulation to support data-driven decision-making for increasing traffic safety through scenario planning. Leveraging the traffic data obtained th

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Ortiz-Amezcua, Pablo, Alodía Martínez-Herrera, Antti J. Manninen, et al. "Wind and Turbulence Statistics in the Urban Boundary Layer over a Mountain–Valley System in Granada, Spain." Remote Sensing 14, no. 10 (2022): 2321. http://dx.doi.org/10.3390/rs14102321.

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Urban boundary layer characterization is currently a challenging and relevant issue, because of its role in weather and air quality modelling and forecast. In many cities, the effect of complex topography at local scale makes this modelling even more complicated. This is the case of mid-latitude urban areas located in typical basin topographies, which usually present low winds and high turbulence within the atmospheric boundary layer (ABL). This study focuses on the analysis of the first ever measurements of wind with high temporal and vertical resolution throughout the ABL over a medium-sized

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Behley, Jens, Martin Garbade, Andres Milioto, et al. "Towards 3D LiDAR-based semantic scene understanding of 3D point cloud sequences: The SemanticKITTI Dataset." International Journal of Robotics Research 40, no. 8-9 (2021): 959–67. http://dx.doi.org/10.1177/02783649211006735.

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A holistic semantic scene understanding exploiting all available sensor modalities is a core capability to master self-driving in complex everyday traffic. To this end, we present the SemanticKITTI dataset that provides point-wise semantic annotations of Velodyne HDL-64E point clouds of the KITTI Odometry Benchmark. Together with the data, we also published three benchmark tasks for semantic scene understanding covering different aspects of semantic scene understanding: (1) semantic segmentation for point-wise classification using single or multiple point clouds as input; (2) semantic scene co

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Park, Yujin, and Jean-Michel Guldmann. "Creating 3D city models with building footprints and LIDAR point cloud classification: A machine learning approach." Computers, Environment and Urban Systems 75 (May 2019): 76–89. http://dx.doi.org/10.1016/j.compenvurbsys.2019.01.004.

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Pöchtrager, Markus, Gudrun Styhler-Aydın, Marina Döring-Williams, and Norbert Pfeifer. "Digital reconstruction of historic roof structures: developing a workflow for a highly automated analysis." Virtual Archaeology Review 9, no. 19 (2018): 21. http://dx.doi.org/10.4995/var.2018.8855.

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<p>Planning on adaptive reuse, maintenance and restoration of historic timber structuresrequiresextensive architectural and structural analysis of the actual condition. Current methods for a modellingof roof constructions consist of several manual steps including the time-consuming dimensional modelling. The continuous development of terrestrial laser scanners increases the accuracy, comfort and speed of the surveying work inroof constructions. Resultingpoint clouds enabledetailed visualisation of theconstructionsrepresented by single points or polygonal meshes, but in fact donot contain

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Kissling, W. Daniel, Arie Seijmonsbergen, Ruud Foppen, and Willem Bouten. "eEcoLiDAR, eScience infrastructure for ecological applications of LiDAR point clouds: reconstructing the 3D ecosystem structure for animals at regional to continental scales." Research Ideas and Outcomes 3 (July 16, 2017): e14939. https://doi.org/10.3897/rio.3.e14939.

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The lack of high-resolution measurements of 3D ecosystem structure across broad spatial extents impedes major advancements in animal ecology and biodiversity science. We aim to fill this gap by using Light Detection and Ranging (LiDAR) technology to characterize the vertical and horizontal complexity of vegetation and landscapes at high resolution across regional to continental scales. The newly LiDAR-derived 3D ecosystem structures will be applied in species distribution models for breeding birds in forests and marshlands, for insect pollinators in agricultural landscapes, and songbirds at st

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Gruen, A., S. Schubiger, R. Qin, et al. "SEMANTICALLY ENRICHED HIGH RESOLUTION LOD 3 BUILDING MODEL GENERATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W15 (September 23, 2019): 11–18. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w15-11-2019.

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<p><strong>Abstract.</strong> This paper reports about an effort to generate LoD3 models of buildings semi-automatically, with the highest possible level of automation. It is work in progress. We use multi-sensor data like aerial images from a 5-head camera with a GSD of 10&amp;thinsp;cm, UAV images, and aerial and mobile LiDAR point clouds. We distinguish two cases: LoD2 models are available and they are not. We apply Multi-Photo Geometrically Constrained Least Squares Matching for different kind of point measurements. The regularity of many building fa&amp;ccedil;ad

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Buldo, Michele, Luis Agustín-Hernández, and Cesare Verdoscia. "Semantic Enrichment of Architectural Heritage Point Clouds Using Artificial Intelligence: The Palacio de Sástago in Zaragoza, Spain." Heritage 7, no. 12 (2024): 6938–65. https://doi.org/10.3390/heritage7120321.

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In the current landscape dominated by Artificial Intelligence, the integration of Machine Learning and Deep Learning within the realm of Cultural Heritage, particularly within architectural contexts, is paramount for the efficient processing and interpretation of point clouds. These advanced methods facilitate automated segmentation and classification, significantly improving both the clarity and practical use of data acquired from laser scanning and photogrammetry. The present study investigates the Palacio de Sástago—a prominent Renaissance palace in Zaragoza, Spain—and introduces a cutting-

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Wen, Li, Shawn Ryan, Megan Powell, and Joanne E. Ling. "From Clusters to Communities: Enhancing Wetland Vegetation Mapping Using Unsupervised and Supervised Synergy." Remote Sensing 17, no. 13 (2025): 2279. https://doi.org/10.3390/rs17132279.

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High thematic resolution vegetation mapping is essential for monitoring wetland ecosystems, supporting conservation, and guiding water management. However, producing accurate, fine-scale vegetation maps in large, heterogeneous floodplain wetlands remains challenging due to complex hydrology, spectral similarity among vegetation types, and the high cost of extensive field surveys. This study addresses these challenges by developing a scalable vegetation classification framework that integrates cluster-guided sample selection, Random Forest modelling, and multi-source remote-sensing data. The ap

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Žabota, Barbara, Matjaž Mikoš, and Milan Kobal. "Rockfall Modelling in Forested Areas: The Role of Digital Terrain Model Grid Cell Size." Applied Sciences 11, no. 4 (2021): 1461. http://dx.doi.org/10.3390/app11041461.

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This article examines how digital terrain model (DTM) grid cell size influences rockfall modelling using a probabilistic process-based model, Rockyfor3D, while taking into account the effect of forest on rockfall propagation and runout area. Two rockfall sites in the Trenta valley, NW Slovenia, were chosen as a case study. The analysis included DTM square grid cell sizes of 1, 2, 5, and 10 m, which were extracted from LiDAR data. In the paper, we compared results of rockfall propagation and runout areas, maximum kinetic energy, and maximum passing height between different grid cell sizes and f

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Wang, Yanjun, Yunhao Lin, Huiqing Huang, Shuhan Wang, Shicheng Wen, and Hengfan Cai. "A Weak Sample Optimisation Method for Building Classification in a Semi-Supervised Deep Learning Framework." Remote Sensing 15, no. 18 (2023): 4432. http://dx.doi.org/10.3390/rs15184432.

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Deep learning has gained widespread interest in the task of building semantic segmentation modelling using remote sensing images; however, neural network models require a large number of training samples to achieve better classification performance, and the models are more sensitive to error patches in the training samples. The training samples obtained in semi-supervised classification methods need less reliable weakly labelled samples, but current semi-supervised classification research puts the generated weak samples directly into the model for applications, with less consideration of the i

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