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  1. 1. Introduction
  2. Use of LIDAR in landslide investigations: a review
  3. Synthetic aperture lidar as a future tool for earth observation
  4. A remote forest monitoring system
  5. Headwall integrates hyperspectral and lidar instruments aboard UAV platforms

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Request a Quote Request a Demo Contact. Focus S The ultra-portable Focus S enables fast, straightforward and ultra-high accurate measurements of objects and buildings. Focus S 70 The ultra-portable Focus S 70 enables fast, straightforward and ultra-high accurate measurements of objects and buildings.

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You can read more about how we use cookies and how to configure or disable them on our site. The acquired experience in running the SVD script can be incorporated into future works to improve their performance. Since the algorithm itself is random, not all the same points and only slightly different planes will be output anytime a run is attempted on a particular roof structure or roof facade. Hence it is suggested to include in the homogeneity criteria the minimization of the angular discrepancy between the raw and the interpolated z values.

It will facilitate the prediction and management of urban sprawl [ 75 — 77 ] and push forward sustainable urban planning in not just the developed world, but also the developing countries as they catch on with the technologies [ 46 , 49 ]. To that end, open sourcing the methodology and software is another pressing topic, which could help further accelerate the paradigm shift. The paper provides a methodology for the application of LiDAR to automated solar photovoltaic deployment analysis on the regional scale.

First, a comprehensive examination and comparisons of existing algorithms and approaches to turn LiDAR point cloud into 2. The methodology implements what previous literature recommends in terms of integrating cross disciplinary competences in remote sensing, GIS, computer vision and urban environmental studies. It is a robust methodology that can work with poor-quality data and reconstruct vegetation and building separately but concurrently.

Since the coarse selection of building regions is crucial to reliable results considerable attention was focused on this first step. Subsequent steps in building extraction, segmentation and reconstruction were carried out accompanied with mathematical proofs and illustrations. The approach was data driven hence the whole attempt can be regarded as a large scale optimization problem aiming at best approximating the point cloud.

Rules of thumb were collected to incorporate in the development of such scripts for extracting rooftops for solar photovoltaic potential. But there is still room for the more mathematically rigorous or biologically minded audience to contribute and orient the workflow to suit their needs. Hence this can be regarded as the next step towards a new generation of urban analysis software.

National Center for Biotechnology Information , U. Journal List Sensors Basel v. Sensors Basel. Published online Apr Nguyen , 1 Joshua M. Joshua M. Author information Article notes Copyright and License information Disclaimer. This article has been cited by other articles in PMC. Introduction Solar photovoltaic PV energy conversion offers a sustainable method of producing electricity to provide for contemporary society's needs [ 1 ].

Background 2. LiDAR and the Cityscape The proverbial Holy Grail of the urban remote sensing research community is the ability to quickly and easily build accurate 2D and 3D representations of urban areas. Building Detection Reliable and accurate building generation from LiDAR data requires a number of processes beyond capture of accurate raw data.

Building Segmentation Segmentation provides an excellent starting point for subsequent geospatial analyses [ 50 , 51 ]. Building Reconstruction After individual cloud segments that correspond to building faces are recognized a method is needed to interpolate the heights in between the points and hence transform the working geometry from points to polygons. Methodology As outlined above, a wide range of techniques have been used to extract building geometry, and in particular roof geometry, from LiDAR point clouds and from imagery with or without independent building outline data.

Open in a separate window. Figure 1. Figure 2. Elevation Cut-Off Next, all points within the roofs' outlines are filtered by introducing a threshold value above the bare earth elevation level DEM , with the goal being to remove LiDAR points sampled through skylights, into small courtyards, and the like. Figure 3. Individual Point Subcloud Processing. Tree and Noise Detection Since the automation algorithms are highly sensitive to any remaining noise, i. Roof Fitting The subpoints at this point are ready to be segmented and used for reconstruction.

Noise points were to be eliminated before running the script Section 3. Results 4. Buffering Size Determination The effects of each buffer size on the chosen area were investigated by counting the number of points being encompassed by each buffer. Figure 4. Elevation Cutoff Based on point counts it was found that i 2. Figure 5. Figure 6. Figure 7. Point Cloud Statistical Analysis Point cloud statistical analysis was carried out to distinguish flat rooftops from tilted roof planes. Error Analysis Nyruhuma assessed how accurate the reconstructed urban scene was to reality using roof angle, roof area and building height [ 56 ].

Figure 8. Conclusions The paper provides a methodology for the application of LiDAR to automated solar photovoltaic deployment analysis on the regional scale. References 1. Pearce J. Photovoltaics—A path to sustainable futures. Olz S. Contribution of Renewables to Energy Security. Wong J. Getting out of the shade: Solar energy as a National Security Strategy. China Secur. Branker K. Financial return for government support of large-scale thin film solar photovoltaic manufacturing in Canada. Energy Policy.

Myrans K. Fthenakis V.


Emissions from photovoltaic life cycles. Cameron M. The changing landscape of the global solar electricity market: Opportunities and challenges for European Industry. Hoffman W. PV solar electricity industry: Market growth and perspective. Energy Mater. Green M. Solar cell efficiency tables Version 35 Prog. Mints P. Renewable Energy Focus. Frankl P. Technology Roadmap-Solar Photovoltaic Energy. International Energy Agency; Paris, France: Doty G. Volume Price S. Renewable energy sources: Their global potential for the first-half of the 21st century at a global level: An integrated approach.

Marvin S. Infrastructure provision, development processes and the co-production of environmental value. Urban Stud.

1. Introduction

Helm D. The assessment: The new energy paradigm. Cities, regions and privatised utilities. Monstad J. Urban governance and the transition of energy systems: Institutional change and shifting energy and climate policies in Berlin. Urban Reg.


Use of LIDAR in landslide investigations: a review

Wiginton L. Quantifying solar photovoltaic potential on a large scale for renewable energy regional policy. Urban Syst. Nguyen H. Estimating potential photovoltaic yield with r. Brenner C. Building reconstruction from images and laser scanning. Earth Obs. Dorninger P. A comprehensive automated 3D approach for building extraction, reconstruction and regularization from airborne laser scanning point clouds. Carneiro C. Incorporating shading losses in solar photovoltaic potential assessment at the municipal scale.

Habib A. Kim C. Object-based integration of photogrammetric and LiDAR data for automated generation of complex polyhedral building models. Alexander C. Integrating building footprints and LiDAR elevation data to classify roof structures and visualize buildings. Jochem A. Automatic roof plan detection and analysis of airborne liDAR point clouds for solar potential assessment. PDF accessed on 16 February Zhou Q. Rottensteiner F. Suzuki S. Lafarge F. Automatic building extraction from DEMs using an object approach and application to the 3d-city modeling. Ratti C.

Synthetic aperture lidar as a future tool for earth observation

Energy consumption and urban texture. Energy Build. Compagnon R. Solar and daylight availability in the urban fabric. Madlener R. Impacts of urbanization on urban structures and energy demand: What can we learn for urban energy planning and urbanization management? Cities Soc. Awrangjeb M.

A remote forest monitoring system

Khoshelham K. Performance evaluation of automated approaches to building detection in multi-source aerial data. Matikainen L. Automatic detection of buildings and changes in buildings for updating of maps. Remote Sens. Building detection by fusion of airborne laser scanner data and multi-spectral images: Performance evaluation and sensitivity analysis.

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Kassner R. Towards virtual maps: On the production of 3D city models. Kaartinen H. Proceedings of the Laser Scanning ; Enschede, the Netherlands. Ahmadi S. Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours. Kass M. Snakes: Active contour models. Baud I. Understanding heterogeneity in metropolitan India: The added value of remote sensing data for analyzing sub-standard residential areas. Devereux B. An efficient image segmentation algorithm for landscape analysis. Vosselman G. This threshold depends on the type of bedrock, the presence or absence of a soil cover and the DEM resolution Loye et al.

This method makes it possible to discriminate efficiently the true cliffs from the one drawn on topographic maps. As discussed in Janeras et al. Hazard assessment requires often performing trajectographic modelling in order to delineate the propagation area. In addition, the kinetic energy profile is greatly modified when the resolution of the DEM is increased.

Both parameters are of primary importance for hazard mapping and dimensioning of mitigation measures. But obtaining the complete field of displacements for the whole landslide is of great help to understand landslide kinematics and failure mechanism Fig. Laser scanning is nowadays a common tool for displacement monitoring even if few published papers exists. Nevertheless, as monitoring requires both high resolution and high precision data sets, most of the works have been done up to now using TLS-derived HRDEMs.

The results are either vectors between two points or common areas or distances between two data sets point to surface comparison either in a user-defined direction or as shortest distance Hausdorff distance between the two surfaces. This difference calculation allows for the computation of volume differences, as is discussed by different authors Bitelli et al.

Together with these monitoring results, the possibility to link spatial and temporal prediction of rockfalls constitutes a great challenge for landslide monitoring. Indeed, two different precursory indicators are currently being investigated: 1 the increase in rockfall activity before the final collapse, shown by Rosser et al. Although in certain cases precursory movements can be of the same order of magnitude than instrumental errors, different authors have observed that errors can be considerably reduced by taking into account the information of the neighbouring points, i.

As an example, it was shown that it is possible to detect millimetric surface displacements in an outdoor experiment, even if single points had a higher standard deviation Abellan et al. As we already mentioned, it is possible to monitor the channel changes using TLS Oppikofer ; Theules et al. Recently, Scheidl et al. Open Access This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author s and source are credited.

Eng Geol — Abellan A, Jaboyedoff M, Oppikofer T, Vilaplana JM Detection of millimetric deformation using a terrestrial laser scanner: experiment and application to rockfall event. Nat Hazards Earth Syst Sci — Geomorphology — Case study of the basaltic rock face at Castellfollit de la Roca Catalonia, Spain. Geomorphology 3—4 — Baltsavias EP Airbone laser scanning: basic relations and formulas. Landslide — Bitelli G, Dubbini M, Zanutta A Terrestrial laser scanning and digital photogrammetry techniques to monitor landslide bodies. Geophys Res Abst Google Scholar. Lausanne, Switzerland, pp — Google Scholar.

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J Struct Geol — Can Geotech J — Tectonophysics 3—4 — Gordon S, Lichti D, Stewart M Application of a high-resolution, ground-based laser scanner for deformation measurements. Comput Geosci 29 7 — Haneberg WC Directional roughness profiles from three-dimensional photogrammetric or laser scanner point clouds. Bull Eng Geol Environ — Harding D Pulsed laser altimeter ranging techniques and implications for terrain mapping, Chap 5. Earth Surf Proc Land 34 12 — Appl Opt 19 6 — Hungr O Classification and terminology.

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Infrared information analysis center. Ann Arbor, Michigan, pp 1—76 Google Scholar. Kemeny J, Post R Estimating three-dimensional rock discontinuity orientation from digital images of fracture traces. Comput Geosci — Rock slope Mapping and assessment. Int J Remote Sens 16 7 — Science — Comput Geosci 33 2 — Photogramm Rec — Geomat Res Australas —24 Google Scholar. Lindenbergh R, Pfeifer N A statistical deformation analysis of two epochs of terrestrial laser data of a lock.

McKean J, Roering J Objective landslide detection and surface morphology mapping using high-resolution airborne laser altimetry. Miller B Laser altimeter may aid photo mapping. Monserrat O, Crosetto M Deformation measurement using terrestrial laser scanning data and least squares 3D surface matching. Geology — Swiss J Geosci 2 — Oppikofer T Detection, analysis and monitoring of slope movements by high-resolution digital elevation models. Nat Geosci — Int J Remote Sens 28 1 — Annals Geophys — Google Scholar. Introduction to laser ranging, profiling and scanning, II.

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Headwall integrates hyperspectral and lidar instruments aboard UAV platforms

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