scholarly journals New Metrics for Spatial and Temporal 3D Urban Form Sustainability Assessment Using Time Series Lidar Point Clouds and Advanced GIS Techniques

2020 ◽  
Author(s):  
Sara Shirowzhan ◽  
John Trinder ◽  
Paul Osmond
Keyword(s):  
Time Series ◽  
Urban Form ◽  
Processing Time ◽  
Sustainability Assessment ◽  
Smart Cities ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Technical Problems ◽  
3D Data ◽  
Over Time

Monitoring sustainability of urban form as a 3D phenomenon over time is crucial in the era of smart cities for better planning of the future, and for such a monitoring system, appropriate tools, metrics, methodologies and time series 3D data are required. While accurate time series 3D data are becoming available, a lack of 3D sustainable urban form (3D SUF) metrics, appropriate methodologies and technical problems of processing time series 3D data has resulted in few studies on the assessment of 3D SUF over time. In this chapter, we review volumetric building metrics currently under development and demonstrate the technical problems associated with their validation based on time series airborne lidar data. We propose new metrics for application in spatial and temporal 3D SUF assessment. We also suggest a new approach in processing time series airborne lidar to detect three-dimensional changes of urban form. Using this approach and the developed metrics, we detected a decreased volume of vegetation and new areas prepared for the construction of taller buildings. These 3D changes and the proposed metrics can be used to numerically measure and compare urban areas in terms of trends against or in favor of sustainability goals for caring for the environment.

scholarly journals CityJSON Building Generation from Airborne LiDAR 3D Point Clouds

2020 ◽  
Vol 9 (9) ◽  
pp. 521
Author(s):  
Gilles-Antoine Nys ◽  
Florent Poux ◽  
Roland Billen
Keyword(s):  
Smart Cities ◽  
Contextual Information ◽  
Compliance Rate ◽  
Point Clouds ◽  
3D Models ◽  
Automatic Generation ◽  
Airborne Lidar ◽  
Generation Process ◽  
3D Point Clouds ◽  
Unique Source

The relevant insights provided by 3D City models greatly improve Smart Cities and their management policies. In the urban built environment, buildings frequently represent the most studied and modeled features. CityJSON format proposes a lightweight and developer-friendly alternative to CityGML. This paper proposes an improvement to the usability of 3D models providing an automatic generation method in CityJSON, to ensure compactness, expressivity, and interoperability. In addition to a compliance rate in excess of 92% for geometry and topology, the generated model allows the handling of contextual information, such as metadata and refined levels of details (LoD), in a built-in manner. By breaking down the building-generation process, it creates consistent building objects from the unique source of Light Detection and Ranging (LiDAR) point clouds.

scholarly journals HYBRID GEOREFERENCING, ENHANCEMENT AND CLASSIFICATION OF ULTRA-HIGH RESOLUTION UAV LIDAR AND IMAGE POINT CLOUDS FOR MONITORING APPLICATIONS

2020 ◽  
Vol V-2-2020 ◽  
pp. 727-734
Author(s):  
N. Haala ◽  
M. Kölle ◽  
M. Cramer ◽  
D. Laupheimer ◽  
G. Mandlburger ◽  
...  
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Image Texture ◽  
Image Point ◽  
Joint Orientation ◽  
3D Point Clouds ◽  
3D Data

Abstract. This paper presents a study on the potential of ultra-high accurate UAV-based 3D data capture by combining both imagery and LiDAR data. Our work is motivated by a project aiming at the monitoring of subsidence in an area of mixed use. Thus, it covers built-up regions in a village with a ship lock as the main object of interest as well as regions of agricultural use. In order to monitor potential subsidence in the order of 10 mm/year, we aim at sub-centimeter accuracies of the respective 3D point clouds. We show that hybrid georeferencing helps to increase the accuracy of the adjusted LiDAR point cloud by integrating results from photogrammetric block adjustment to improve the time-dependent trajectory corrections. As our main contribution, we demonstrate that joint orientation of laser scans and images in a hybrid adjustment framework significantly improves the relative and absolute height accuracies. By these means, accuracies corresponding to the GSD of the integrated imagery can be achieved. Image data can also help to enhance the LiDAR point clouds. As an example, integrating results from Multi-View Stereo potentially increases the point density from airborne LiDAR. Furthermore, image texture can support 3D point cloud classification. This semantic segmentation discussed in the final part of the paper is a prerequisite for further enhancement and analysis of the captured point cloud.

scholarly journals COMPREHENSIVE COMPARISON OF TWO IMAGE-BASED POINT CLOUDS FROM AERIAL PHOTOS WITH AIRBORNE LIDAR FOR LARGE-SCALE MAPPING

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 557-565 ◽  
Author(s):  
E. Widyaningrum ◽  
B. G. H. Gorte
Keyword(s):  
Large Scale ◽  
Three Dimensional ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Topographic Map ◽  
Aerial Photos ◽  
3D Data ◽  
Comparison Results ◽  
Global Matching ◽  
3D Information

The integration of computer vision and photogrammetry to generate three-dimensional (3D) information from images has contributed to a wider use of point clouds, for mapping purposes. Large-scale topographic map production requires 3D data with high precision and accuracy to represent the real conditions of the earth surface. Apart from LiDAR point clouds, the image-based matching is also believed to have the ability to generate reliable and detailed point clouds from multiple-view images. In order to examine and analyze possible fusion of LiDAR and image-based matching for large-scale detailed mapping purposes, point clouds are generated by Semi Global Matching (SGM) and by Structure from Motion (SfM). In order to conduct comprehensive and fair comparison, this study uses aerial photos and LiDAR data that were acquired at the same time. Qualitative and quantitative assessments have been applied to evaluate LiDAR and image-matching point clouds data in terms of visualization, geometric accuracy, and classification result. The comparison results conclude that LiDAR is the best data for large-scale mapping.

scholarly journals CHANGE DETECTION FROM POINT CLOUDS TO SUPPORT INDOOR 3D CADASTRE

2018 ◽  
Vol XLII-4 ◽  
pp. 451-457
Author(s):  
S. Nikoohemat ◽  
M. Koeva ◽  
S. J. Oude Elberink ◽  
C. H. J. Lemmen
Keyword(s):  
Point Clouds ◽  
3D Models ◽  
Domain Model ◽  
Indoor Environments ◽  
3D Data ◽  
Spatial Changes ◽  
3D Cadastre ◽  
Building Geometry ◽  
Changes Over Time ◽  
Over Time

<p><strong>Abstract.</strong> Recently in The Netherlands, there are many examples of changes in the functionalities of buildings over time. Tracking these changes could be challenging when the building geometry will change as well; for example a change from administrative to residential use of the space, or merging two spaces in the building without updating the functionality. To record the changes, a common practice is to use 2D plans for subdivisions and to assign new rights, restrictions and responsibilities for the changes in a building. In the meantime, with the advances of 3D data collection techniques, the benefits of 3D models in various forms are increasingly being researched. The current work explores the opportunities of using the point clouds to establish a link between spatial changes and 3D Cadastre in indoor environments. We investigate the changes over time in the geometry of the building that can be automatically detected from point clouds to update the 3D indoor cadastre. The permanent changes (e.g., walls, rooms) are automatically distinguished by dynamic changes (e.g., human, furniture) and will be associated with the space subdivisions. Finally, the results will be linked to the spatial units in a Land Administration Domain Model (LADM).</p>

scholarly journals A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 141
Author(s):  
Jacob Hale ◽  
Suzanna Long
Keyword(s):  
Time Series ◽  
Electricity Generation ◽  
Sustainability Assessment ◽  
Moving Average ◽  
Time Series Prediction ◽  
State Level ◽  
Autoregressive Integrated Moving Average ◽  
Energy Transitions ◽  
Univariate Time Series ◽  
Partial Energy

Energy portfolios are overwhelmingly dependent on fossil fuel resources that perpetuate the consequences associated with climate change. Therefore, it is imperative to transition to more renewable alternatives to limit further harm to the environment. This study presents a univariate time series prediction model that evaluates sustainability outcomes of partial energy transitions. Future electricity generation at the state-level is predicted using exponential smoothing and autoregressive integrated moving average (ARIMA). The best prediction results are then used as an input for a sustainability assessment of a proposed transition by calculating carbon, water, land, and cost footprints. Missouri, USA was selected as a model testbed due to its dependence on coal. Of the time series methods, ARIMA exhibited the best performance and was used to predict annual electricity generation over a 10-year period. The proposed transition consisted of a one-percent annual decrease of coal’s portfolio share to be replaced with an equal share of solar and wind supply. The sustainability outcomes of the transition demonstrate decreases in carbon and water footprints but increases in land and cost footprints. Decision makers can use the results presented here to better inform strategic provisioning of critical resources in the context of proposed energy transitions.

scholarly journals Terrestrial Laser Scanning for Vegetation Analyses with a Special Focus on Savannas

2021 ◽  
Vol 13 (3) ◽  
pp. 507
Author(s):  
Tasiyiwa Priscilla Muumbe ◽  
Jussi Baade ◽  
Jenia Singh ◽  
Christiane Schmullius ◽  
Christian Thau
Keyword(s):  
Remote Sensing ◽  
Vegetation Structure ◽  
Laser Scanning ◽  
Spatial Scales ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Parameter Extraction ◽  
Airborne Lidar ◽  
Special Focus ◽  
Vegetation Parameter

Savannas are heterogeneous ecosystems, composed of varied spatial combinations and proportions of woody and herbaceous vegetation. Most field-based inventory and remote sensing methods fail to account for the lower stratum vegetation (i.e., shrubs and grasses), and are thus underrepresenting the carbon storage potential of savanna ecosystems. For detailed analyses at the local scale, Terrestrial Laser Scanning (TLS) has proven to be a promising remote sensing technology over the past decade. Accordingly, several review articles already exist on the use of TLS for characterizing 3D vegetation structure. However, a gap exists on the spatial concentrations of TLS studies according to biome for accurate vegetation structure estimation. A comprehensive review was conducted through a meta-analysis of 113 relevant research articles using 18 attributes. The review covered a range of aspects, including the global distribution of TLS studies, parameters retrieved from TLS point clouds and retrieval methods. The review also examined the relationship between the TLS retrieval method and the overall accuracy in parameter extraction. To date, TLS has mainly been used to characterize vegetation in temperate, boreal/taiga and tropical forests, with only little emphasis on savannas. TLS studies in the savanna focused on the extraction of very few vegetation parameters (e.g., DBH and height) and did not consider the shrub contribution to the overall Above Ground Biomass (AGB). Future work should therefore focus on developing new and adjusting existing algorithms for vegetation parameter extraction in the savanna biome, improving predictive AGB models through 3D reconstructions of savanna trees and shrubs as well as quantifying AGB change through the application of multi-temporal TLS. The integration of data from various sources and platforms e.g., TLS with airborne LiDAR is recommended for improved vegetation parameter extraction (including AGB) at larger spatial scales. The review highlights the huge potential of TLS for accurate savanna vegetation extraction by discussing TLS opportunities, challenges and potential future research in the savanna biome.

scholarly journals Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Tree Height ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Simulation Based ◽  
Maximum Tree Height ◽  
Airborne Lidar Data ◽  
Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

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