ExCalibR: A targetless multi-LiDAR extrinsic calibration framework for diverse environments

This paper describes the methodology proposed for 1) calculate solar potential; 2) generate an urban 3D model, both based on LIDAR data; 3) semantize the urban 3D model with different data sources and calculations data for finally, 4) visualize the urban 3D model in a 3D web visualization tool. As a first step, digital surface model data of the case study is preprocessed selecting only building and ground points in order to later calculate the solar potential in a GIS tool. A workflow is presented describing the followed steps. In the second step, an urban 3D model is generated (in CityGML format) using cadastral data and LIDAR data, both digital surface and digital terrain model. Then, in the third step, the urban 3D model is semantized with a) buildings data (that comes from cadastral and statistical office), b) geometrical data such as main building orientation, number of adjoining walls, etc. (that comes from a geometric processing tool), c) key performance indicators data (that are calculated based on the urban 3D model data) and d) solar potential data, which have been calculated in the previous step. In the fourth step, all the gathered data is presented and can be filtered/selected in a 3D web visualization tool. This paper shows the potential of the usage of LIDAR data in different domains that can be connected using different technologies and in different scales.

Solar maps are becoming a popular resource and are available via the web to help plan investments for the benefits of renewable energy. These maps are especially useful when the results have high accuracy. LiDAR technology currently offers high-resolution data sources that are very suitable for obtaining an urban 3D geometry with high precision. Three-dimensional visualization also offers a more accurate and intuitive perspective of reality than 2D maps. This paper presents a new method for the calculation and visualization of the solar potential of building roofs on an urban 3D model, based on LiDAR data. The paper describes the proposed methodology to (1) calculate the solar potential, (2) generate an urban 3D model, (3) semantize the urban 3D model with different existing and calculated data, and (4) visualize the urban 3D model in a 3D web environment. The urban 3D model is based on the CityGML standard, which offers the ability to consistently combine geometry and semantics and enable the integration of different levels (building and city) in a continuous model. The paper presents the workflow and results of application to the city of Vitoria-Gasteiz in Spain. This paper also shows the potential use of LiDAR data in different domains that can be connected using different technologies and different scales.

Three-dimensional (3D) urban models, come with huge data size, mainly consisting of the details of geometry and texture of the objects, and simplification of both is often needed for efficient streaming, rendering and visualization. A large number of objects in 3D urban models are buildings and the image files representing their texture contain information of the walls, streets, doors, and windows etc., which have linear edges. We present a technique to identify the images representing the texture of these objects, and propose a compact format to encode them in much smaller size compared to the existing standard image formats.

In sustainable urban development, Urban Building Energy Modeling (UBEM) and smart city initiatives depend on computable three-dimensional (3D) urban models. To enhance computational efficiency, research often uses Level of Detail (LOD) 1 simplified models for simulations. While LOD1 models simplify building geometry, obtaining accurate building height data typically requires substantial manual effort. Balancing model simplification with simulation accuracy is crucial for reliable and efficient results. This study proposes an automated method using open-source electronic maps and UAV oblique imagery to address 3D modeling challenges caused by incomplete databases. We developed computable 3D urban models at various LODs, with the highest detail achieved at LOD2.1. EnergyPlus was used to examine the relationship between LOD and energy use. The study follows five key steps: (1) Data Collection and Preprocessing—gather building footprints and UAV point clouds, refine point clouds to create voxels; (2) Roof Point Acquisition—extract and cluster roof points to determine building height; (3) Roof Type Identification—use machine learning to predict roof types; (4) Parametric Modeling and Simulation—construct databases and models at varying LODs for UBEM; (5) Results Comparison—evaluate modeling time, modeling accuracy, and energy use across LODs. This method demonstrates high accuracy, achieving a roof face identification accuracy of 92%, with building height errors within 1 meter and an average modeling time of just 2.5 seconds per building. Additionally, the roof type prediction model based on point cloud features attained an impressive AUC of 0.93. Increasing LOD slightly extended the energy simulation time, with LOD2.1 increasing simulation time by 6.5 seconds per building compared to LOD1_H_mean. The baseline model and LOD1_H_mean’s Energy Use Intensity (EUI) exhibited a MAPE of 3.02% and an RMSE of 4.41 kWh/m², underscoring the importance of high-LOD models for accurate simulations. It was found that LOD has a greater impact on heating than cooling, and building height errors significantly affect cooling simulations. These findings highlight the importance of accurate building height in hot regions for effective cooling and high-LOD modeling in cold regions, with assuming flat roofs where the type is uncertain helping to minimize errors. In summary, this study presents methods for automating the construction of high-LOD models for UBEM, also applicable to microclimate simulation and CIM. It demonstrates the significant impact of high-LOD on simulation accuracy and provides solutions for various simulation needs and data limitations, advancing UBEM and smart city development.

The currently available long-term snow depth data sets are either from point-scale ground measurements or from gridded satellite/modeled/reanalysis data with coarse spatial resolution, which limits the applications in climate model, hydrological model, and regional snow disaster monitoring. Benefit from its unique advantages of cost-effective and high spatial-temporal resolution (~ 1000 m2, hourly in theory), snow depth retrieval using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has become a popular topic in recent years. However, due to complex environmental and observation conditions, developing robust and operational technology to produce long-term snow depth data sets using observations from various GNSS station networks is still challenging. The two objectives of this study are 1) to propose a comprehensive framework using raw data of the complex GNSS station networks to retrieve snow depth and to control its quality automatically; and 2) to produce a long-term snow depth data set over northern China (i.e., GSnow-CHINA v1.0, 12 h/24 h, 2013–2020) using the proposed framework and historical data from 80 stations. The data set has high internal consistency with regards to different GNSS systems (mean r = 0.97 & RMSD = 1.93 cm), different frequency bands (mean r = 0.96 & RMSD = 2.73 cm), and different GNSS receivers (mean r = 0.88). The data set also has high external consistency with the in-situ measurements and the passive microwave (PMW) product, with a consistent illustration of the interannual snow depth variability. The results also show the good potential of GNSS to derive hourly snow depth observations for better monitoring snow disasters. The proposed framework to develop the data set provides comprehensive and supportive information for users to process raw data of ground GNSS stations with complex environmental conditions and various observation conditions. The resulting GSnow-CHINA v1.0 data set is distinguished from the current point-scale in-situ data or coarse-gridded data, which can be used as an independent data source for validation purposes. The data set is also useful for regional climate research and other meteorological and hydrological applications. The algorithm and the data files will be maintained and updated as more years of data become available in the future. The GSnow-CHINA v1.0 data set is available at https://doi.org/10.11888/Cryos.tpdc.271839 (Wan et al. 2021).

The currently available long-term snow depth data sets are either from point-scale ground measurements or from gridded satellite/modeled/reanalysis data with coarse spatial resolution, which limits the applications in climate model, hydrological model, and regional snow disaster monitoring. Benefit from its unique advantages of cost-effective and high spatial-temporal resolution (~ 1000 m2, hourly in theory), snow depth retrieval using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has become a popular topic in recent years. However, due to complex environmental and observation conditions, developing robust and operational technology to produce long-term snow depth data sets using observations from various GNSS station networks is still challenging. The two objectives of this study are 1) to propose a comprehensive framework using raw data of the complex GNSS station networks to retrieve snow depth and to control its quality automatically; and 2) to produce a long-term snow depth data set over northern China (i.e., GSnow-CHINA v1.0, 12 h/24 h, 2013–2020) using the proposed framework and historical data from 80 stations. The data set has high internal consistency with regards to different GNSS systems (mean r = 0.97 & RMSD = 1.93 cm), different frequency bands (mean r = 0.96 & RMSD = 2.73 cm), and different GNSS receivers (mean r = 0.88). The data set also has high external consistency with the in-situ measurements and the passive microwave (PMW) product, with a consistent illustration of the interannual snow depth variability. The results also show the good potential of GNSS to derive hourly snow depth observations for better monitoring snow disasters. The proposed framework to develop the data set provides comprehensive and supportive information for users to process raw data of ground GNSS stations with complex environmental conditions and various observation conditions. The resulting GSnow-CHINA v1.0 data set is distinguished from the current point-scale in-situ data or coarse-gridded data, which can be used as an independent data source for validation purposes. The data set is also useful for regional climate research and other meteorological and hydrological applications. The algorithm and the data files will be maintained and updated as more years of data become available in the future. The GSnow-CHINA v1.0 data set is available at https://doi.org/10.11888/Cryos.tpdc.271839 (Wan et al. 2021).

<strong class="journal-contentHeaderColor">Abstract.</strong> The currently available long-term snow depth data sets are either from point-scale ground measurements or from gridded satellite/modeled/reanalysis data with coarse spatial resolution, which limits the applications in climate models, hydrological models, and regional snow disaster monitoring. Benefitting from its unique advantages of cost-effective and high spatiotemporal resolution (<span class="inline-formula">∼</span> 1000 m<span class="inline-formula">²</span>, hourly in theory), snow depth retrieval using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has become a popular topic in recent years. However, due to complex environmental and observation conditions, developing robust and operational technology to produce long-term snow depth data sets using observations from various GNSS station networks is still challenging. The two objectives of this study are (1) to propose a comprehensive framework using raw data of the complex GNSS station networks to retrieve snow depth and control its quality automatically; and (2) to produce a long-term snow depth data set over northern China (i.e., GSnow-CHINA v1.0, 12 h or 24 h, 2013–2022) using the proposed framework and historical data from 80 stations. The data set has high internal consistency with regards to different GNSS constellations (mean <span class="inline-formula"><i>r</i>=0</span>.98, RMSD <span class="inline-formula">=</span> 0.99 cm, and nRMSD (snow depth <span class="inline-formula">&gt;</span> 5 cm) <span class="inline-formula">=</span> 0.11), different frequency bands (mean <span class="inline-formula"><i>r</i></span> <span class="inline-formula">=</span> 0.97, RMSD <span class="inline-formula">=</span> 1.46 cm, and nRMSD (snow depth <span class="inline-formula">&gt;</span> 5 cm) <span class="inline-formula">=</span> 0.16), and different GNSS receivers (mean <span class="inline-formula"><i>r</i></span> <span class="inline-formula">=</span> 0.62). The data set also has high external consistency with the in situ measurements and the passive microwave (PMW) product, with a consistent illustration of the interannual snow depth variability. Additionally, the result show the potential of GNSS to derive hourly snow depth observations for better monitoring of snow disasters. The proposed framework to develop the data set provides comprehensive and supportive information for users to process raw data of ground GNSS stations with complex environmental conditions and various observation conditions. The resulting GSnow-CHINA v1.0 data set is distinguished from the current point-scale in situ data or coarse-gridded data, which can be used as an independent data source for validation purposes. The data set is also useful for regional climate research and other meteorological and hydrological applications. The algorithm and data files will be maintained and updated as more data become available in the future. The GSnow-CHINA v1.0 data set is available at the National Tibetan Plateau/Third Pole Environment Data Center via <a href="https://doi.org/10.11888/Cryos.tpdc.271839">https://doi.org/10.11888/Cryos.tpdc.271839</a> (Wan et al., 2021).

The currently available long-term snow depth data sets are either from point-scale ground measurements or from gridded satellite/modeled/reanalysis data with coarse spatial resolution, which limits the applications in climate model, hydrological model, and regional snow disaster monitoring. Benefit from its unique advantages of cost-effective and high spatial-temporal resolution (~ 1000 m2, hourly in theory), snow depth retrieval using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has become a popular topic in recent years. However, due to complex environmental and observation conditions, developing robust and operational technology to produce long-term snow depth data sets using observations from various GNSS station networks is still challenging. The two objectives of this study are 1) to propose a comprehensive framework using raw data of the complex GNSS station networks to retrieve snow depth and to control its quality automatically; and 2) to produce a long-term snow depth data set over northern China (i.e., GSnow-CHINA v1.0, 12 h/24 h, 2013–2020) using the proposed framework and historical data from 80 stations. The data set has high internal consistency with regards to different GNSS systems (mean r = 0.97 & RMSD = 1.93 cm), different frequency bands (mean r = 0.96 & RMSD = 2.73 cm), and different GNSS receivers (mean r = 0.88). The data set also has high external consistency with the in-situ measurements and the passive microwave (PMW) product, with a consistent illustration of the interannual snow depth variability. The results also show the good potential of GNSS to derive hourly snow depth observations for better monitoring snow disasters. The proposed framework to develop the data set provides comprehensive and supportive information for users to process raw data of ground GNSS stations with complex environmental conditions and various observation conditions. The resulting GSnow-CHINA v1.0 data set is distinguished from the current point-scale in-situ data or coarse-gridded data, which can be used as an independent data source for validation purposes. The data set is also useful for regional climate research and other meteorological and hydrological applications. The algorithm and the data files will be maintained and updated as more years of data become available in the future. The GSnow-CHINA v1.0 data set is available at https://doi.org/10.11888/Cryos.tpdc.271839 (Wan et al. 2021).

Abstract. The currently available long-term snow depth data sets are either from point-scale ground measurements or from gridded satellite/modeled/reanalysis data with coarse spatial resolution, which limits the applications in climate models, hydrological models, and regional snow disaster monitoring. Benefitting from its unique advantages of cost-effective and high spatiotemporal resolution (∼ 1000 m2, hourly in theory), snow depth retrieval using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has become a popular topic in recent years. However, due to complex environmental and observation conditions, developing robust and operational technology to produce long-term snow depth data sets using observations from various GNSS station networks is still challenging. The two objectives of this study are (1) to propose a comprehensive framework using raw data of the complex GNSS station networks to retrieve snow depth and control its quality automatically; and (2) to produce a long-term snow depth data set over northern China (i.e., GSnow-CHINA v1.0, 12 h or 24 h, 2013–2022) using the proposed framework and historical data from 80 stations. The data set has high internal consistency with regards to different GNSS constellations (mean r=0.98, RMSD = 0.99 cm, and nRMSD (snow depth &gt; 5 cm) = 0.11), different frequency bands (mean r = 0.97, RMSD = 1.46 cm, and nRMSD (snow depth &gt; 5 cm) = 0.16), and different GNSS receivers (mean r = 0.62). The data set also has high external consistency with the in situ measurements and the passive microwave (PMW) product, with a consistent illustration of the interannual snow depth variability. Additionally, the result show the potential of GNSS to derive hourly snow depth observations for better monitoring of snow disasters. The proposed framework to develop the data set provides comprehensive and supportive information for users to process raw data of ground GNSS stations with complex environmental conditions and various observation conditions. The resulting GSnow-CHINA v1.0 data set is distinguished from the current point-scale in situ data or coarse-gridded data, which can be used as an independent data source for validation purposes. The data set is also useful for regional climate research and other meteorological and hydrological applications. The algorithm and data files will be maintained and updated as more data become available in the future. The GSnow-CHINA v1.0 data set is available at the National Tibetan Plateau/Third Pole Environment Data Center via https://doi.org/10.11888/Cryos.tpdc.271839 (Wan et al., 2021).

The currently available long-term snow depth data sets are either from point-scale ground measurements or from gridded satellite/modeled/reanalysis data with coarse spatial resolution, which limits the applications in climate model, hydrological model, and regional snow disaster monitoring. Benefit from its unique advantages of cost-effective and high spatial-temporal resolution (~ 1000 m2, hourly in theory), snow depth retrieval using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has become a popular topic in recent years. However, due to complex environmental and observation conditions, developing robust and operational technology to produce long-term snow depth data sets using observations from various GNSS station networks is still challenging. The two objectives of this study are 1) to propose a comprehensive framework using raw data of the complex GNSS station networks to retrieve snow depth and to control its quality automatically; and 2) to produce a long-term snow depth data set over northern China (i.e., GSnow-CHINA v1.0, 12 h/24 h, 2013–2020) using the proposed framework and historical data from 80 stations. The data set has high internal consistency with regards to different GNSS systems (mean r = 0.97 & RMSD = 1.93 cm), different frequency bands (mean r = 0.96 & RMSD = 2.73 cm), and different GNSS receivers (mean r = 0.88). The data set also has high external consistency with the in-situ measurements and the passive microwave (PMW) product, with a consistent illustration of the interannual snow depth variability. The results also show the good potential of GNSS to derive hourly snow depth observations for better monitoring snow disasters. The proposed framework to develop the data set provides comprehensive and supportive information for users to process raw data of ground GNSS stations with complex environmental conditions and various observation conditions. The resulting GSnow-CHINA v1.0 data set is distinguished from the current point-scale in-situ data or coarse-gridded data, which can be used as an independent data source for validation purposes. The data set is also useful for regional climate research and other meteorological and hydrological applications. The algorithm and the data files will be maintained and updated as more years of data become available in the future. The GSnow-CHINA v1.0 data set is available at https://doi.org/10.11888/Cryos.tpdc.271839 (Wan et al. 2021).

The currently available long-term snow depth data sets are either from point-scale ground measurements or from gridded satellite/modeled/reanalysis data with coarse spatial resolution, which limits the applications in climate model, hydrological model, and regional snow disaster monitoring. Benefit from its unique advantages of cost-effective and high spatial-temporal resolution (~ 1000 m2, hourly in theory), snow depth retrieval using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique has become a popular topic in recent years. However, due to complex environmental and observation conditions, developing robust and operational technology to produce long-term snow depth data sets using observations from various GNSS station networks is still challenging. The two objectives of this study are 1) to propose a comprehensive framework using raw data of the complex GNSS station networks to retrieve snow depth and to control its quality automatically; and 2) to produce a long-term snow depth data set over northern China (i.e., GSnow-CHINA v1.0, 12 h/24 h, 2013–2020) using the proposed framework and historical data from 80 stations. The data set has high internal consistency with regards to different GNSS systems (mean r = 0.97 & RMSD = 1.93 cm), different frequency bands (mean r = 0.96 & RMSD = 2.73 cm), and different GNSS receivers (mean r = 0.88). The data set also has high external consistency with the in-situ measurements and the passive microwave (PMW) product, with a consistent illustration of the interannual snow depth variability. The results also show the good potential of GNSS to derive hourly snow depth observations for better monitoring snow disasters. The proposed framework to develop the data set provides comprehensive and supportive information for users to process raw data of ground GNSS stations with complex environmental conditions and various observation conditions. The resulting GSnow-CHINA v1.0 data set is distinguished from the current point-scale in-situ data or coarse-gridded data, which can be used as an independent data source for validation purposes. The data set is also useful for regional climate research and other meteorological and hydrological applications. The algorithm and the data files will be maintained and updated as more years of data become available in the future. The GSnow-CHINA v1.0 data set is available at https://doi.org/10.11888/Cryos.tpdc.271839 (Wan et al. 2021).

Guided wave testing has been widely used as an effective way to inspect and monitor structural integrity. Evaluating the performance of damage detection methods is critical for the practical implementation of such methods, especially for long term monitoring systems under complex environmental and operational conditions (EOC). Although damage can be applied to test structures in a laboratory environment to evaluate the performance, it is often prohibitively expensive to comprehensively investigate the sensitivity of damage detection on different damage types, sizes, and locations under different EOC. In this paper, we propose a cost-effective methodology to evaluate the performance and estimate the sensitivity of damage detection methods by synthesizing undamaged records collected under varying EOCs and simulated artificial damage signals. We produce the receiver operating characteristics (ROC) of damage detection methods on different synthetic datasets to predict the performance in practical scenarios. doi: 10.12783/SHM2015/243

Oblique photogrammetry-based three-dimensional (3D) urban models are widely used for smart cities. In 3D urban models, road signs are small but provide valuable information for navigation. However, due to the problems of sliced shape features, blurred texture and high incline angles, road signs cannot be fully reconstructed in oblique photogrammetry, even with state-of-the-art algorithms. The poor reconstruction of road signs commonly leads to less informative guidance and unsatisfactory visual appearance. In this paper, we present a pipeline for embedding road sign models based on deep convolutional neural networks (CNNs). First, we present an end-to-end balanced-learning framework for small object detection that takes advantage of the region-based CNN and a data synthesis strategy. Second, under the geometric constraints placed by the bounding boxes, we use the scale-invariant feature transform (SIFT) to extract the corresponding points on the road signs. Third, we obtain the coarse location of a single road sign by triangulating the corresponding points and refine the location via outlier removal. Least-squares fitting is then applied to the refined point cloud to fit a plane for orientation prediction. Finally, we replace the road signs with computer-aided design models in the 3D urban scene with the predicted location and orientation. The experimental results show that the proposed method achieves a high mAP in road sign detection and produces visually plausible embedded results, which demonstrates its effectiveness for road sign modeling in oblique photogrammetry-based 3D scene reconstruction.

Abstract. Fusion of 3D airborne laser (LIDAR) data and terrestrial optical imagery can be applied in 3D urban modeling and model up-dating. The most challenging aspect of the fusion procedure is registering the terrestrial optical images on the LIDAR point clouds. In this article, we propose an approach for registering these two different data from different sensor sources. As we use iPhone camera images which are taken in front of the interested urban structure by the application user and the high resolution LIDAR point clouds of the acquired by an airborne laser sensor. After finding the photo capturing position and orientation from the iPhone photograph metafile, we automatically select the area of interest in the point cloud and transform it into a range image which has only grayscale intensity levels according to the distance from the image acquisition position. We benefit from local features for registering the iPhone image to the generated range image. In this article, we have applied the registration process based on local feature extraction and graph matching. Finally, the registration result is used for facade texture mapping on the 3D building surface mesh which is generated from the LIDAR point cloud. Our experimental results indicate possible usage of the proposed algorithm framework for 3D urban map updating and enhancing purposes.

ABSTRACTWith the rapid development of photogrammetry, computer vision and three-dimensional (3D) modeling technologies, it is possible to efficiently construct detailed 3D urban models. Accordingly, large corpora of 3D models, such as the Google 3D Warehouse, are now becoming freely available on the web. How to find the proper 3D urban models is a challenging research issue. In this paper, we join shape descriptors and color descriptors for 3D urban model retrieval. The query objects are localized and segmented automatically from the input images by using a new selective search voting algorithm. Through combining the normalization with the light field descriptor, the Horizontal Light Descriptor is introduced to measure the shape similarity among the normalized urban models. The color descriptors are used to represent the color information of the urban models. The two types of descriptors are joined to search 3D urban models similar to the query objects. Experimental results have shown the effectiveness of our approach.