scholarly journals CENTIMETRE-ACCURACY IN FORESTS AND URBAN CANYONS – COMBINING A HIGH-PERFORMANCE IMAGE-BASED MOBILE MAPPING BACKPACK WITH NEW GEOREFERENCING METHODS

2020 ◽  
Vol V-1-2020 ◽  
pp. 333-341
Author(s):  
S. Blaser ◽  
J. Meyer ◽  
S. Nebiker ◽  
L. Fricker ◽  
D. Weber
Keyword(s):  
Web Services ◽  
High Performance ◽  
Large Scale ◽  
Superior Performance ◽  
City Centre ◽  
Visual Localization ◽  
Mobile Mapping ◽  
Coordinate Measurement ◽  
Collaborative Processes ◽  
Mobile Mapping System

Abstract. Advances in digitalization technologies lead to rapid and massive changes in infrastructure management. New collaborative processes and workflows require detailed, accurate and up-to-date 3D geodata. Image-based web services with 3D measurement functionality, for example, transfer dangerous and costly inspection and measurement tasks from the field to the office workplace. In this contribution, we introduced an image-based backpack mobile mapping system and new georeferencing methods for capture previously inaccessible outdoor locations. We carried out large-scale performance investigations at two different test sites located in a city centre and in a forest area. We compared the performance of direct, SLAM-based and image-based georeferencing under demanding real-world conditions. Both test sites include areas with restricted GNSS reception, poor illumination, and uniform or ambiguous geometry, which create major challenges for reliable and accurate georeferencing. In our comparison of georeferencing methods, image-based georeferencing improved the median precision of coordinate measurement over direct georeferencing by a factor of 10–15 to 3 mm. Image-based georeferencing also showed a superior performance in terms of absolute accuracies with results in the range from 4.3 cm to 13.2 cm. Our investigations showed a great potential for complementing 3D image-based geospatial web-services of cities as well as for creating such web services for forest applications. In addition, such accurately georeferenced 3D imagery has an enormous potential for future visual localization and augmented reality applications.

scholarly journals OPEN URBAN AND FOREST DATASETS FROM A HIGH-PERFORMANCE MOBILE MAPPING BACKPACK – A CONTRIBUTION FOR ADVANCING THE CREATION OF DIGITAL CITY TWINS

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 125-131
Author(s):  
S. Blaser ◽  
J. Meyer ◽  
S. Nebiker
Keyword(s):  
High Performance ◽  
City Centre ◽  
Data Sets ◽  
High Quality ◽  
Mobile Mapping ◽  
Street Level ◽  
Localization And Mapping ◽  
Digital Twins ◽  
Potential Applications ◽  
The Creation

Abstract. With this contribution, we describe and publish two high-quality street-level datasets, captured with a portable high-performance Mobile Mapping System (MMS). The datasets will be freely available for scientific use. Both datasets, from a city centre and a forest represent area-wide street-level reality captures which can be used e.g. for establishing cloud-based frameworks for infrastructure management as well as for smart city and forestry applications. The quality of these data sets has been thoroughly evaluated and demonstrated. For example, georeferencing accuracies in the centimetre range using these datasets in combination with image-based georeferencing have been achieved. Both high-quality multi sensor system street-level datasets are suitable for evaluating and improving methods for multiple tasks related to high-precision 3D reality capture and the creation of digital twins. Potential applications range from localization and georeferencing, dense image matching and 3D reconstruction to combined methods such as simultaneous localization and mapping and structure-from-motion as well as classification and scene interpretation. Our dataset is available online at: https://www.fhnw.ch/habg/bimage-datasets

scholarly journals FAST DRAWING OF TRAFFIC SIGN USING MOBILE MAPPING SYSTEM

2016 ◽  
Vol XLI-B3 ◽  
pp. 937-944
Author(s):  
Q. Yao ◽  
B. Tan ◽  
Y. Huang
Keyword(s):  
Asset Management ◽  
Large Scale ◽  
Rare Event ◽  
Drawing Process ◽  
Traffic Sign ◽  
Mobile Mapping ◽  
Traffic Signs ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Anchor Points

Traffic sign provides road users with the specified instruction and information to enhance traffic safety. Automatic detection of traffic sign is important for navigation, autonomous driving, transportation asset management, etc. With the advance of laser and imaging sensors, Mobile Mapping System (MMS) becomes widely used in transportation agencies to map the transportation infrastructure. Although many algorithms of traffic sign detection are developed in the literature, they are still a tradeoff between the detection speed and accuracy, especially for the large-scale mobile mapping of both the rural and urban roads. This paper is motivated to efficiently survey traffic signs while mapping the road network and the roadside landscape. Inspired by the manual delineation of traffic sign, a drawing strategy is proposed to quickly approximate the boundary of traffic sign. Both the shape and color prior of the traffic sign are simultaneously involved during the drawing process. The most common speed-limit sign circle and the statistic color model of traffic sign are studied in this paper. Anchor points of traffic sign edge are located with the local maxima of color and gradient difference. Starting with the anchor points, contour of traffic sign is drawn smartly along the most significant direction of color and intensity consistency. The drawing process is also constrained by the curvature feature of the traffic sign circle. The drawing of linear growth is discarded immediately if it fails to form an arc over some steps. The Kalman filter principle is adopted to predict the temporal context of traffic sign. Based on the estimated point,we can predict and double check the traffic sign in consecutive frames.The event probability of having a traffic sign over the consecutive observations is compared with the null hypothesis of no perceptible traffic sign. The temporally salient traffic sign is then detected statistically and automatically as the rare event of having a traffic sign.The proposed algorithm is tested with a diverse set of images that are taken inWuhan, China with theMMS ofWuhan University. Experimental results demonstrate that the proposed algorithm can detect traffic signs at the rate of over 80% in around 10 milliseconds. It is promising for the large-scale traffic sign survey and change detection using the mobile mapping system.

scholarly journals Automatic 3D City Modeling Using a Digital Map and Panoramic Images from a Mobile Mapping System

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Hyungki Kim ◽  
Yuna Kang ◽  
Soonhung Han
Keyword(s):  
Virtual Reality ◽  
Visual Information ◽  
Large Scale ◽  
Mobile Mapping ◽  
3D Navigation ◽  
Digital Map ◽  
Mapping System ◽  
City Modeling ◽  
Mobile Mapping System ◽  
City Models

Three-dimensional city models are becoming a valuable resource because of their close geospatial, geometrical, and visual relationship with the physical world. However, ground-oriented applications in virtual reality, 3D navigation, and civil engineering require a novel modeling approach, because the existing large-scale 3D city modeling methods do not provide rich visual information at ground level. This paper proposes a new framework for generating 3D city models that satisfy both the visual and the physical requirements for ground-oriented virtual reality applications. To ensure its usability, the framework must be cost-effective and allow for automated creation. To achieve these goals, we leverage a mobile mapping system that automatically gathers high-resolution images and supplements sensor information such as the position and direction of the captured images. To resolve problems stemming from sensor noise and occlusions, we develop a fusion technique to incorporate digital map data. This paper describes the major processes of the overall framework and the proposed techniques for each step and presents experimental results from a comparison with an existing 3D city model.

scholarly journals ProTstab – predictor for cellular protein stability

BMC Genomics ◽  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Yang Yang ◽  
Xuesong Ding ◽  
Guanchen Zhu ◽  
Abhishek Niroula ◽  
Qiang Lv ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Sequence Data ◽  
Limited Proteolysis ◽  
Absolute Error ◽  
Cellular Protein ◽  
Superior Performance ◽  
Gradient Boosting ◽  
Protein Chain ◽  
Blind Test

Abstract Background Stability is one of the most fundamental intrinsic characteristics of proteins and can be determined with various methods. Characterization of protein properties does not keep pace with increase in new sequence data and therefore even basic properties are not known for far majority of identified proteins. There have been some attempts to develop predictors for protein stabilities; however, they have suffered from small numbers of known examples. Results We took benefit of results from a recently developed cellular stability method, which is based on limited proteolysis and mass spectrometry, and developed a machine learning method using gradient boosting of regression trees. ProTstab method has high performance and is well suited for large scale prediction of protein stabilities. Conclusions The Pearson’s correlation coefficient was 0.793 in 10-fold cross validation and 0.763 in independent blind test. The corresponding values for mean absolute error are 0.024 and 0.036, respectively. Comparison with a previously published method indicated ProTstab to have superior performance. We used the method to predict stabilities of all the remaining proteins in the entire human proteome and then correlated the predicted stabilities to protein chain lengths of isoforms and to localizations of proteins.

Direct large-scale synthesis of 3D hierarchical mesoporous NiO microspheres as high-performance anode materials for lithium ion batteries

Nanoscale ◽  
2014 ◽  
Vol 6 (6) ◽  
pp. 3268-3273 ◽  
Author(s):  
Zhongchao bai ◽  
Zhicheng Ju ◽  
Chunli Guo ◽  
Yitai Qian ◽  
Bin Tang ◽  
...  
Keyword(s):  
Thermal Decomposition ◽  
Lithium Ion Batteries ◽  
Anode Materials ◽  
Decomposition Method ◽  
High Performance ◽  
Large Scale ◽  
Lithium Ion ◽  
Superior Performance ◽  
Thermal Decomposition Method ◽  
Large Scale Synthesis

3D hierarchical mesoporous NiO microspheres were scalably synthesized by a thermal decomposition method; they exhibit superior performance as anode materials for LIBs.

scholarly journals FAST DRAWING OF TRAFFIC SIGN USING MOBILE MAPPING SYSTEM

2016 ◽  
Vol XLI-B3 ◽  
pp. 937-944
Author(s):  
Q. Yao ◽  
B. Tan ◽  
Y. Huang
Keyword(s):  
Asset Management ◽  
Large Scale ◽  
Rare Event ◽  
Drawing Process ◽  
Traffic Sign ◽  
Mobile Mapping ◽  
Traffic Signs ◽  
Mapping System ◽  
Mobile Mapping System ◽  
Anchor Points

Traffic sign provides road users with the specified instruction and information to enhance traffic safety. Automatic detection of traffic sign is important for navigation, autonomous driving, transportation asset management, etc. With the advance of laser and imaging sensors, Mobile Mapping System (MMS) becomes widely used in transportation agencies to map the transportation infrastructure. Although many algorithms of traffic sign detection are developed in the literature, they are still a tradeoff between the detection speed and accuracy, especially for the large-scale mobile mapping of both the rural and urban roads. This paper is motivated to efficiently survey traffic signs while mapping the road network and the roadside landscape. Inspired by the manual delineation of traffic sign, a drawing strategy is proposed to quickly approximate the boundary of traffic sign. Both the shape and color prior of the traffic sign are simultaneously involved during the drawing process. The most common speed-limit sign circle and the statistic color model of traffic sign are studied in this paper. Anchor points of traffic sign edge are located with the local maxima of color and gradient difference. Starting with the anchor points, contour of traffic sign is drawn smartly along the most significant direction of color and intensity consistency. The drawing process is also constrained by the curvature feature of the traffic sign circle. The drawing of linear growth is discarded immediately if it fails to form an arc over some steps. The Kalman filter principle is adopted to predict the temporal context of traffic sign. Based on the estimated point,we can predict and double check the traffic sign in consecutive frames.The event probability of having a traffic sign over the consecutive observations is compared with the null hypothesis of no perceptible traffic sign. The temporally salient traffic sign is then detected statistically and automatically as the rare event of having a traffic sign.The proposed algorithm is tested with a diverse set of images that are taken inWuhan, China with theMMS ofWuhan University. Experimental results demonstrate that the proposed algorithm can detect traffic signs at the rate of over 80% in around 10 milliseconds. It is promising for the large-scale traffic sign survey and change detection using the mobile mapping system.

scholarly journals TDCMR: Triplet-Based Deep Cross-Modal Retrieval for Geo-Multimedia Data

2021 ◽  
Vol 11 (22) ◽  
pp. 10803
Author(s):  
Jiagang Song ◽  
Yunwu Lin ◽  
Jiayu Song ◽  
Weiren Yu ◽  
Leyuan Zhang
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Location Based Services ◽  
Multimedia Data ◽  
Multimedia Retrieval ◽  
Geographical Information ◽  
Superior Performance ◽  
Hybrid Index ◽  
High Level ◽  
Hash Codes

Mass multimedia data with geographical information (geo-multimedia) are collected and stored on the Internet due to the wide application of location-based services (LBS). How to find the high-level semantic relationship between geo-multimedia data and construct efficient index is crucial for large-scale geo-multimedia retrieval. To combat this challenge, the paper proposes a deep cross-modal hashing framework for geo-multimedia retrieval, termed as Triplet-based Deep Cross-Modal Retrieval (TDCMR), which utilizes deep neural network and an enhanced triplet constraint to capture high-level semantics. Besides, a novel hybrid index, called TH-Quadtree, is developed by combining cross-modal binary hash codes and quadtree to support high-performance search. Extensive experiments are conducted on three common used benchmarks, and the results show the superior performance of the proposed method.

scholarly journals Enhanced Potassium-Ion Storage of the 3D Carbon Superstructure by Manipulating the Nitrogen-Doped Species and Morphology

2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Yanhua Li ◽  
Kui Xiao ◽  
Cong Huang ◽  
Jin Wang ◽  
Ming Gao ◽  
...  
Keyword(s):  
Energy Storage ◽  
High Performance ◽  
Density Functional ◽  
Large Scale ◽  
Specific Capacity ◽  
Rate Capability ◽  
High Energy ◽  
Potassium Ion ◽  
High Energy Density ◽  
Superior Performance

Abstract Potassium-ion batteries (PIBs) are attractive for grid-scale energy storage due to the abundant potassium resource and high energy density. The key to achieving high-performance and large-scale energy storage technology lies in seeking eco-efficient synthetic processes to the design of suitable anode materials. Herein, a spherical sponge-like carbon superstructure (NCS) assembled by 2D nanosheets is rationally and efficiently designed for K+ storage. The optimized NCS electrode exhibits an outstanding rate capability, high reversible specific capacity (250 mAh g−1 at 200 mA g−1 after 300 cycles), and promising cycling performance (205 mAh g−1 at 1000 mA g−1 after 2000 cycles). The superior performance can be attributed to the unique robust spherical structure and 3D electrical transfer network together with nitrogen-rich nanosheets. Moreover, the regulation of the nitrogen doping types and morphology of NCS-5 is also discussed in detail based on the experiments results and density functional theory calculations. This strategy for manipulating the structure and properties of 3D materials is expected to meet the grand challenges for advanced carbon materials as high-performance PIB anodes in practical applications.

scholarly journals Video-based Mobile Mapping System Using Smartphones

2014 ◽  
Vol XL-1 ◽  
pp. 13-18 ◽  
Author(s):  
A. Al-Hamad ◽  
A. Moussa ◽  
N. El-Sheimy
Keyword(s):  
Spatial Data ◽  
Large Scale ◽  
Low Cost ◽  
Video Camera ◽  
Mobile Mapping ◽  
Gis Applications ◽  
Mobile Mapping System ◽  
The Cost ◽  
Mapping Solution ◽  
Mapping Systems

The last two decades have witnessed a huge growth in the demand for geo-spatial data. This demand has encouraged researchers around the world to develop new algorithms and design new mapping systems in order to obtain reliable sources for geo-spatial data. Mobile Mapping Systems (MMS) are one of the main sources for mapping and Geographic Information Systems (GIS) data. MMS integrate various remote sensing sensors, such as cameras and LiDAR, along with navigation sensors to provide the 3D coordinates of points of interest from moving platform (e.g. cars, air planes, etc.). Although MMS can provide accurate mapping solution for different GIS applications, the cost of these systems is not affordable for many users and only large scale companies and institutions can benefits from MMS systems. <br><br> The main objective of this paper is to propose a new low cost MMS with reasonable accuracy using the available sensors in smartphones and its video camera. Using the smartphone video camera, instead of capturing individual images, makes the system easier to be used by non-professional users since the system will automatically extract the highly overlapping frames out of the video without the user intervention. Results of the proposed system are presented which demonstrate the effect of the number of the used images in mapping solution. In addition, the accuracy of the mapping results obtained from capturing a video is compared to the same results obtained from using separate captured images instead of video.

scholarly journals A 3D MAP AIDED DEEP LEARNING BASED INDOOR LOCALIZATION SYSTEM FOR SMART DEVICES

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 391-397
Author(s):  
Y. Yang ◽  
C. Toth ◽  
D. Brzezinska
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Large Scale ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Location Based Services ◽  
Smart Devices ◽  
Visual Localization ◽  
Indoor Location ◽  
Semantic Labeling

Abstract. Indoor positioning technologies represent a fast developing field of research due to the rapidly increasing need for indoor location-based services (ILBS); in particular, for applications using personal smart devices. Recently, progress in indoor mapping, including 3D modeling and semantic labeling started to offer benefits to indoor positioning algorithms; mainly, in terms of accuracy. This work presents a method for efficient and robust indoor localization, allowing to support applications in large-scale environments. To achieve high performance, the proposed concept integrates two main indoor localization techniques: Wi-Fi fingerprinting and deep learning-based visual localization using 3D map. The robustness and efficiency of technique is demonstrated with real-world experiences.

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