Methodology for Road Defect Detection and Administration Based on Mobile Mapping Data

A detailed inspection of roads requires highly detailed spatial data with sufficient precision to deliver an accurate geometry and to describe road defects visually.This paper presents a novel method for the detection of road defects.The input data for road defect detection included point clouds and orthomosaics gathered by mobile mapping technology.The defects were categorized in three major groups with the following geometric primitives:points,lines and polygons.The method suggests the detection of point objects from matched point clouds,panoramic images and ortho photos.Defects were mapped as point,line or polygon geometries,directly derived from orthomosaics and panoramic images.Besides the geometric position of road defects,all objects were assigned to a variety of attributes:defect type,surface material,center-of-gravity,area,length,corresponding image of the defect and degree of damage.A spatial dataset comprising defect values with a matching data type was created to perform the attribute analysis quickly and correctly.The final product is a spatial vector data set,consisting of points,lines and polygons,which contains attributes with further information and geometry.This paper demonstrates that mobile mapping suits a large-scale feature extraction of road infrastructure defects.By its simplicity and flexibility,the presented methodology allows it to be easily adapted to extract further feature types with their attributes.This makes the proposed approach a vital tool for data extraction settings with multiple mobile mapping data analysts,e.g.,offline crowdsourcing.

Spatio-temporal-spectral-angular observation model that integrates observations from UAV and mobile mapping vehicle for better urban mapping

In a complex urban scene,observation from a single sensor unavoidably leads to voids in observations,failing to describe urban objects in a comprehensive manner.In this paper,we propose a spatio-temporal-spectral-angular observation model to integrate observations from UAV and mobile mapping vehicle platform,realizing a joint,coordinated observation operation from both air and ground.We develop a multi-source remote sensing data acquisition system to effectively acquire multi-angle data of complex urban scenes.Multi-source data fusion solves the missing data problem caused by occlusion and achieves accurate,rapid,and complete collection of holographic spatial and temporal information in complex urban scenes.We carried out an experiment on Baisha Town,Chongqing,China and obtained multi-sensor,multi-angle data from UAV and mobile mapping vehicle.We first extracted the point cloud from UAV and then integrated the UAV and mobile mapping vehicle point cloud.The inte-grated results combined both the characteristics of UAV and mobile mapping vehicle point cloud,confirming the practicability of the proposed joint data acquisition platform and the effectiveness of spatio-temporal-spectral-angular observation model.Compared with the observation from UAV or mobile mapping vehicle alone,the integrated system provides an effective data acquisition solution toward comprehensive urban monitoring.

Non-parametric belief propagation for mobile mapping sensor fusion

Many different forms of sensor fusion have been proposed each with its own niche.We propose a method of fusing multiple different sensor types.Our approach is built on the discrete belief propagation to fuse photogrammetry with GPS to generate three-dimensional(3D)point clouds.We propose using a non-parametric belief propagation similar to Sudderth et al’s work to fuse different sensors.This technique allows continuous variables to be used,is trivially parallel making it suitable for modern many-core processors,and easily accommodates varying types and combinations of sensors.By defining the relationships between common sensors,a graph containing sensor readings can be automatically generated from sensor data without knowing a priori the availability or reliability of the sensors.This allows the use of unreliable sensors which firstly,may start and stop providing data at any time and secondly,the integration of new sensor types simply by defining their relationship with existing sensors.These features allow a flexible framework to be developed which is suitable for many tasks.Using an abstract algorithm,we can instead focus on the relationships between sensors.Where possible we use the existing relationships between sensors rather than developing new ones.These relationships are used in a belief propagation algorithm to calculate the marginal probabilities of the network.In this paper,we present the initial results from this technique and the intended course for future work.

Urban Geospatial Information Acquisition Mobile Mapping System based on close-range photogrammetry and IGS site calibration

The measurement accuracy of the Mobile Mapping System (MMS) is the main problem, which restricts its development and application, so how to calibrate the MMS to improve its measure-ment accuracy has always been a research hotspot in the industry. This paper proposes a position and attitude calibration method with error correction based on the combination of the feature point and feature surface. First, the initial value of the spatial position relation-ship between each sensor of MMS is obtained by close-range photogrammetry. Second, the optimal solution for error correction is calculated by feature points in global coordinates jointly measured with International GNSS Service (IGS) stations. Then, the final transformation para-meters are solved by combining the initial values obtained originally, thereby realizing the rapid calibration of the MMS. Finally, it analyzed the RMSE of MMS point cloud after calibration, and the results demonstrate the feasibility of the calibration approach proposed by this method. Under the condition of a single measurement sensor accuracy is low, the plane and elevation absolute accuracy of the point cloud after calibration can reach 0.043 m and 0.072 m, respectively, and the relative accuracy is smaller than 0.02 m. It meets the precision require-ments of data acquisition for MMS. It is of great significance for promoting the development of MMS technology and the application of some novel techniques in the future, such as auton-omous driving, digital twin city, urban brain et al.

A Low-Cost Mobile Mapping System (LCMMS) for field data acquisition: a potential use to validate aerial/satellite building damage assessment

Among the major natural disasters that occurred in 2010,the Haiti earthquake was a real turning point concerning the availability,dissemination and licensing of a huge quantity of geospatial data.In a few days several map products based on the analysis of remotely sensed data-sets were delivered to users.This demonstrated the need for reliable methods to validate the increasing variety of open source data and remote sensing-derived products for crisis management,with the aim to correctly spatially reference and interconnect these data with other global digital archives.As far as building damage assessment is concerned,the need for accurate field data to overcome the limitations of both vertical and oblique view satellite and aerial images was evident.To cope with the aforementioned need,a newly developed Low-Cost Mobile Mapping System(LCMMS)was deployed in Port-au-Prince(Haiti)and tested during a five-day survey in FebruaryMarch 2010.The system allows for acquisition of movies and single georeferenced frames by means of a transportable device easily installable(or adaptable)to every type of vehicle.It is composed of four webcams with a total field of view of about 180 degrees and one Global Positioning System(GPS)receiver,with the main aim to rapidly cover large areas for effective usage in emergency situations.The main technical features of the LCMMS,the operational use in the field(and related issues)and a potential approach to be adopted for the validation of satellite/aerial building damage assessments are thoroughly described in the article.

Toward the use of smartphones for mobile mapping

This paper considers the use of a low cost mobile device in order to develop a mobile mapping system(MMS),which exploits only sensors embedded in the device.The goal is to make this MMS usable and reliable even in difficult environments(e.g.emergency conditions,when also WiFi connection might not work).For this aim,a navigation system able to deal with the unavailability of the GNSS(e.g.indoors)is proposed first.Positioning is achieved by a pedestrian dead reckoning approach,i.e.a specific particle filter has been designed to enable good position estimations by a small number of particles(e.g.100).This specific characteristic enables its real time use on the standard mobile devices.Then,3D reconstruction of the scene can be achieved by processing multiple images acquired with the standard camera embedded in the device.As most of the vision-based 3D reconstruction systems are recently proposed in the literature,this work considers the use of structure from motion to estimate the geometrical structure of the scene.The detail level of the reconstructed scene is clearly related to the number of images processed by the reconstruction system.However,the execution of a 3D reconstruction algorithm on a mobile device imposes several restrictions due to the limited amount of available energy and computing power.This consideration motivates the search for new methods to obtain similar results with less computational cost.This paper proposes a novel method for feature matching,which allows increasing the number of correctly matched features between two images according to our simulations and can make the matching process more robust.

Estimating underground mine ventilation friction factors from low density 3D data acquired by a moving LiDAR

Ventilation system analysis for underground mines has remained mostly unchanged since the Atkinson method was made popular by Mc Elroy in 1935. Data available to ventilation technicians and engineers is typically limited to the quantity of air moving through any given heading. Because computer-aided modelling, simulation, and ventilation system design tools have improved, it is now important to ensure that developed models have the most accurate information possible. This paper presents a new technique for estimating underground drift friction factors that works by processing 3 D point cloud data obtained by using a mobile Li DAR. Presented are field results that compare the proposed approach with previously published algorithms, as well as with manually acquired measurements.

LD2000 System with 3S and Multi-sensor

This paper introduces a car_borne road information collecting and updating system (LD2000) developed by Wuhan Technical University of Surveying and Mapping.This system is capable of collecting road network information and creating digital road network effectively by means of GPS,GIS and multi_sensor integration.The design and development of LD2000 system are also presented in this paper.

Anonymous crowdsourcing-based WLAN indoor localization

In order to solve the problem of location privacy under big data and improve the user positioning experience,a new concept of anonymous crowdsourcing-based WLAN indoor localization is proposed by employing the Micro-Electro-Mechanical System(MEMS)motion sensors as well as WLAN module in off-the-shelf smartphones.First of all,the crowdsourced motion traces with similar Received Signal Strength(RSS)sequences are assembled into a motion graph.Second,the mobility map is constructed according to traces segmentation and clustering.Third,the pixel template matching is adopted to physically label the pre-constructed mobility map.Finally,the robust Extended Kalman Filter(EKF)is designed to perform localization by matching the newly-collected RSS measurements against the mobility map.The extensive experimental results show that the proposed approach is capable of constructing a physically-labeled mobility map from the sporadically-collected crowdsourced motion traces as well as achieving satisfactory localization accuracy in a cost-efficient manner.

Coupling ground-level panoramas and aerial imagery for change detection

Geographic landscapes in all over the world may be subject to rapid changes induced,for instance,by urban,forest,and agricultural evolutions.Monitoring such kind of changes is usually achieved through remote sensing.However,obtaining regular and up-to-date aerial or satellite images is found to be a high costly process,thus preventing regular updating of land cover maps.Alternatively,in this paper,we propose a low-cost solution based on the use of groundlevel geo-located landscape panoramic photos providing high spatial resolution information of the scene.Such photos can be acquired from various sources:digital cameras,smartphone,or even web repositories.Furthermore,since the acquisition is performed at the ground level,the users’immediate surroundings,as sensed by a camera device,can provide information at a very high level of precision,enabling to update the land cover type of the geographic area.In the described herein method,we propose to use inverse perspective mapping(inverse warping)to transform the geo-tagged ground-level 360◦photo onto a top-down view as if it had been acquired from a nadiral aerial view.Once re-projected,the warped photo is compared to a previously acquired remotely sensed image using standard techniques such as correlation.Wide differences in orientation,resolution,and geographical extent between the top-down view and the aerial image are addressed through specific processing steps(e.g.registration).Experiments on publicly available data-sets made of both ground-level photos and aerial images show promising results for updating land cover maps with mobile technologies.Finally,the proposed approach contributes to the crowdsourcing efforts in geo-information processing and mapping,providing hints on the evolution of a landscape.

Modelling the effect of non-pharmaceutical interventions on COVID-19 transmission from mobility maps

The world has faced the COVID-19 pandemic for over two years now,and it is time to revisit the lessons learned from lockdown measures for theoretical and practical epidemiological improvements.The interlink between these measures and the resulting change in mobility(a predictor of the disease transmission contact rate)is uncertain.We thus propose a new method for assessing the efficacy of various non-pharmaceutical interventions(NPI)and examine the aptness of incorporating mobility data for epidemiological modelling.Facebook mobility maps for the United Arab Emirates are used as input datasets from the first infection in the country to mid-Oct 2020.Dataset was limited to the pre-vaccination period as this paper focuses on assessing the different NPIs at an early epidemic stage when no vaccines are available and NPIs are the only way to reduce the reproduction number(R_(0)).We developed a travel network density parameterβ_(t)to provide an estimate of NPI impact on mobility patterns.Given the infection-fatality ratio and time lag(onset-to-death),a Bayesian probabilistic model is adapted to calculate the change in epidemic development withβt.Results showed that the change inβ_(t)clearly impacted R_(0).The three lockdowns strongly affected the growth of transmission rate and collectively reduced R_(0)by 78%before the restrictions were eased.The model forecasted daily infections and deaths by 2%and 3%fractional errors.It also projected what-if scenarios for different implementation protocols of each NPI.The developed model can be applied to identify the most efficient NPIs for confronting new COVID-19 waves and the spread of variants,as well as for future pandemics.