Mobile Mapping Research Articles

Improving Older Adults’ Technology Adoption on Mobile Map: A Gamification Approach

In the era of digitalization, it is critical to improve older adults’ digital literacy and inclusion by enhancing their technology adoption to create a more accessible and inclusive social environment. However, older adults always exhibit lower levels of technology usage, and there is insufficient support available to meet their specific needs. Gamification, with its potential to motivate people, presents a great opportunity to address the issue of technology adoption among older adults. Despite gamification being predominantly applied to younger generations, this study offers a unique contribution by examining the impact of gamification on supporting older adults’ technology adoption. A case study of tailor-made gamification for Google Maps was constructed based on the game-based learning model and game mechanics. The effectiveness of the proposed gamification strategy has been assessed through interviews and observation. The results revealed a significant positive effect of gamification on enhancing older adults’ technology knowledge and adoption intentions.

Personalising mobile maps with AI for better fit

Personalising mobile maps with AI for better fit

CLOUDSPAM: Contrastive Learning On Unlabeled Data for Segmentation and Pre-Training Using Aggregated Point Clouds and MoCo

SegContrast TEST paved the way for contrastive learning on outdoor point clouds. Its original formulation targeted individual scans in applications like autonomous driving and object detection. However, mobile mapping purposes such as digital twin cities and urban planning require large-scale dense datasets to capture the full complexity and diversity present in outdoor environments. In this paper, the SegContrast method is revisited and adapted to overcome its limitations associated with mobile mapping datasets, namely the scarcity of contrastive pairs and memory constraints. To overcome the scarcity of contrastive pairs, we propose the merging of heterogeneous datasets. However, this merging is not a straightforward procedure due to the variety of size and number of points in the point clouds of these datasets. Therefore, a data augmentation approach is designed to create a vast number of segments while optimizing the size of the point cloud samples to the allocated memory. This methodology, called CLOUDSPAM, guarantees the performance of the self-supervised model for both small- and large-scale mobile mapping point clouds. Overall, the results demonstrate the benefits of utilizing datasets with a wide range of densities and class diversity. CLOUDSPAM matched the state of the art on the KITTI-360 dataset, with a 63.6% mIoU, and came in second place on the Toronto-3D dataset. Finally, CLOUDSPAM achieved competitive results against its fully supervised counterpart with only 10% of labeled data.

Audio-Vibratory You-Are-Here Mobile Maps for People with Visual Impairments

Self-localization and wayfinding are challenging tasks for people with visual impairments (PVIs), severely impacting independent mobility. Visual “You-are-here” (YAH) maps are useful for assisting local wayfinding of sighted users. They are used to self-localize and display points of interest, landmarks and routes in the surroundings. However, these maps are not always available and rarely accessible to PVIs. Relying on an iterative participatory design process with eight end-users with visual impairments, we created a proof of concept of a mobile audio-vibratory YAH map. Our design is based on either a tablet or a smartphone to ensure a small and portable solution. A user study with ten PVIs showed that the audio-vibratory YAH map that we designed provides the user with a good understanding of the surroundings and wayfinding cues. Surprisingly, the results show that the audio-vibratory YAH map prototype was as usable as the control condition (audio-tactile YAH map with a tactile overlay), with similar user satisfaction and cognitive load. A follow-up field study with two participants showed the effectiveness of the prototype for assisting in crossroad understanding. To conclude, our innovative design of a mobile audio-vibratory YAH map can overcome the portability and printing issues associated with tactile overlays and can be an appropriate solution for assisting the pedestrian navigation of PVIs.

Control Analysis of Building Under Construction from Indoor Mobile Mapping Systems

Abstract. During the execution phase of construction projects, quality defects arise due to various factors such as human intervention, technical aspects, logistical issues and environmental influences. Early detection of these defects is imperative to minimize their impact and prevent possible delays, increased costs and safety hazards. This paper explores the effectiveness of indoor mobile laser scanners for geometric quality control, adhering to established European technical norms and Spanish standards. Our developed approach presents a simple method for rapid assessment, capable of identifying vertical deviations in columns and walls, axis deviation in columns, flatness deviation in horizontal planes and level deviation using LIDAR data from Mobile Mapping Systems. This methodology has been validated through a real-world case study, demonstrating its practical applicability.

Strong Robust LIO System Based on Event Camera Assistance

Abstract. At present, the LiDAR system can achieve high precision and is extensively used for indoor and outdoor mobile positioning and mapping. However, LiDAR systems still face issues in cluttered environments where strong features are absent, leading to a degradation of the LiDAR-based solution. When the carrier movement involves high-speed or prolonged exposure to the mirror wall, it can cause severe degradation issues or even positioning failures in the laser slam system. Event cameras are vision sensors inspired by biology that exhibit strong robustness in high dynamic and low texture environments, potentially leading to better performance in such environments. However, some issues with event cameras remain unresolved. In this paper, a multi-source fusion method based on EVIO, LIO and IMU trajectory layer post-processing method is proposed, which will fully consider the robustness of event camera in high dynamic environment and the high precision advantage of Lidar in conventional environment, and use an algorithm based on normalized uncertainty. The elastic multi-source fusion of event camera and LiDAR is realized and tested in realde environment. Experimental results show that the proposed algorithm can effectively improve the accuracy of event camera and LiDAR. Compared to the current more advanced algorithms, the proposed algorithm effectively addresses the problem of LIO in degraded environments. Additionally, it mitigates the scale inaccuracy and divergence of the EVIO trajectory to some extent. Compared to LIO, the algorithm can reduce the maximum position error by approximately 30% and increase the overall position accuracy by 32%. Additionally, it can significantly constrain the divergence of errors in the Y direction, improving its accuracy by about 75% and 65% compared to the LIO and EVIO algorithms, respectively.

Evaluating Mobile LiDAR Intensity Data for Inventorying Durable Tape Pavement Markings.

Good visibility of lane markings is important for all road users, particularly autonomous vehicles. In general, nighttime retroreflectivity is one of the most challenging marking visibility characteristics for agencies to monitor and maintain, particularly in cold weather climates where agency snowplows remove retroreflective material during winter operations. Traditional surface-applied paint and glass beads typically only last one season in cold weather climates with routine snowplow activity. Recently, transportation agencies in cold weather climates have begun deploying improved recessed, durable pavement markings that can last several years and have very high retroreflective properties. Several dozen installations may occur in a state in any calendar year, presenting a challenge for states that need to program annual repainting of traditional waterborne paint lines, but not paint over the much more costly durable markings. This study reports on the utilization of mobile mapping LiDAR systems to classify and evaluate pavement markings along a 73-mile section of westbound I-74 in Indiana. LiDAR intensity data can be used to classify pavement markings as either tape or non-tape and then identify areas of tape markings that need maintenance. RGB images collected during LiDAR intensity data collection were used to validate the LiDAR classification. These techniques can be used by agencies to develop accurate pavement marking inventories to ensure that only painted lines (or segments with missing tape) are repainted during annual maintenance. Repeated tests can also track the marking intensity over time, allowing agencies to better understand material lifecycles.

Beyond Navigation: Exploring Users' Contextual Information Needs and Concerns When Interacting with Mobile Map Apps

ABSTRACTMobile map applications greatly facilitate users' everyday information practices and provide them with real‐time and accurate information in different contexts. However, few studies have explored the contextual information needs of users in their interactions with mobile maps. Semi‐structured interviews with 18 participants revealed that users present diverse and rich information needs in their everyday use of map applications, and in addition to the most common information needs for situ navigation, users' contextual information needs varied. Particularly, mobile map applications are increasingly characterized by serious leisure while highlighting their utility. In addition, with the evolution of the platformization of map applications, users are also demanding more autonomy, visibility, and security. This study contributes to the literature on human‐information interaction in mobile maps and sheds light on the user experience design of mobile map applications.

Metagenomics reveals the characteristics and potential spread of microbiomes and virulence factor genes in the dairy cattle production system

Virulence factor genes (VFGs) pose a potential threat to ecological security and animal health, and have attracted increasing attention in the livestock industry. As one of the primary livestock types, dairy cattle may be an important source of VFG transmission. However, the distribution, transmission, and evolution of VFGs in the gastrointestinal tract and surrounding environment of dairy cattle remain unclear. In the present study, a total of 263 samples were collected from cows, calves, colostrum, farm wastewater, and soil. Metagenomics was conducted to analyze changes in the microbiome and VFGs characteristics in these ecological niches. The VFGs of the cows showed distinct differences between the rumen and feces, and were influenced by the region. The dominant VFG hosts was regulated by their microbial structure. Colostrum administration of cows increased VFG abundance in their newborn calf feces sharply and Enterobacteriaceae became the primary host. While diet was the primary driving force for the temporal variation in calf VFGs. For samples of the surrounding environment, water and soil had higher VFG concentrations and were more structurally stable. Moreover, extensive interactions between the mobile genetic elements and VFGs and gene mobile analysis map based on metagenomic binning both displayed the potential horizontal transfer ability of VFGs in the cows and environment. Our study revealed the prevalence, diffusion, and regulatory factors of VFGs in dairy cattle production systems, providing novel insights into reducing livestock VFGs and limiting their spread.

Efficient and low-drift point-cloud mapping method for GNSS weak and obstructed areas

Abstract Road modelling based on point-cloud data often encounters signal-rejection areas, where the absence of absolute position constraints leads to rapid error accumulation in the constructed map. To mitigate error growth, the manual setting of control points is necessary, which compromises automation in the mapping process and causes a challenging trade-off between efficiency and accuracy. To address these issues, in the present study, we designed a fast and efficient map-construction method that provides absolute pose constraints for simultaneous localisation and mapping (SLAM) based on the bidirectional filter position of global navigation satellite system (GNSS)/inertial measurement unit (IMU) integrated navigation. First, we designed an absolute position constraint factor (BF_Factor) updated at each epoch according to the reference-position difference and time-delay error, which can significantly mitigate error divergence during mobile mapping in a GNSS-rejected region. An adaptive noise model was then constructed for the above factors so that the weights of different factors could be adjusted automatically to achieve a more accurate mapping result. Finally, we conducted experimental tests on the proposed mapping approach in two scenarios that included multiple GNSS-rejected areas. Even when several hundred metres of GNSS signal-obstructed areas were present in the scene, the relocation results indicated that the three-dimensional position error obtained using point-cloud maps was <0.15 m and the pitch/roll error was better than 2°. Compared with Fastlio2 and Fastlio2-SC (Fastlio2 with Scan Context), the proposed method had a better mapping effect and higher mapping accuracy in the aforementioned scenarios. The method proposed in this paper can reduce the human cost in traditional modeling and can provide more rapid and accurate map for related industry applications. The method proposed in this paper can reduce the human cost in traditional modeling and can provide more rapid and accurate map for related industry applications.

Exploring the Use of Landmarks to Aid Pedestrian Wayfinding

When wayfinding with mobile maps, the acquisition of spatial knowledge can be limited by relying on automated route instructions and the small map on the screen. Repeating a route without support from navigation aids may be challenging as the user has been focused on their mobile phone and not the surroundings. According to theories of spatial knowledge acquisition, landmarks are environmental elements important in the development of such knowledge. This study examines how different navigation aids impact spatial knowledge, with a particular focus on landmarks. Participants navigated a route using one of three aids: a booklet of sequential landmarks, a booklet of Google Maps screenshots, or a paper map. The landmarks were selected based on their conspicuity and strategic placement along the route. Thirty participants completed tasks assessing their spatial knowledge of the route and surrounding area after walking it. This study, divided into three phases, compared the effectiveness of each navigation aid, highlighting the effectiveness of landmarks in wayfinding. Results indicate that landmarks significantly enhance wayfinding, especially for pedestrians navigating short, pre-set routes without a map. This suggests that incorporating landmarks into mobile maps could improve on-screen spatial information.

Future opportunities for mobile thematic map design in data journalism

Future opportunities for mobile thematic map design in data journalism

Optimizing mobile map reading: An adaptive map approach in response to dynamic pedestrian context

Optimizing mobile map reading: An adaptive map approach in response to dynamic pedestrian context

Editorial on Special Issue “3D Reconstruction and Mobile Mapping in Urban Environments Using Remote Sensing”

Both 3D reconstruction and mobile mapping are critical in supporting various applications in urban environments, including but not limited to autonomous driving, smart logistics, pedestrian navigation, and virtual reality [...]

해양 사고 안전을 위한 면상발열체 기반의 위치추적 복합기를 구비한 융합 웨어러블슈트 구현 - 저체온과 저온 화상 방지를 위한 융합 웨어러블 슈트

This study aims to develop an amphibious wearable suit that combines a carbon nano heating fabric and a location tracking device for the prevention and safety of the human body's hypothermia in case of marine accidents. To prevent hypothermia, we develop a fusion wearable suit based on design production technology according to the far-infrared emission of planar heating elements, safety through far-infrared emission and strain experiments, and RF communication necessary for location tracking in the ocean. In particular, it uses a waterproof integrated battery and connection cable that can be charged by USB and enables location tracking of complex transmitters around the world using mobile SOS maps. Marine accidents often lead to death due to extreme hypothermia, and suits caused by conventional heating wires have complained of skin damage due to low-temperature burns on the skin. Overcoming this problem, we manufacture a fusion wearable suit that can expand the market to athleisure outdoor clothing through the development of Stylish & Trendy designs. Existing heating wires secure far-infrared effects and antibacterial properties by manufacturing planar heating elements woven with nanocarbon components. These nanocarbon planar heating elements are effective in preventing low and low-temperature burns. In addition, general-purpose low-current batteries and waterproof connection cables are used to provide convenience to users, and weight reduction and usage time are expanded. In particular, the marine area of Korea is 4.4 times that of the land change, and it is very difficult for a small number of maritime police to control a wide area. In the event of a marine accident, the cost and economic loss of the patrol boat, helicopter, Coast Guard personnel, and attached infrastructure to the accident point are very large. Therefore, for accurate location tracking, we propose a complex receiver module capable of RF communication and satellite communication. Recently, the Coast Guard has installed a large number of receiving repeaters to enable the social safety net linkage service to the Mobile SOS Map. As marine leisure activities and sports activities expand, marine accidents are also expanding. After the accident, the death accident due to hypothermia could be experienced through the overturning of the Japanese Hugaido tourist ship. It is very important to secure the safety of accidents and divers before and after accidents. The results of this study can secure a social safety consensus by supporting survivors and solving the life support problem of divers. In particular, by fusing RF communication and satellite communication complex receivers for location tracking functions, police patrol ships, divers mobilization, and multiple units' costs are reduced when an accident is dispatched, and accurate location tracking is possible. Therefore, it is possible to transmit compatible RFs and secure GPS functions through a number of RF transceiver conversion rescue equipment installed in the Navy and Coast Guard (instrumental ships and safety relief ships) on an area 4.4 times the land, and to suppress deaths due to hypothermia during Golden Time. In addition, accidents caused by the spread of leisure sports activities can be minimized through linkage with the Mobile SOS social safety net.

Towards automatic urban tree inventory: Enhancing tree instance segmentation via moving object removal and a chord length-based DBH estimation approach

To enhance urban forestry efficacy in Hong Kong, implementing a paradigm shift towards an automated urban tree inventory that utilizes advanced sensing technologies and artificial intelligence is essential for streamlined data collection and analysis. This study advances this objective by creating a comprehensive framework for estimating diameter at breast height (DBH) and extracting tree images. This framework encompasses five key stages: (1) data acquisition utilizing StructXray, a mobile mapping system equipped with a 360° camera and a multi-beam flash LiDAR sensor; (2) vegetation point clouds extraction using deep learning techniques; (3) individual tree segmentation through machine learning algorithms; (4) DBH estimation; and (5) tree image extraction. Six datasets were collected, yielding tree detection precision, recall and F1 score of 0.88, 0.95 and 0.91 respectively. The presence of moving objects within the 3D point cloud map, exhibiting diverse geometric structures, hinders precise vegetation point cloud segmentation by the pointwise neural network. To tackle this challenge, SalsaNext was employed to rectify the predictions of a pointwise neural network, specifically RandLA-Net in this study, eliminating 91 % of misclassified moving object point clouds and completely removing them from 47 % of affected individual tree point clouds. Additionally, a chord length-based method was proposed to enhance DBH estimation accuracy by dividing the point cloud slice into sectors and summing the chord lengths to estimate the tree trunk perimeter. Compared to the ellipse least squares fitting method, this approach reduced the root-mean-square error of the estimated DBH by 1.31 cm.

Inequities in food access during the COVID19 pandemic: a multilevel, mixed methods pilot study.

Innovative data integration may serve to inform rapid, local responses to community needs. We conducted a mixed methods pilot study among communities of color or low-income in the San Francisco Bay Area amid the COVID-19 pandemic to assess a hypothesized data model to inform rapid response efforts. Between 2020-2021, we collected (1) qualitative data through neighborhood reports submitted via Streetwyze, a mobile neighborhood mapping platform; (2) survey data on social and economic circumstances; and (3) geospatial data among residents of three counties. Qualitative data were coded and then integrated with survey and geospatial data. We used descriptive analyses to examine participants' experiences with food in their neighborhoods. Seventy percent of participants reported food insecurity before and after the pandemic began in March 2020. Within neighborhood reports, food was the most frequently occurring sub-theme within the Goods and Resources parent themes (68% and 49% of reports, respectively). Security (88%), resource programs (88%), outdoor space (84%), and equity (83%) were more likely to be mentioned by participants who were food insecure compared to those who were not (12%, 12%, 16%, 17%, respectively). Mentions of food in neighborhood reports more often occurred in census tracts with lower socioeconomic status and more area-level food insecurity. Individuals who were food insecure reported a constellation of needs beyond food, including needs related to safety and greater social equity. Our data model illustrates the potential for rapid assessment of community residents' experiences to provide enhanced understanding of community-level needs and effective support in the face of changing circumstances.

Cognitive issues of mobile map design and use

ABSTRACT Internet-connected mobile devices have changed how people access information. Like other information sources, maps have benefited from and been re-envisioned for mobile devices, and they are used in new contexts. However, these new contexts often generate additional cognitive load. We explored in depth two strategies designers could use to mitigate high cognitive loads associated with mobile map use: offloading cognition and reducing cognitive load by improved design to support the allocation of attention between the map and the environment. In reviewing these strategies, we considered their relevance to several mobile map use cases (navigation, individual and collaborative spatial decision making, information enrichment, and entertainment). Next, we identified recent progress in our understanding of how to measure cognitive load and map use context. Finally, we explored the wider implications of mobile maps for human behaviour and cognition. We identified two important cross-cutting research questions: 1) How can mobile maps be designed to reduce cognitive load by providing what is really needed by users to facilitate their cognitive processes?; 2) How can the intrinsic additional cognitive load created by the characteristics of mobile maps be managed and minimised by supporting the distribution of the user's attention between the map and environment?

Research on a Matching Method for Vehicle-Borne Laser Point Cloud and Panoramic Images Based on Occlusion Removal

Vehicle-borne mobile mapping systems (MMSs) have been proven as an efficient means of photogrammetry and remote sensing, as they simultaneously acquire panoramic images, point clouds, and positional information along the collection route from a ground-based perspective. Obtaining accurate matching results between point clouds and images is a key issue in data application from vehicle-borne MMSs. Traditional matching methods, such as point cloud projection, depth map generation, and point cloud coloring, are significantly affected by the processing methods of point clouds and matching logic. In this study, we propose a method for generating matching relationships based on panoramic images, utilizing the raw point cloud map, a series of trajectory points, and the corresponding panoramic images acquired using a vehicle-borne MMS as input data. Through a point-cloud-processing workflow, irrelevant points in the point cloud map are removed, and the point cloud scenes corresponding to the trajectory points are extracted. A collinear model based on spherical projection is employed during the matching process to project the point cloud scenes to the panoramic images. An algorithm for vectorial angle selection is also designed to address filtering out the occluded point cloud projections during the matching process, generating a series of matching results between point clouds and panoramic images corresponding to the trajectory points. Experimental verification indicates that the method generates matching results with an average pixel error of approximately 2.82 pixels, and an average positional error of approximately 4 cm, thus demonstrating efficient processing. This method is suitable for the data fusion of panoramic images and point clouds acquired using vehicle-borne MMSs in road scenes, provides support for various algorithms based on visual features, and has promising applications in fields such as navigation, positioning, surveying, and mapping.

A fast point cloud registration method based on spatial relations and features

Abstract Point cloud registration plays a crucial role in mobile robot localization, map building and three-dimensional (3D) model reconstruction. However, it remains challenged by issues such as compromised accuracy and sluggish efficiency, posing significant obstacles in achieving precise and timely alignments. Therefore, we propose a lightweight and fast point cloud registration method. Firstly, we mesh the 3D point cloud, compared with the traditional gridded point cloud method, it achieves initial point cloud registration by preserving the curvature characteristics of the internal point cloud, and utilizing the spatial relationship between grid cells and the quantitative relationship between the internal point cloud. Moreover, we adopt an iterative nearest point based on KD-Tree to realize the fine registration. So, our method does not necessitate intricate feature analysis and data training, and is resilient to similar transformations, non-uniform densities and noise. Finally, we conduct point cloud registration experiments using multiple publicly available point cloud datasets and compare them with several point cloud registration methods. The results demonstrate it is able to accomplish the point cloud registration quickly and exhibit high accuracy. More importantly, it maintains its efficacy and robustness even in the presence of noisy and defective point clouds.