Map Update Research Articles

An efficient calculation method of large-region dynamic traffic noise maps based on hybrid modeling

The construction of noise maps is of great significance for the management and control of urban noise and the protection of residents’ physical and mental health. The European Noise Directive recommends using computational methods to construct strategic noise maps when possible. The current noise maps based on model calculation rely on complex noise emission and propagation models, and their huge number of regional grids needs to consume a lot of calculation time. This seriously restricts the update efficiency of noise maps, making it difficult to realize large-scale application and real-time dynamic update of noise maps. In order to improve the computational efficiency of noise maps, based on big data-driven technology, this paper combines the traditional CNOSSOS-EU noise emission modeling method with the multivariate nonlinear regression modeling method, and proposes an efficient calculation method of large-region dynamic traffic noise maps based on hybrid modeling method. First, this paper constructs the (daily and nightly) noise contribution prediction models of road sources with different classes, considering the daily and nightly periods and different urban road classes. Parameters of the proposed model are evaluated by using the multivariate nonlinear regression method to replace the complex nonlinear acoustic mechanism modeling. On this basis, in order to further improve the computational efficiency, noise contribution attenuations of the constructed models are parameterized and evaluated quantitatively. And then, the database containing the index table of the road noise sources-receivers and the corresponding noise contribution attenuations is constructed. The experimental results show that compared with the traditional calculation methods based on acoustic mechanism model, the noise map calculation method based on hybrid model proposed in this paper greatly reduces the model computations of noise map, improves the efficiency of noise mapping. It will provide technical support for constructing dynamic noise maps of large urban regions.

An improved algorithm for detecting mesoscale ocean fronts from satellite observations: Detailed mapping of persistent fronts around the China Seas and their long-term trends

Mesoscale fronts are highly energetic ocean processes that dominate local biogeochemical processes and air-sea interactions and exert significant effects on ocean dynamics, ecology, and climate. An automatic front detection algorithm applied to satellite-based observations is a fundamental approach to acquiring frontal occurrence information. Histogram-based algorithms have been widely used in previous studies, and, despite being the most popular front detection algorithm, continuous improvements have been needed to address imperfect detection issues (undetected coastal fronts, discontinuities of fronts and repeated detection of fronts). In this study, a new algorithm is proposed to address the issues by coupling inverse distance weighting and mathematical morphology operators. Using 14,610 images acquired from 1982 to 2021, a series of comparisons between the new and existing algorithms have been implemented to validate the improvements. Statistical results suggest that the number of frontal pixels detected by our algorithm increased by 58.45% in the coastal domain when compared to previous methods. Statistical analysis also shows most of these increased fronts can be supported by a classic gradient-based method which is superior in detecting coastal fronts, which demonstrates the improvement of new algorithm in detecting coastal fronts. Furthermore, visual analyses suggest that our algorithm can better connect discontinuous fronts and reduce repeated detections, and statistical comparisons in the open seas present a 34.50% improvement in the average length of frontal segments. Moreover, based on an automated objective delineation method, an analysis of 40 years of seasonal frontal occurrences (as detected by our algorithm) produced a dataset that shows a detailed map of 59 persistent fronts around the China Seas. This map updates previously hand-delineated frontal positions and identifies some previously unreported persistent fronts. Further analyses show that the frontal occurrence or intensity of a majority of persistent fronts around the China Seas presents significant (P < 0.05) long-term variability over the last 40 years. The improved algorithm, together with its application in identifying persistent fronts around the China Seas, is expected to be extended to other waters to better investigate the response of marine ecosystems to front dynamics and predict the distribution of marine organisms within a changing climate.

A REM Update Methodology Based on Clustering and Random Forest

In this paper, we propose a radio environment map (REM) update methodology based on clustering and machine learning for indoor coverage. We use real measurements collected by the TurtleBot3 mobile robot using the received signal strength indicator (RSSI) as a measure of link quality between transmitter and receiver. We propose a practical framework for timely updates to the REM for dynamic wireless communication environments where we need to deal with variations in physical element distributions, environmental factors, movements of people and devices, and so on. In the proposed approach, we first rely on a historical dataset from the area of interest, which is used to determine the number of clusters via the K-means algorithm. Next, we divide the samples from the historical dataset into clusters, and we train one random forest (RF) model with the corresponding historical data from each cluster. Then, when new data measurements are collected, these new samples are assigned to one cluster for a timely update of the RF model. Simulation results validate the superior performance of the proposed scheme, compared with several well-known ML algorithms and a baseline scheme without clustering.

Update of global maps of Alisov’s climate classification

Proposed in 1954, Alisov’s climate classification (CC) focuses on climatic changes observed in January–July in large-scale air mass zones and their fronts. Herein, data clustering by machine learning was applied to global reanalysis data to quantitatively and objectively determine air mass zones, which were then used to classify the global climate. The differences in air mass zones between two half-year seasons were used to determine climatic zones, which were then subdivided into continental or maritime climatic regions or according to east–west climatic differences. This study renews Alisov’s CC for the first time in almost 70 years and employs data-driven machine learning to establish a standard for causal CC based on air masses.

Autonomous exploration through deep reinforcement learning

PurposeRobots equipped with LiDAR sensors can continuously perform efficient actions for mapping tasks to gradually build maps. However, with the complexity and scale of the environment increasing, the computation cost is extremely steep. This study aims to propose a hybrid autonomous exploration method that makes full use of LiDAR data, shortens the computation time in the decision-making process and improves efficiency. The experiment proves that this method is feasible.Design/methodology/approachThis study improves the mapping update module and proposes a full-mapping approach that fully exploits the LiDAR data. Under the same hardware configuration conditions, the scope of the mapping is expanded, and the information obtained is increased. In addition, a decision-making module based on reinforcement learning method is proposed, which can select the optimal or near-optimal perceptual action by the learned policy. The decision-making module can shorten the computation time of the decision-making process and improve the efficiency of decision-making.FindingsThe result shows that the hybrid autonomous exploration method offers good performance, which combines the learn-based policy with traditional frontier-based policy.Originality/valueThis study proposes a hybrid autonomous exploration method, which combines the learn-based policy with traditional frontier-based policy. Extensive experiment including real robots is conducted to evaluate the performance of the approach and proves that this method is feasible.

SemiRoadExNet: A semi-supervised network for road extraction from remote sensing imagery via adversarial learning

Road extraction from remote sensing imagery is a popular and frontier research focus, since road information plays an essential role in application fields, such as urban management, map updating and traffic planning. Deep learning based methods have shown their dominance on road extraction from remote sensing imagery. However, the performance of the existing road extraction methods relies heavily on a large amount of high-quality annotated training data, which is usually hard to obtain in practice. Current semi-supervised road extraction models can effectively reduce the dependency on the labeled data, nevertheless they cannot fully utilize the latent information of low-confidence pixels in pseudo-labels effectively. These pixels are usually a border between road and non-road area and of importance for the prediction accuracy of road extraction models. In order to address these issues, we proposed a novel semi-supervised road extraction network, named SemiRoadExNet. SemiRoadExNet is based on a Generative Adversarial Network (GAN), containing one generator with two discriminators. Firstly, both labeled and unlabeled images are put into the generator network for road extraction, and the outputs of the generator not only include road segmentation results but also the corresponding entropy maps. The entropy maps represent the confidence of prediction (road or non-road) for each pixel. Then, the two discriminators enforce the feature distributions keeping the consistency of road prediction maps and entropy maps between the labeled and unlabeled data. During the adversarial training, the generator is continuously regularized by exploiting the potential information from unlabeled data, thus the generalization capacity of the proposed model can be improved effectively. Compared to several state-of-the-art semi-supervised semantic segmentation methods, the proposed SemiRoadExNet achieves 0.96–5.38% IoU improvements on DeepGlobe Road Extraction, Massachusetts Roads and CHN6-CUG datasets respectively. The source code of d SemiRoadExNet is freely available at https://github.com/hchen118/SemiRoadExNet.

Rapid Mapping of Slow-Moving Landslides Using an Automated SAR Processing Platform (HyP3) and Stacking-InSAR Method

The increasing number of landslide hazards worldwide has placed greater demands on the production and updating of landslide inventory maps. As an important data source for landslide detection, interferometric synthetic aperture radar (InSAR) data processing is time-consuming and also requires specialized knowledge, which severely hinders its widespread application. At present, a new cloud-based online platform, i.e., Alaska Satellite Facility’s Hybrid Pluggable Processing Pipeline (ASF HyP3) was developed for massive SAR data processing. In this study, combining the HyP3 online platform and Stacking-InSAR method, we constructed a new easy-to-use processing chain for rapidly identifying slow-moving landslides over large areas. With this processing chain, a total of 923 interferometric pairs covering an area of over 1800 km2 were processed within a few hours (about 4 to 5 h). A total of 81 slow-moving landslides were immediately detected and mapped using Stacking-InSAR method, of which 65 landslides were confirmed by previous studies and 16 landslides were newly detected. Results show that the new processing chain can greatly improve the efficiency of wide-area landslide mapping and is expected to serve as an effective tool for rapid updating the existing landslide inventories and contribute to the prevention and management of geological hazards.

VIS-MM: a novel map-matching algorithm with semantic fusion from vehicle-borne images

Conventional map-matching (MM) algorithms take blind eyes to the complexity in realistic traffic conditions and hence present significant limitations in distinguishing the detailed driving paths of vehicles within complex urban road networks. The popularity of vehicle-borne cameras and advances in image recognition technologies provide an opportunity to remedy the gap through integrating vehicle-borne image semantic information with MM algorithms. Following this logic, this article proposes a novel MM algorithm with semantic fusion from vehicle-borne images (VIS-MM) suited to the parallel road scenes. First, a multipath output algorithm is developed using the hidden Markov model to obtain candidate paths. Second, image recognition techniques are employed to extract vehicle-borne image semantics. Finally, the entropy weight method is performed to determine the most promising driving path among the candidate paths. The experimental results show that semantic fusion from vehicle-borne images contributes to a significant improvement of accuracy from 66.18% to 99.88% against the parallel road scenes. The proposed map-matching algorithm can be applied into the fields of unmanned autonomous navigation and crowdsourcing updating of high-definition maps.

Evidence map on post-acute COVID-19 sequelae and rehabilitation: Update as of July 2022Mapa de evidencia sobre las secuelas y la rehabilitación tras la COVID-19 aguda: versión actualizada en julio del 2022

To update the evidence map on the effects of interventions for post-acute COVID-19 rehabilitation. The search scope was defined according to the population (patients with symptomatic COVID-19 and post-acute COVID sequelae), the context (interventions for rehabilitation), and the type of study (systematic reviews, rapid reviews, scoping reviews or overviews of reviews). Following a search in PubMed and the Virtual Health Library, two independent authors selected the articles for review. The map was updated on July 27, 2022, using the same procedures employed in the initial review. The initial evidence map included 22 studies (four systematic reviews, four rapid reviews, four reviews of case reports, one scoping review, and nine systematic review protocols). In the present update, an additional 10 studies were included. The analysis revealed four groups of interventions (multimodal, therapeutic, complementary, and pharmacological) and seven groups of outcomes (pathological conditions, diseases/respiratory disorders, pain, physiological and metabolic markers, mental health/quality of life, sensory function, and mortality), totaling 166 associations between interventions and outcomes. The highest number of associations was observed for complementary therapies (n = 94). Among the outcomes, the highest number of associations was observed for physiological and metabolic markers, pathological conditions, and mental health/quality of life (44, 41, and 35 associations respectively). The map update involved the analysis of 69 associations, most notably exercise (isolated, multicomponent, or multimodal intervention), with 23 positive and four potentially positive effects) and pharmacologic and complementary therapies for sensorial functions (15 associations). The high number of systematic review protocols indicates that the literature is still incipient.

Love of Variety Based Latency Analysis for High Definition Map Updating: Age of Information and Distributional Robust Perspectives

High definition (HD) map is a key technology that enables autonomous driving, which has the characteristic of frequent updates and low delay requirements. In order to minimize the HD map updating latency, this paper jointly considers the generation and transmission processes of HD map. The generation process of HD map relies heavily on the data captured by different types of sensors, which is modeled by the federated analytics process. Also, in order to quantify the diversity of utilized sensors, the concept of love of variety is introduced in this paper. Then, considering HD map is composed of dynamic and static layers that have different latency requirements during the transmission process, this paper proposes a method to allocate edge server capacity to each HD map layer such that the overall information staleness can be minimized. Firstly, the deterministic edge capacity case is discussed and the solution is derived by obtaining the Karush—Kuhn—Tucker conditions. Then, considering the practice that an edge server provides services to multiple attached devices simultaneously, contentions among these devices make available capacity for the autonomous vehicle variational. Therefore, an uncertain edge capacity case is discussed as well, where the uncertainty is described by Wasserstein metrics and the problem is reformulated into distributional robust chance constrained optimization problem. And for the low latency purpose, we utilize an inner approximation to reduce the complexity of the original problem and find a suboptimal solution. Finally, simulation results demonstrate the effectiveness of our proposed methods that achieves the lowest latency for HD map updating.

Learning Salient Feature for Salient Object Detection Without Labels.

Supervised salient object detection (SOD) methods achieve state-of-the-art performance by relying on human-annotated saliency maps, while unsupervised methods attempt to achieve SOD by not using any annotations. In unsupervised SOD, how to obtain saliency in a completely unsupervised manner is a huge challenge. Existing unsupervised methods usually gain saliency by introducing other handcrafted feature-based saliency methods. In general, the location information of salient objects is included in the feature maps. If the features belonging to salient objects are called salient features and the features that do not belong to salient objects, such as background, are called nonsalient features, by dividing the feature maps into salient features and nonsalient features in an unsupervised way, then the object at the location of the salient feature is the salient object. Based on the above motivation, a novel method called learning salient feature (LSF) is proposed, which achieves unsupervised SOD by LSF from the data itself. This method takes enhancing salient feature and suppressing nonsalient features as the objective. Furthermore, a salient object localization method is proposed to roughly locate objects where the salient feature is located, so as to obtain the salient activation map. Usually, the object in the salient activation map is incomplete and contains a lot of noise. To address this issue, a saliency map update strategy is introduced to gradually remove noise and strengthen boundaries. The visualization of images and their salient activation maps show that our method can effectively learn salient visual objects. Experiments show that we achieve superior unsupervised performance on a series of datasets.

Multitemporal Feature-Level Fusion on Hyperspectral and LiDAR Data in the Urban Environment

Technological innovations and advanced multidisciplinary research increase the demand for multisensor data fusion in Earth observations. Such fusion has great potential, especially in the remote sensing field. One sensor is often insufficient in analyzing urban environments to obtain comprehensive results. Inspired by the capabilities of hyperspectral and Light Detection and Ranging (LiDAR) data in multisensor data fusion at the feature level, we present a novel approach to the multitemporal analysis of urban land cover in a case study in Høvik, Norway. Our generic workflow is based on bitemporal datasets; however, it is designed to include datasets from other years. Our framework extracts representative endmembers in an unsupervised way, retrieves abundance maps fed into segmentation algorithms, and detects the main urban land cover classes by implementing 2D ResU-Net for segmentation without parameter regularizations and with effective optimization. Such segmentation optimization is based on updating initial features and providing them for a second iteration of segmentation. We compared segmentation optimization models with and without data augmentation, achieving up to 11% better accuracy after segmentation optimization. In addition, a stable spectral library is automatically generated for each land cover class, allowing local database extension. The main product of the multitemporal analysis is a map update, effectively detecting detailed changes in land cover classes.

ДОСВІД ЗАСТОСУВАННЯ ГІС-ТЕХНОЛОГІЙ ПРИ СТВОРЕННІ (ОНОВЛЕННІ) ЦИФРОВИХ ТОПОГРАФІЧНИХ КАРТ МАСШТАБУ 1:25000

The purpose of this work is to research the actual methodology of creating a digital version of the nomenclature sheet on a scale of 1:25000 based on aerospace data using modern software for the formation of a single database of topographic data on a scale of 1:25000. The basis of the study is the analysis of the possibilities of applying theoretical and practical approaches to the updating of topographic maps, the selection of vectorization methods,the use of tools for quick editing and geoprocessing of data, ensuring the implementation of a system of checks of the nomenclature sheet and automatic control and assessment of the quality of vectorization by using geoinformation technologies of the ArcGIS software. The article discusses the importance of research aimed at creating and updating electronic topographic maps of Ukraine. The authors investigate the processes of creating (updating) digital topographic maps on a scale of 1:50000 in order to form a single database of topographic maps on a scale of 1:25000. The key stages of creating (updating) electronic topographic maps on a scale of 1:25000 for Ukraine are described. Using the ArcGIS program, a practical example of updating a topographic map ofscale 1:25000 was developed, based on the application of geoinformation technologies, the use of scanned paper maps, recent aerial photographs and satel-lite images. Methodological aspects of vectorization, detailing, completeness of attributive information, topological consistency and consistency of data, as well as features of automated quality control of updated electronic topographic maps are analyzed. The results of the study confirm that the use of geoinformation technologies and process automation can provide an up-to-date topographic data base that is important for various fields, improving project planning and implementation. The created base of topographic data on a scale of 1:25000 takes into account the functionality of step-by-step vectorization, selection criteria, rules of topological relationships, and automated quality control using modern geoinformation technologies.

Federated HD Map Updating Through Overlapping Coalition Formation Game

High Definition (HD) maps have become core supporting components for autonomous driving. To date, their updates heavily depend on the vehicle fleets of the map vendors, which cannot scale and timely reflect the highly dynamic environment. To ensure the HD map quality, it is advocated social vehicles should be used. Nevertheless, there are privacy concerns and a lack of incentives for social vehicles to contribute data. In this paper, we leverage federated analytics (FA), a newly developed collaborative data analytics paradigm, where raw data are kept local and only the insights generated from local analytics are sent to a server for aggregation. We present a new Federated Analytics based HD map Updating model (FAUMap) to protect the privacy of social vehicles. To motivate social vehicles to contribute data and improve the HD map quality, we formulate an overlapping coalition formation game, OCFUMap, and develop an algorithm to find feasible coalitions. Simulations show that our approach can improve the quality of the updated HD map by 1.56 times. To study an end-to-end operation of the FAUMap model and OCFUMap game, we present a case of HD map updates of the Powell street in San Francisco using the autonomous driving simulator CarLA.

Trajectory Planning of Automated Vehicles Using Real-Time Map Updates

Trajectory Planning of Automated Vehicles Using Real-Time Map Updates

基于无人机正射航飞技术的大比例尺地形图更新应用浅析——以醴陵市地形图更新为例

基于无人机正射航飞技术的大比例尺地形图更新应用浅析——以醴陵市地形图更新为例

Development of a Data-Driven On-Street Parking Information System Using Enhanced Parking Features

On-street parking information (OSPI) systems help reduce congestion in the city by lessening parking search time. However, current systems use features mainly relying on costly manual observations to maintain a high quality. In this paper, on top of traditional location-based features based on spatial, temporal and capacity attributes, vehicle parked-in and parked-out events are employed to fill the quality assurance gap. The parking events (PEs) are used to develop dynamic features to make the system adaptive to changes that impact on-street parking availability. Additionally, a parking behavior change detection (PBCD) model is developed as an OSPI supplementary component to trigger potential parking map updates. The evaluation shows that the developed OSPI availability prediction model is on par with state-of-the-art models, despite having simpler but more enhanced and adaptive features. The foundational temporal and aggregated spatial parking capacity features help the most, while the PE-based features capture variances better and enable adaptivity to disruptions. The PE-based features are advantageous as data are automatically gathered daily. For the PBCD model, impacts by construction events can be detected as validation. The methodology proves that it is possible to create a reliable OSPI system with predominantly PE-based features and aggregated parking capacity features.

Intelligent SLAM Algorithm Fusing Low-Cost Sensors at Risk of Building Collapses

When firefighters search inside a building that is at risk of collapse due to abandonment or disasters such as fire, they use old architectural drawings or a simple monitoring method involving a video device attached to a robot. However, using these methods, the disaster situation inside a building at risk of collapse is difficult to detect and identify. Therefore, we investigate the generation of digital maps for a disaster site to accurately analyze internal situations. In this study, a robot combined with a low-cost camera and two-dimensional light detection and ranging (2D-lidar) traverses across a floor to estimate the location of obstacles while drawing an internal map of the building. We propose an algorithm that detects the floor and then determines the possibility of entry, tracks collapses, and detects obstacles by analyzing patterns on the floor. The robot’s location is estimated, and a digital map is created based on Hector simultaneous localization and mapping (SLAM). Subsequently, the positions of obstacles are estimated based on the range values detected by 2D-lidar, and the position of the obstacles are identified on the map using the map update method in semantic SLAM. All equipment are implemented using low-specification devices, and the experiments are conducted using a low-cost robot that affords near-real-time performance. The experiments are conducted in various actual internal environments of buildings. In terms of obstacle detection performance, almost all obstacles are detected, and their positions identified on the map with a high accuracy of 89%.

Frequent and Automatic Update of Lane-Level HD Maps with a Large Amount of Crowdsourced Data Acquired from Buses and Taxis in Seoul

Recently, HD maps have become important parts of autonomous driving, from localization to perception and path planning. For the practical application of HD maps, it is significant to regularly update environmental changes in HD maps. Conventional approaches require expensive mobile mapping systems and considerable manual work by experts, making it difficult to achieve frequent map updates. In this paper, we show how frequent and automatic updates of lane marking in HD maps are made possible with enormous crowdsourced data. Crowdsourced data is acquired from onboard low-cost sensing devices installed on many city buses and taxis in Seoul, South Korea. A large amount of crowdsourced data is daily accumulated on the server. The quality of sensor measurement is not very high due to the limited performance of low-cost devices. Therefore, the technical challenge is to overcome the uncertainty of the crowdsourced data. Appropriately filtering out a large amount of low-quality data is a significant problem. The proposed HD map update strategy comprises several processing steps including pose correction, observation assignment, observation clustering, and landmark classification. The proposed HD map update strategy is experimentally verified using crowdsourced data. If the changed environments are successfully extracted, then precisely updated HD maps are generated.

CAOM: Change-aware online 3D mapping with heterogeneous multi-beam and push-broom LiDAR point clouds

A High-Definition (HD) map is an essential component not only for autonomous vehicles but also for surveyors in favor of city management. Moreover, the HD map must be up-to-date by reflecting environmental changes continuously. The development of LiDAR Simultaneous Localization And Mapping (SLAM) techniques has provided an effective way to collect precise point clouds for the construction of HD maps, while traditional methods are time-consuming and labor-intensive on account of the complex street scene. Furthermore, life-long 3D mapping is quite necessary and challenging nowadays. In this paper, we introduce a novel change-aware 3D online mapping framework CAOM,11Code at https://github.com/DCSI2022/CAOM. using point clouds collected by Multi-Beam LiDAR (MBL) and Push-Broom LiDAR (PBL) for rapid city map update and change detection. Concerning the prior map with higher precision, heterogenous fusion is performed to rectify the SLAM drift, based on the View-Traced Clouds (ViTC) derived according to their view in common. Meanwhile, corresponding virtual range images are integrated with distance images to detect the 3D changes between the current map and the historical reference map offline. The detection results are later fused in a probabilistic way along with the homogeneous fusion process where a resilient graph consisting of diverse factors is established to maintain the local and global consistency of the final point cloud map. Compared with 3D-CSTM, the real-world experiments have shown an average 0.5 m and max 1.5 m improvement in the ATE (Absolute Translation Error) of the trajectory generated by our system. Quantitative measurements on the individual point accuracy are also conducted to verify the performance of 3D mapping with up to 16% improvement in comparison with respect to the 3D-CSTM method. The effects of the disparity detection and probabilistic fusion can be revealed from the small objects distinguished in the point cloud map as well as > 95% overall accuracy.