The Possibility of Precise Positioning in the Urban Area

A third civil frequency at 1176.45MHz will be added to the GPS system.QZSS (Quasi Zenith Satellite System) proposed by Japan will also have thenew signal. This new frequency and the advent of QZSS will greatly enhancethe accuracy, reliability and robustness of civilian GPS receivers. One of theseenhancements is that it is possible to determine the GPS phase ambiguitiesmore or less instantaneously. This performance will have a tremendous impacton navigation. In this paper, the possibility of precise positioning in the urbanarea is examined from a point of instantaneous ambiguity resolution.A typical QZSS constellation, a third civil frequency andambiguity_estimation for triple-frequency data is discussed. The simulator forprecise positioning includes multipath effect which has been developed is alsodiscussed. To reflect multipath effect, the following points are considered:Building reflection, building diffraction, ground reflection, antenna pattern, andcorrelator selection. It is confirmed that a third civil frequency could make itmuch easier to resolve ambiguities more quickly and the advent of QZSS helpsto increase visible satellites in the urban area (Asian area). Although nextgeneration satellite positioning system doesn’t provide perfect navigation,improved performance could be realized

Hybrid Feature Embedding For Automatic Building Outline Extraction

Building outline extracted from high-resolution aerial images can be used in various application fields such as change detection and disaster assessment. However, traditional CNN model cannot recognize contours very precisely from original images. In this paper, we proposed a CNN and Transformer based model together with active contour model to deal with this problem. We also designed a triple-branch decoder structure to handle different features generated by encoder. Experiment results show that our model outperforms other baseline model on two datasets, achieving 91.1% mIoU on Vaihingen and 83.8% on Bing huts

Metapopulation Graph Neural Networks: Deep Metapopulation Epidemic Modeling with Human Mobility

Epidemic prediction is a fundamental task for epidemic control and prevention. Many mechanistic models and deep learning models are built for this task. However, most mechanistic models have difficulty estimating the time/region-varying epidemiological parameters, while most deep learning models lack the guidance of epidemiological domain knowledge and interpretability of prediction results. In this study, we propose a novel hybrid model called MepoGNN for multi-step multi-region epidemic forecasting by incorporating Graph Neural Networks (GNNs) and graph learning mechanisms into Metapopulation SIR model. Our model can not only predict the number of confirmed cases but also explicitly learn the epidemiological parameters and the underlying epidemic propagation graph from heterogeneous data in an end-to-end manner. The multi-source epidemic-related data and mobility data of Japan are collected and processed to form the dataset for experiments. The experimental results demonstrate our model outperforms the existing mechanistic models and deep learning models by a large margin. Furthermore, the analysis on the learned parameters illustrate the high reliability and interpretability of our model and helps better understanding of epidemic spread. In addition, a mobility generation method is presented to address the issue of unavailable mobility data, and the experimental results demonstrate effectiveness of the generated mobility data as an input to our model.Comment: This is the extended version of an ECMLPKDD2022 pape

Detection and Localization of Traffic Signals with GPS Floating Car Data and Random Forest

As Floating Car Data are becoming increasingly available, in recent years many research works focused on leveraging them to infer road map geometry, topology and attributes. In this paper, we present an algorithm, relying on supervised learning to detect and localize traffic signals based on the spatial distribution of vehicle stop points. Our main contribution is to provide a single framework to address both problems. The proposed method has been experimented with a one-month dataset of real-world GPS traces, collected on the road network of Mitaka (Japan). The results show that this method provides accurate results in terms of localization and performs advantageously compared to the OpenStreetMap database in exhaustivity. Among many potential applications, the output predictions may be used as a prior map and/or combined with other sources of data to guide autonomous vehicles

Enhancing Building Semantic Segmentation Accuracy with Super Resolution and Deep Learning: Investigating the Impact of Spatial Resolution on Various Datasets

The development of remote sensing and deep learning techniques has enabled building semantic segmentation with high accuracy and efficiency. Despite their success in different tasks, the discussions on the impact of spatial resolution on deep learning based building semantic segmentation are quite inadequate, which makes choosing a higher cost-effective data source a big challenge. To address the issue mentioned above, in this study, we create remote sensing images among three study areas into multiple spatial resolutions by super-resolution and down-sampling. After that, two representative deep learning architectures: UNet and FPN, are selected for model training and testing. The experimental results obtained from three cities with two deep learning models indicate that the spatial resolution greatly influences building segmentation results, and with a better cost-effectiveness around 0.3m, which we believe will be an important insight for data selection and preparation