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This paper proposes a method of establishing a time reference without a free paper time scale. The core idea is predicting and steering each atomic clock versus external reference time scale, respectively.Each clock can obtain a time scale steered by the external reference time scale. Then, the time reference is a weighted average of the steered time scales. The new method avoids the problems resulting from the correlation of the paper time scale and the time difference of each contributing clocks of traditional methods. Moreover, prediction algorithms and steering algorithms can be optimized according to different clocks with different characteristics, respectively. At the beginning, this paper briefly describes the basic principles of traditional methods and their weaknesses. Then, the basic principle, the theoretical advantages and the design principles of the prediction algorithm, steering algorithm and weighting algorithm of the new method are described in detail. An experiment shows the weakness of traditional methods. A simulation shows the traditional method and new method performances, and also preliminarily validates the excellent performances of this method.

The aggregation of buildings is generally based on the disjoint in spatial topological relationships. However, with the diversification of data sources, the spatial topological relationships between polygons inside large buildings also become diversified and even inconsistent, making it difficult to aggregate building polygons, while the multi-scale visualization of buildings in the smart city requires rapid and accurate extraction of the contour shapes of complex buildings. For this reason, this paper proposes an aggregation method of buildings based on the shortest adjacent line between vertexes interpolated. First, recursively search for adjacent polygons based on the aggregation distance to determine the clustering regions, which is convenient for processing the map data in every region. Then, for each clustered region, encrypt vertices for each polygon using a linear interpolation method according to the order of the points on it; after that, search and construct the shortest adjacent line between each vertice and the adjacent polygon through the grid index, and delete the three types of shortest adjacent lines which are abnormal; then, merge the effective shortest adjacent lines in a certain order to form the shortest adjacent line group, and further filter out three kinds of special cases, so as to determine the aggregation area of adjacent polygons; finally, the recursive search is performed clockwise according to the minimum rotation angle, and the contour of polygons including the inner ring and the outer ring is extracted. Experiments show that this method can quickly detect the aggregation area between the adjacent polygons. Experiments in this paper show that this method can quickly detect the adjacent aggregation areas between building polygons and accurately extract the outer contour lines of complex building entities in real-time. The overall contours extracted are close to the actual situation, which provides conditions for building visualization.

In order to effectively solve the trust problem of both parties in the process of geographic data transaction, this paper proposes a geographic data transaction certificate and copyright protection model combining zero-watermark, IPFS and smart contract technology. The model uses the IPFS system to build a new mechanism for zero-watermark registration, which can eliminate the dependence on third-party IPR institutions. Through the smart contract design, the zero-watermark information is registered, and the transaction information is stored permanently in the blockchain to realize the time-stamp authentication of zero-watermark registration. The tamper-resistant of blockchain and the automatic execution mechanism of smart contract in the model enable both parties to reach a consensus and achieve mutual trust without the supervision of a third party. Based on this model, this paper uses "Ant OpenChain" as a smart contract development platform to develop a transaction and copyright protection system for geographic data. With this system, both sides of the transaction can call the smart contract to obtain the transaction certificate from the blockchain. Then, the watermark information is extracted by the zero-watermark downloaded from the IPFS network. Finally, the data copyright can be confirmed and the source can be traced effectively for protecting rights or solving disputes by combining the transaction certificate and watermark information to confirm. Experiments show that the proposed method can protect the rights and interests of both parties and protect the security of transaction data simultaneously.

The selection of road network is a fuzzy decision-making process. Aiming at problems such as noise, conflict and human decision-making in the cartographic generalization algorithm based on general case reasoning, this paper proposes a road network selection method which uses ontology to organize cases and conduct knowledge reasoning. In this paper, we extract expert cases from series scale map and describe them formally according to ternary model. To realize knowledge reasoning, these cases are used to build a domain ontology repository, reorganizing their representation format. Then, ontology rule extension helps to identify and eliminate noise and conflict cases. Additionally, with methods of semantic mapping and graph query, the semantic and geometric features of the road network to be selected are associated with the ontology repository to realize road network selection automatically.

Whether the location is accurately described or not directly affects the quality of service of LBS. Relative location description is a common way of location information exchange within the text big data, and its core element lies in the selection of reference landmarks. Accurate and effective evaluation of landmark influence is an important foundation for correct selection of a reference landmark. The models involved in the current research focus on the impact on the inherent characteristics of landmarks. However, non-isolated landmarks are always subject to the dual constraints on their internal characteristics and external landmarks at the same time. Therefore, the influence of distance between landmarks can not be ignored in actual scenes. This paper evaluates the individual influence of landmarks from the aspects of cognition, vision and scene, and calculates the relative influence of each landmark on the point to be located by using the distance between the point and the landmark. A landmark influence evaluation model considering distance constraints is proposed to solve above problems. Finally, take multiple adjacent landmarks in the surrounding area of Bayi square in Nanchang as an example, an on-site questionnaire surveys was performed to verify the effectiveness of the evaluation model. The results show that the model proposed in the paper has a good performance and most recommended landmarks could meet people's psychological expectations than the existing models.

Extracting accurate road curb is a crucial task for driverless vehicles. However, existing road curb points extraction methods are not robust for sparse 16-ray LiDAR data. This paper presents a road curb points extraction algorithm that combines multiscale spatial features and measuring distance. The points outside the road areas are firstly removed by adopting the random sample consensus (RANSAC) algorithm, then most of the road surface points are removed by judging the horizontal and vertical continuity between points in the same laser beam. According to the measurement model of the road curb points, if the measured distance of the reserved points is within a reasonable distance and the angle between the two horizontal vectors starting with the point is larger than a certain threshold, the point will be identified as the road curb point. Experiments show that the road curb points extraction method proposed in this paper performs better than the other methods under 16-ray LiDAR dataset and meets the real-time requirements for environmental perception of driverless vehicles.

Road intersections are important components of a road network, which are not only numerous and diverse in shape, but also complex in structure and different in size. It is difficult to recognize comprehensive and accurate road junctions based on single data source, as its limited describe information. To this end, this paper designs a multiple integration method to identify road intersections from vehicle trajectories and remote sensing images. Firstly, based on the unsupervised idea, a method combining morphological processing, density peak clustering and tensor voting is proposed to extract the seed intersections, which is regarded as a small sample set. Based on it two intersection classifiers based on deep convolution network and oriented to vehicle trajectories and remote sensing images are constructed by using collaborative training mechanism, and finally, the advantages of the two models are combined to form an integrated classification model of road intersections. In this paper, a semi supervised intersection extraction technology is proposed by fusing the complementary description features of vehicle trajectories and remote sensing images on multiple levels, which can effectively identify complex and diverse road intersections without manual labeling. Experiments based on Wuhan taxi trajectories and remote sensing images show that the accuracy of this method is more than 93% and the recall rate is 87% without manually labeled samples.

Visual saliency modeling of driving scenes is an important research direction in intelligent driving, especially in the areas of assisted driving and automatic driving. The existing visual saliency modeling methods for static and virtual scenes cannot adapt to the real-time, dynamic and task-driven characteristics of road scenes in real driving environments. Building a visual saliency model of dynamic road scenes in real driving environments is a challenge for current research. Starting from the characteristics of driving environment and driver's visual cognitive law, this paper extracts low-level visual features, high-level visual features and dynamic visual features of road scenes, and combines two influencing factors of speed and road curvature to build a visual saliency calculation model of driving scenes based on logistic regression model (LR). In this paper, the AUC value is used to evaluate the model, and the results showed an accuracy of 90.43%, which is significant advantage over traditional algorithms.

From the automatic driving vehicle to the intelligent vehicle, and then to intelligent connected vehicle, the intelligent driving vehicle technology and industry has developed rapidly, surveying and remote sensing technology have played an important supporting role. Firstly, this paper introduces the development of intelligent vehicles at home and abroad and their differences from conventional vehicles. Based on the perspective of three important waves of scientific and technological development, this paper comparatively analyzes and summarizes the development process and core technology driving forces of autonomous driving and surveying remote sensing. The intelligent driving vehicle's key technologies are then introduced from four aspects: top-level planning and policy environment, environment perception and computing decision-making vehicle key technology, high definition map and navigation positioning basic support key technology, and vehicle road collaborative information interaction key technology. Several advanced navigation and positioning technology achievements, such as satellite-based navigation enhancement, high precision position and attitude measurement, multi-source fusion perception, are taken as examples to demonstrate that surveying and mapping can enable intelligent driving of a single car. By introducing mobile measurement and crowdsourcing high precision map-making technology, the key supporting role of surveying and remote sensing for intelligent driving is expounded. This paper takes the construction of space-based information real-time service system as an example to describe the development trend of how surveying, mapping, and remote sensing will serve the "person vehicle road cloud" of intelligent connected vehicles in the future.Finally, the paper summarizes and analyzes the problems that need to be solved in developing intelligent (connected) vehicles and the challenges that need to be addressed in the near future.

Accurate monitoring of surface water is an important basic application of remote sensing. The principle of optical remote sensing water extraction is based on different ground features having different spectral reflection characteristics. Some ground features (ice, snow, shadows, clouds) have similar reflection characteristics to water bodies, which leads to the failure of extraction and classification. Aiming at the problem of misclassification and omission of traditional water body index in water body extraction, this paper proposes a normalized difference multi-band water index model. This paper uses two experiments to test the stability of the new index. The area of experiment 1 is the Linzhi area of Tibet. The data source is Landsat 8 and Sentinel 2 satellite images in the same time phase. The experimental results verify the ability of the new index to suppress snow and ice. The Kappa coefficient of the new index is 0.86, the overall accuracy is 0.93, and the misclassification error is 0.03, the omission error is 0.12, the drawing accuracy is 0.97, and the producer accuracy is 0.88, which are better than the existing index. The data source of experiment 2 was GF-1, and Hong Kong Disneyland was used as the experimental area. Experiment 3 extracted water bodies in multiple regions and verified the stability of the water body index in this paper. Water extraction was performed in an environment with a small amount of clouds, which proved that the new index can suppress clouds and their shadows. This paper uses multi-source optical remote sensing image to verify the feasibility of the new index. Without additional auxiliary data, the influence of snow, cloud and shadow can be eliminated, and the water can be more effectively and automatically extracted, which can be extended to coastal resource research, glacier change, inland lake change and other fields.