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The Copernicus high-resolution layer imperviousness density (HRL IMD) for 2018 is a 10 m resolution raster showing the degree of soil sealing across Europe. The imperviousness gradation (0–100%) per pixel is determined by semi-automated classification of remote sensing imagery and based on calibrated NDVI. The product was assessed using a within-pixel point sample of ground truth examined on very high-resolution orthophoto for the section of the product covering Norway. The results show a high overall accuracy, due to the large tracts of natural surfaces correctly portrayed as permeable (0% imperviousness). The total sealed area in Norway is underestimated by approximately 33% by HRL IMD. Point sampling within pixels was found to be suitable for verification of remote sensing products where the measurement is a binomial proportion (e.g., soil sealing or canopy coverage) when high-resolution aerial imagery is available as ground truth. The method is, however, vulnerable to inaccuracies due to geometrical inconsistency, sampling errors and mistaken interpretation of the ground truth. Systematic sampling inside each pixel is easy to work with and is known to produce more accurate estimates than a simple random sample when spatial autocorrelation is present, but this improvement goes unnoticed unless the status and location of each sample point inside the pixel is recorded and an appropriate method is applied to estimate the within-pixel sampling accuracy. [ABSTRACT FROM AUTHOR]

Automatic control of intelligent vehicles depends heavily on accurate lane-level location information. The traditional digital map cannot meet the automatic driving requirements, and it is very difficult for lane-level road maps (LRMs) to achieve a good balance between the representation accuracy and data storage volume. To tackle this problem, we obtain the recursive definition of a lane line by analyzing road lanes using the fractal geometry theory. Subsequently, we construct a recursive feedback system and model it. On this basis, an autonomous storage optimization LRM generation strategy based on cascaded NURBS is devised. According to the input data density, this framework generates an LRM with the minimum data storage volume within the error constraint. It is mainly composed of two modules: (1) a random fractal and (2) an adaptive iteration memory optimizer, based on cascaded NURBS error feedback. First, the whole lane is divided into several meta lane line modules. Second, data sampling/interpolation is carried out iteratively according to the NURBS fitting error and symmetry of road geometry. In this way, the storage space used to store lane lines and other information is automatically optimized. Last, a simulation experiment is carried out by using a dataset obtained from a park. The simulation results show that the proposed method can achieve high accuracy and high storage efficiency. Under the constraint of a 0.1 m curve fitting error, the average memory demand of the lane line elements is less than 2.4 bytes/m. [ABSTRACT FROM AUTHOR]

This paper deals with area-based subpixel image registration under the rotation-isometric scaling-translation transformation hypothesis. Our approach is based on parametrical modeling of geometrically transformed textural image fragments and maximum-likelihood estimation of the transformation vector between them. Due to the parametrical approach based on the fractional Brownian motion modeling of the local fragments' texture, the proposed estimator \mboxML_\mathrmfBm (ML stands for “maximum likelihood” and fBm stands for “fractal Brownian motion”) has the ability to better adapt to real image texture content compared with other methods relying on universal similarity measures such as mutual information or normalized correlation. The main benefits are observed when assumptions underlying the fBm model are fully satisfied, e.g., for isotropic normally distributed textures with stationary increments. Experiments on both simulated and real images and for high and weak correlations between registered images show that the \mboxML_\mathrmfBm estimator offers significant improvement compared with other state-of-the-art methods. It reduces translation vector, rotation angle, and scaling factor estimation errors by a factor of about 1.75–2, and it decreases the probability of false match by up to five times. In addition, an accurate confidence interval for \mboxML_\mathrmfBm estimates can be obtained from the Cramér–Rao lower bound on rotation-scaling-translation parameter estimation error. This bound depends on texture roughness, noise level in reference and template images, correlation between these images, and geometrical transformation parameters. [ABSTRACT FROM AUTHOR]

In large databases, the amount and the complexity of the data calls for data summarization techniques. Such summaries are used to assist fast approximate query answering or query optimization. Histograms are a prominent class of model-free data summaries and are widely used in database systems. So-called self-tuning histograms look at query-execution results to refine themselves. An assumption with such histograms, which has not been questioned so far, is that they can learn the dataset from scratch, that is—starting with an empty bucket configuration. We show that this is not the case. Self-tuning methods are very sensitive to the initial configuration. Three major problems stem from this. Traditional self-tuning is unable to learn projections of multi-dimensional data, is sensitive to the order of queries, and reaches only local optima with high estimation errors. We show how to improve a self-tuning method significantly by starting with a carefully chosen initial configuration. We propose initialization by dense subspace clusters in projections of the data, which improves both accuracy and robustness of self-tuning. Our experiments on different datasets show that the error rate is typically halved compared to the uninitialized version. [ABSTRACT FROM AUTHOR]

The BOKZ-M star sensor (a unit for measuring star coordinates) is intended to determine the parameters of the orientation of the axes of its own coordinate system relative to the axes of the inertial system using the observations of the areas of the stellar sky. A large number of factors influence the accuracy of this determination, and the estimation of the contribution of every factor to the total error is not always possible. In general, it must be limited by an estimation of the total error according to the results of studying a longterm series of determinations of the orientation parameters obtained in the ground tests and in space flight. This paper presents accuracy estimations for the BOKZ-M device obtained during its flight operation on the Meteor-M satellite. [ABSTRACT FROM AUTHOR]

Location estimation in wireless sensor networks (WSNs) has received tremendous attention in recent times. Improved technology and efficient algorithms systematically empower WSNs with precise location identification. However, while algorithms are efficient in improving the location estimation error, the factor of the network lifetime has not been researched thoroughly. In addition, algorithms are not optimized in balancing the load among nodes, which reduces the overall network lifetime. In this paper, we have proposed an algorithm that balances the load of computation for location estimation among the anchor nodes. We have used vector-based swarm optimization on the connected dominating set (CDS), consisting of anchor nodes for that purpose. In this algorithm, major tasks are performed by the base station with a minimum number of messages exchanged by anchor nodes and unknown nodes. The simulation results showed that the proposed algorithm significantly improves the network lifetime and reduces the location estimation error. Furthermore, the proposed optimized CDS is capable of providing a global optimum solution with a minimum number of iterations. [ABSTRACT FROM AUTHOR]