Geospatial Agents, Agents Everywhere . . .

The use of the related terms “agent‐based”, “multi‐agent”, “software agent” and “intelligent agent” have witnessed significant growth in the Geographic Information Science (GIScience) literature in the past decade. These terms usually refer to both artificial life agents that simulate human and animal behavior and software agents that support human‐computer interactions. In this article we first comprehensively review both types of agents. Then we argue that both these categories of agents borrow from Artificial Intelligence (AI) research, requiring them to share the characteristics of and be similar to AI agents. We also argue that geospatial agents form a distinct category of AI agents because they are explicit about geography and geographic data models. Our overall goal is to first capture the diversity of, and then define and categorize GIScience agent research into geospatial agents, thereby capturing the diversity of agent‐oriented architectures and applications that have been developed in the recent past to present a holistic review of geospatial agents.     

MaskMy.XYZ: An easy‐to‐use tool for protecting geoprivacy using geographic masks

Geographic masks are techniques used to protect privacy when publishing sensitive data in maps, but are not well adopted among researchers and may be difficult to execute for some GIS users. We developed a client‐side web application called MaskMy.XYZ that makes geographic masking easy to perform. It executes donut geomasking, a well‐known geographic mask, on thousands of points in seconds, and visualizes the original and masked point patterns in an integrated web map for visual comparison. MaskMy.XYZ also features metrics for both privacy protection and information loss, and allows users to rapidly and iteratively adjust masking parameters based on these metrics. The user interface was designed to prioritize usability, and clear documentation has been included to educate users about geographic masks, which is otherwise only found in niche literatures. By developing this application, we hope that geographic masks will be more widely adopted such that privacy is better protected in research.     

Classifiers vs. input variables—The drivers in image classification for land cover mapping

The study investigates the performance of image classifiers for landscape-scale land cover mapping and the relevance of ancillary data for the classification success in order to assess and to quantify the importance of these components in image classification. Specifically tested are the performance of maximum likelihood classification (MLC), artificial neural networks (ANN) and discriminant analysis (DA) based on Landsat7 ETM+ spectral data in combination with topographic measures and NDVI. ANN produced high accuracies of more than 75% also with limited input information, while MLC and DA produced comparable results only by incorporating ancillary data into the classification process. The superiority of ANN classification was less pronounced on the level of the single land cover classes. The use of ancillary data generally increased classification accuracy and showed a similar potential for increasing classification accuracy than the selection of the classifier. Therefore, a stronger focus on the development of appropriate and optimised sets of input variables is suggested. Also the definition and selection of land cover classes has shown to be crucial and not to be simply adaptable from existing land cover class schemes. A stronger research focus towards discriminating land cover classes by their typical spectral, topographic or seasonal properties is therefore suggested to advance image classification.     

Edge Enhancement for Remote Sensing Image Using Norm Algorithm in L2 Vector Space

The Householder transformation-norm structure function in L2 vector space of linear algebra is introduced, and the edge enhancement for remote sensing images is realized. The experiment result is compared with traditional Laplacian and Sobel edge enhancements and it shows that the effect of the new method is better than that of the traditional algorithms.     

Estimation of parameters in a split functional model of geodetic observations (<Emphasis Type="Italic">M</Emphasis><Subscript>split</Subscript> estimation)

The paper presents a method of estimating parameters in two competitive functional models. The models considered here are concerned with the same observation set and are based on the assumption that an observation may result from a realization of either of two different random variables. These variables differ from one another at least in the main characteristic (for example, outliers can be realizations of one variable). A quantity that describes the opportunity of identifying a single observation with one random variable is assumed to be known. That quantity, called the elementary split potential, is strictly referred to the amount of information that an observation can provide about two competitive assumptions concerning the observation distribution. Parameter assessments that maximize the global elementary split potential (concerning all observations), are called M _split estimators. A generalization of M _split estimation presented in the paper refers to the theoretical foundation of M-estimation. An erratum to this article can be found at     

Analysis of Object Depth Effects on Accuracy of Dimensional Shape in X and Y Directions Using Single Non-metric Image

In general, to reconstruct the accurate shape of buildings, we need at least one stereomodel (two photographs) for each building. In most cases, however, only a single non-metric photograph is availabl...     

The geopotential value <Emphasis Type="Italic">W</Emphasis><Subscript>0</Subscript> for specifying the relativistic atomic time scale and a global vertical reference system

The TOPEX/Poseidon (T/P) satellite alti- meter mission marked a new era in determining the geopotential constant W ₀. On the basis of T/P data during 1993–2003 (cycles 11–414), long-term variations in W ₀ have been investigated. The rounded value W ₀ = 62636856.0 ± 0.5) m ² s ⁻² has already been adopted by the International Astronomical Union for the definition of the constant L _G = W ₀/c ² = 6.969290134 × 10⁻¹⁰ (where c is the speed of light), which is required for the realization of the relativistic atomic time scale. The constant L _G , based on the above value of W ₀, is also included in the 2003 International Earth Rotation and Reference Frames Service conventions. It has also been suggested that W ₀ is used to specify a global vertical reference system (GVRS). W ₀ ensures the consistency with the International Terrestrial Reference System, i.e. after adopting W ₀, along with the geocentric gravitational constant (GM), the Earth’s rotational velocity (ω) and the second zonal geopotential coefficient (J ₂) as primary constants (parameters), then the ellipsoidal parameters (a,α) can be computed and adopted as derived parameters. The scale of the International Terrestrial Reference Frame 2000 (ITRF2000) has also been specified with the use of W ₀ to be consistent with the geocentric coordinate time. As an example of using W ₀ for a GVRS realization, the geopotential difference between the adopted W ₀ and the geopotential at the Rimouski tide-gauge point, specifying the North American Vertical Datum 1988 (NAVD88), has been estimated.     

<Emphasis Type="Italic">C</Emphasis>/<Emphasis Type="Italic">N</Emphasis><Subscript>0</Subscript> estimators for high-sensitivity snapshot GNSS receivers

We address the problem of estimating the carrier-to-noise ratio (C/N₀) in weak signal conditions. There are several environments, such as forested areas, indoor buildings and urban canyons, where high-sensitivity global navigation satellite system (HS-GNSS) receivers are expected to work under these reception conditions. The acquisition of weak signals from the satellites requires the use of post-detection integration (PDI) techniques to accumulate enough energy to detect them. However, due to the attenuation suffered by these signals, estimating their C/N₀ becomes a challenge. Measurements of C/N₀ are important in many applications of HS-GNSS receivers such as the determination of a detection threshold or the mitigation of near-far problems. For this reason, different techniques have been proposed in the literature to estimate the C/N₀, but they only work properly in the high C/N₀ region where the coherent integration is enough to acquire the satellites. We derive four C/N₀ estimators that are specially designed for HS-GNSS snapshot receivers and only use the output of a PDI technique to perform the estimation. We consider four PDI techniques, namely non-coherent PDI, non-quadratic non-coherent PDI, differential PDI and truncated generalized PDI and we obtain the corresponding C/N₀ estimator for each of them. Our performance analysis shows a significant advantage of the proposed estimators with respect to other C/N₀ estimators available in the literature in terms of estimation accuracy and computational resources.