Search Results (3)

The global navigation satellite systems (GNSS) have become important in conjunction with the advancement of technology, in order to improve the accuracy of positioning and navigation on mobile devices. In the current project, a mobile application for navigation using the network transport of restricted test case modeling (RTCM) via internet protocol (NTRIP) was developed, and it has been focused on the autonomous mobility of people with visual disabilities. This occurred through a web viewer that stores the base cartography in a genome database (GDB). Such information is integrated into the application interface with Java Script language within the Android Studio platform, with a personalized design. This incorporates a screen reader for selection, navigation and direction of destinations, in addition to an early warning system for obstacles. Additionally, a differential position correction was implemented using the BKG Ntrip Client (BNC) software, for the adjustment of coordinates with the precise point positioning (PPP) method through streams in the format of RTCM with casters EPEC3, IGS03 and BCEP00BKG0. The evaluation of the application was performed using the National Standard for Spatial Data Accuracy (NSSDA), establishing 30 control points. These were obtained through the fast static method, in order to compare the horizontal accuracy of the observations in static and navigation modes between high-end and mid-range mobile devices. Full article

Digital elevation models (DEMs) are highly relevant geospatial products, and their positional accuracy has demonstrated influence on elevation derivatives (e.g., slope, aspect, curvature, etc.) and GIS results (e.g., drainage network and watershed delineation, etc.). The accuracy assessment of the DEMs is usually based on analyzing the altimetric component by means of positional accuracy assessment methods that are based on the use of a normal distribution for error modeling but, unfortunately, the observed distribution of the altimetric errors is not always normal. This paper proposes the application of a finite mixture model (FMM) to model altimetric errors. The way to adjust the FMM is provided. Moreover, the behavior under sampling is analyzed when applying different positional accuracy assessment standards such as National Map Accuracy Standards (NMAS), Engineering Map Accuracy Standard (EMAS) and National Standard for Spatial Data Accuracy (NSSDA) under the consideration of the FMM and the traditional approach-based one-single normal distribution model (1NDM). For the NMAS, the FMM performs statistically much better than the 1NDM when considering all the tolerance values and sample sizes. For the EMAS, the type I error level is around 3.5 times higher in the case of the 1NDM than in the case of the FMM. In the case of the NSSDA, as it has been applied in this research (simple comparison of values, not hypothesis testing), there is no great difference in behavior. The conclusions are clear; the FMM offers results that are always more consistent with the real distribution of errors, and with the supposed statistical behavior of the positional accuracy assessment standard when based on hypothesis testing. Full article

Point-based standard methodologies (PBSM) suggest using ‘at least 20’ check points in order to assess the positional accuracy of a certain spatial dataset. However, the reason for decreasing the number of checkpoints to 20 is not elaborated upon in the original documents provided by the mapping agencies which develop these methodologies. By means of theoretical analysis and experimental tests, several authors and studies have demonstrated that this limited number of points is clearly insufficient. Using the point-based methodology for the automatic positional accuracy assessment of spatial data developed in our previous study Ruiz-Lendínez, et al (2017) and specifically, a subset of check points obtained from the application of this methodology to two urban spatial datasets, the variability of National Standard for Spatial Data Accuracy (NSSDA) estimations has been analyzed according to sample size. The results show that the variability of NSSDA estimations decreases when the number of check points increases, and also that these estimations have a tendency to underestimate accuracy. Finally, the graphical representation of the results can be employed in order to give some guidance on the recommended sample size when PBSMs are used. Full article