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With methods for processing remote sensing data becoming widely available, the ability to quantify changes in spatial data and to evaluate the distribution of diverse landforms across target areas in datasets becomes increasingly important. One way to approach this problem is through satellite image processing. In this paper, we primarily focus on the methods of the unsupervised classification of the Landsat OLI/TIRS images covering the region of the Qena governorate in Upper Egypt. The Qena Bend of the Nile River presents a remarkable morphological feature in Upper Egypt, including a dense drainage network of wadi aquifer systems and plateaus largely dissected by numerous valleys of dry rivers. To identify the fluvial structure and stream network of the Wadi Qena region, this study addresses the problem of interpreting the relevant space-borne data using R, with an aim to visualize the land surface structures corresponding to various land cover types. To this effect, high-resolution 2D and 3D topographic and geologic maps were used for the analysis of the geomorphological setting of the Qena region. The information was extracted from the space-borne data for the comparative analysis of the distribution of wadi streams in the Qena Bend area over several years: 2013, 2015, 2016, 2019, 2022, and 2023. Six images were processed using computer vision methods made available by R libraries. The results of the k-means clustering of each scene retrieved from the multi-temporal images covering the Qena Bend of the Nile River were thus compared to visualize changes in landforms caused by the cumulative effects of geomorphological disasters and climate–environmental processes. The proposed method, tied together through the use of R scripts, runs effectively and performs favorably in computer vision tasks aimed at geospatial image processing and the analysis of remote sensing data. [ABSTRACT FROM AUTHOR]

Augmented reality (AR) combines information from databases with information derived directly through the human senses or different sensors. AR is the organic, real-time, and dynamically overlaying virtual images created by computers and other information on a real environment that the observer can see. Virtual information changes according to the movement of the observer, as if that virtual information truly exists in the real world. The convergence of geographic information systems (GIS), web mapping, mobile technology, and augmented reality (AR) implies the emergence of location based systems (LBS) and, in turn, the diminishing use of traditional analog maps by smartphone users. The focus and review of current achievements in this subject on the wider trends of the use of AR also prove that coding of some data and metadata on an image or a map (both in digital and analogue form) permits inter alia the inclusion of, for example, a paper map or analogue image into the chain of digital devices use. Some solutions, remarks, and comments concerning functioning of the digitally augmented (printed) map and their digital counterparts within the information society are presented. [ABSTRACT FROM AUTHOR]

There has been an enormous technological boom that impacted all areas of geoscience in the past few decades. Part of the change was also the process of democratization of cartography as well as geographic information systems (GIS), together with new approaches that have emerged, bringing social dimension into cartography and GIS. These new approaches were variously labelled as critical cartography, collaborative mapping, digital citizenship, Bottom-up GIS and Participatory GIS. The paper describes the role of collaborative mapping and digital participation in the process of community building and community assets mapping. Secondly, we will use the examples of Kenya and Peru to support our findings of community development. Thirdly, we will discuss a possible further development within the use of OpenStreetMap (OSM) for remote communities. The analysis compares approaches and experiences in different countries on different continents. [ABSTRACT FROM AUTHOR]

The filter bank multicarrier employing offset quadrature amplitude modulation (FBMC/OQAM) is a candidate transmission scheme for 5G wireless communication systems. However, it has a high peak-to-average power ratio (PAPR). Due to the nature of overlapped signal structure of FBMC/OQAM, conventional PAPR reduction schemes cannot work effectively. A hybrid PAPR reduction scheme based on selective mapping (SLM) and multi data block partial transmit sequence (M-PTS) methods is proposed for FBMC/OQAM signals in this paper. Different from the simple SLM-PTS method, the proposed hybrid algorithm takes into account the overlapping effect of multiple adjacent data blocks on its PTS process. From simulation results, it can be obtained that the proposed method can offer a significant PAPR reduction performance improvement compared with the SLM, PTS and SLM-PTS methods. The proposed method can effectively reduce the PAPR in FBMC/OQAM systems. [ABSTRACT FROM AUTHOR]