Streaming Applications Research Articles

Development and Assessment of a Social Media-Based Construct of Firearm Ownership: Computational Derivation and Benchmark Comparison.

Gun violence research is characterized by a dearth of data available for measuring key constructs. Social media data may offer a potential opportunity to significantly reduce that gap, but developing methods for deriving firearms-related constructs from social media data and understanding the measurement properties of such constructs are critical precursors to their broader use. This study aimed to develop a machine learning model of individual-level firearm ownership from social media data and assess the criterion validity of a state-level construct of ownership. We used survey responses to questions on firearm ownership linked with Twitter data to construct different machine learning models of firearm ownership. We externally validated these models using a set of firearm-related tweets hand-curated from the Twitter Streaming application programming interface and created state-level ownership estimates using a sample of users collected from the Twitter Decahose application programming interface. We assessed the criterion validity of state-level estimates by comparing their geographic variance to benchmark measures from the RAND State-Level Firearm Ownership Database. We found that the logistic regression classifier for gun ownership performs the best with an accuracy of 0.7 and an F1-score of 0.69. We also found a strong positive correlation between Twitter-based estimates of gun ownership and benchmark ownership estimates. For states meeting a threshold requirement of a minimum of 100 labeled Twitter users, the Pearson and Spearman correlation coefficients are 0.63 (P<.001) and 0.64 (P<.001), respectively. Our success in developing a machine learning model of firearm ownership at the individual level with limited training data as well as a state-level construct that achieves a high level of criterion validity underscores the potential of social media data for advancing gun violence research. The ownership construct is an important precursor for understanding the representativeness of and variability in outcomes that have been the focus of social media analyses in gun violence research to date, such as attitudes, opinions, policy stances, sentiments, and perspectives on gun violence and gun policy. The high criterion validity we achieved for state-level gun ownership suggests that social media data may be a useful complement to traditional sources of information on gun ownership such as survey and administrative data, especially for identifying early signals of changes in geographic patterns of gun ownership, given the immediacy of the availability of social media data, their continuous generation, and their responsiveness. These results also lend support to the possibility that other computationally derived, social media-based constructs may be derivable, which could lend additional insight into firearm behaviors that are currently not well understood. More work is needed to develop other firearms-related constructs and to assess their measurement properties.

The Influence of 'Ome TV': Exploring the Impact on Students' Listening Skills through Video Live Streaming

This quantitative research study investigates the influence of the video live streaming application 'Ome TV' on students' listening skills. Undergraduate students from a vocational university in Makassar, Indonesia participated in the study, which utilized an online questionnaire to collect data on participants' listening abilities and their experiences with 'Ome TV'. The study aims to investigate the influence of 'Ome TV' on students' listening skills, identify challenges faced by students in using the application for listening practice, understand students' perceptions of the application's effectiveness, and provide recommendations for optimizing the integration of video live streaming applications in language learning environments. The findings reveal that most students perceived an improvement in their listening skills through 'Ome TV'; however, difficulties such as infrequent usage, limited improvement in understanding words with similar sounds, security concerns, and network quality issues were reported. The study concludes with practical recommendations for educators, curriculum designers, and developers to enhance the use of video live streaming applications in language learning settings. These findings contribute to the understanding of the potential benefits and challenges associated with video live streaming applications for improving students' listening proficiency.

Analisis Kepuasan Pengguna Terhadap Aplikasi Spotify Menggunakan Metode End User Computing Satisfaction (EUCS)

Music streaming applications are applications that allow users to listen to legal or online music. Spotify is currently known as the application that provides the largest music streaming service, digital music, podcast, and video service with access to millions of songs and other content from various creators worldwide. This research concerns the analysis of user satisfaction with the Spotify application. This research was conducted to understand and determine user satisfaction with the Spotify application. The method used in this research is End User Computing Satisfaction (EUCS). There are also problems with this music application related to the competition system when listening to music online. The sampling technique uses Lemeshow sampling (based on coincidence) with a total of 100 respondents using a research instrument in the form of a questionnaire using a Likert scale, which is a scale used to measure perceptions, attitudes, or opinions of users towards the Spotify application. shows that the level of user satisfaction from the five variables shows that the Content variable gets the highest satisfaction with a total percentage of 78.5%, the Accuracy variable gets high satisfaction with a total percentage of 77.5%, the Format variable gets the highest satisfaction with a total percentage of 79.9%, the Variable Ease of Use gets high satisfaction with a percentage of 84.6%, the Timelines variable gets high satisfaction with a percentage of 78.9%.

Technology Capitalization for Javanese Cultural Broadcast Serving Surabaya Urban Community

This study explored the use of technology in Radio Media 90.1 FM for broadcasting Javanese cultural programs to increase capital. The biggest audience for this radio is Surabaya's urban community, which loves Javanese culture. This study uses a qualitative method with a virtual ethnographic approach so that researchers can be participatory online and offline with broadcasting activities. Researchers get data about programming policies and activities of the Javanese culture-lover community at the time of the broadcast. Five informants in this study (WID, ANT, SAM, GEN, MUS representing elements of managers, programmers, broadcasters, technicians, and listeners). This study found that the technology adaptation for capitalization used is audio streaming applications and Facebook. Through the Facebook account (https://www.facebook.com/Media90.1FM), listeners build a virtual community of Javanese culture lovers. The study concludes that the interaction of the Javanese culture lover community on Facebook (viewers, likes, and comments) is a form of technology capitalization applied by radio Media 90,1 FM as data advertisers can directly utilize

Intelligent resource allocation scheme for cloud-edge-end framework aided multi-source data stream

To support multi-source data stream generated from Internet of Things devices, edge computing emerges as a promising computing pattern with low latency and high bandwidth compared to cloud computing. To enhance the performance of edge computing within limited communication and computation resources, we study a cloud-edge-end computing architecture, where one cloud server and multiple computational access points can collaboratively process the compute-intensive data streams that come from multiple sources. Moreover, a multi-source environment is considered, in which the wireless channel and the characteristic of the data stream are time-varying. To adapt to the dynamic network environment, we first formulate the optimization problem as a markov decision process and then decompose it into a data stream offloading ratio assignment sub-problem and a resource allocation sub-problem. Meanwhile, in order to reduce the action space, we further design a novel approach that combines the proximal policy optimization (PPO) scheme with convex optimization, where the PPO is used for the data stream offloading assignment, while the convex optimization is employed for the resource allocation. The simulated outcomes in this work can help the development of the application of the multi-source data stream.

When Virtual Reality Meets Rate Splitting Multiple Access: A Joint Communication and Computation Approach

Rate Splitting Multiple Access (RSMA) has emerged as an effective interference management scheme for applications that require high data rates. Although RSMA has shown advantages in rate enhancement and spectral efficiency, it has yet not to be ready for latency-sensitive applications such as virtual reality streaming, which is an essential building block of future 6G networks. Unlike conventional High-Definition streaming applications, streaming virtual reality applications requires not only stringent latency requirements but also the computation capability of the transmitter to quickly respond to dynamic users' demands. Thus, conventional RSMA approaches usually fail to address the challenges caused by computational demands at the transmitter, let alone the dynamic nature of the virtual reality streaming applications. To overcome the aforementioned challenges, we first formulate the virtual reality streaming problem assisted by RSMA as a joint communication and computation optimization problem. A novel multicast approach is then proposed to cluster users into different groups based on a Field-of-View metric and transmit multicast streams in a hierarchical manner. After that, we propose a deep reinforcement learning approach to obtain the solution for the optimization problem. Extensive simulations show that our framework can achieve the millisecond-latency requirement, which is much lower than other baseline schemes.

Secure Real-Time Multimedia Data Transmission from Low-Cost UAVs with A Lightweight AES Encryption

In the context of real-time wireless multimedia transmission from Unmanned Aerial Vehicles (UAVs), equipped with microphone arrays and cameras, data security is crucial. Data security should be guaranteed from the acquisition onboard the drones to its delivery to the destination node or ground station. A real-time light-weight application-level Advanced Encryption Standard (AES) encryption algorithm has been considered to be applied to multimedia data and supported by the single-board-computers mounted onboard the drones, with low computational power. The study shows that the main advances are related to the design of a streaming application in a transmission architecture for military counter-UAV scenarios that is effective, secure, quick, and trustworthy for audio and video data. A multiple synchronous key management is also newly experimented. The thorough experiments have confirmed the effective validity of the encryption and decryption processes, even in the presence of adverse radio spectrum conditions.

Streaming From the Air : Enabling Drone-Sourced Video Streaming Applications on 5G Open-RAN Architectures

Enabling high data-rate uplink cellular connectivity for drones is a challenging problem, since a flying drone has a higher likelihood of having line-of-sight propagation to base stations that terrestrial UEs normally do not have line-of-sight to. This may result in uplink inter-cell interference and uplink performance degradation for the neighboring ground UEs when drones transmit at high data-rates (e.g., video streaming). We address this problem from a cellular operator's standpoint to support drone-sourced video streaming of a point of interest. We propose a low-complexity, closed-loop control system for Open-RAN architectures that jointly optimizes the drone's location in space and its transmission directionality to support video streaming and minimize its uplink interference impact on the network. We prototype and experimentally evaluate the proposed control system on a dedicated outdoor multi-cell RAN testbed, which is the first measurement campaign of its kind. Furthermore, we perform a large-scale simulation assessment of the proposed control system using the actual cell deployment topologies and cell load profiles of a major US cellular carrier. The proposed Open-RAN control scheme achieves an average 19% network capacity gain over traditional BS-constrained control solutions and satisfies the application data-rate requirements of the drone (e.g., to stream an HD video).

ACPSEM position paper on ROMP scope of practice and staffing levels for magnetic resonance linear accelerators

The purpose of this position paper is to outline the ACPSEM recommendations on Medical Physicist scope of practice and staffing levels, as they relate to the use of dedicated MRI-Linacs in the treatment of patients. A core function of Medical Physicists is to safely implement changes in medical practice via the introduction of new technology and to ensure high quality radiation oncology services are provided to patients. Determining the feasibility of MRI-Linacs in any existing setting, or in establishing a new site, mandates the knowledge and services of Radiation Oncology Medical Physicists (ROMPs) as the Qualified Experts within this setting. ROMPs are key members of the multi-disciplinary team which will be required to steer the successful establishment of MRI Linac infrastructure within departments. To support efficient implementation, ROMPs must be embedded in the process from the start, including any feasibility study, initiation of the project, and development of the business case. ROMPs must be retained throughout all stages of acquisition, service development, and ongoing clinical use and expansion. The number of MRI-Linacs in Australia and New Zealand is growing. This expansion is occurring in parallel with rapid technological evolution, expanding tumour stream applications, and increasing consumer uptake. Growth and applications of MRI-Linac therapy will continue to occur beyond current known horizons, via development on the MR-Linac platform itself and through the migration of learning from this platform to conventional Linacs (known horizons for example include the use of daily, online image guided adaptive radiotherapy and MRI data informing decision making for planning and treatment before and throughout treatment courses). Clinical use, research and development will be a significant component of expanding patient access to MRI-Linac treatment and there will be an ongoing need to attract and retain ROMPs to initially establish services and in particular to drive service development and delivery for the life of the Linacs. MRI and Linac technologies mean it is necessary to perform a specialized workforce assessment for these devices, distinct from those employed for conventional Linacs and associated services. MRI-Linacs are complex, have a heightened risk profile compared to standard Linacs, and are unique in their treatment of patients. Accordingly, the workforce needs for MRI-Linacs are greater than for standard Linacs. To ensure safe and high-quality Radiation Oncology patient services are provided, it is recommended that staffing levels should be based on the 2021 ACPSEM Australian Radiation Workforce model and calculator using the MRI-Linac specific ROMP workforce modelling guidelines outlined in this paper. The ACPSEM workforce model and calculator are closely aligned with other Australian/New Zealand and international benchmarks.

Kaseteista suoratoistoon

From Compact Cassettes to Music Streaming Services: The Sociocultural Change of Background Music Industry&#x0D; The history of electronically mediated background music in Finland can be divided into era of scarcity, era of availability and era of plenty. The centralised and regulated supply of background music of the 1970s was followed by re-regulation of media and establishment of commercial radio stations increasing music consumption and listening to it while engaged to other activities. Analogue and tape-based solutions were first replaced with physical digital platforms and programming and finally with music streaming applications and music contents of the international media houses.&#x0D; The transformation of music platforms, music delivery channels and modes of listening are closely related to histories of technology, ­the social use of music and agencies of background music entrepreneurs. The article clarifies the transforming relationship of the environment, listening and music from the 1970s to the present day with the help ­of recent and archived interviews of representatives of background music companies.&#x0D; Keywords: background music; music cultures; anthropology of sound

Automated Atmospheric Correction of Nanosatellites Using Coincident Ocean Color Radiometer Data

Here we present a machine-learning-based method for utilizing traditional ocean-viewing satellites to perform automated atmospheric correction of nanosatellite data. These sensor convolution techniques are required because nanosatellites do not usually possess the wavelength combinations required to atmospherically correct upwelling radiance data for oceanographic applications; however, nanosatellites do provide superior ground-viewing spatial resolution (~3 m). Coincident multispectral data from the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (Suomi NPP VIIRS; referred to herein as “VIIRS”) were used to remove atmospheric contamination at each of the nanosatellite’s visible wavelengths to yield an estimate of spectral water-leaving radiance [Lw(l)], which is the basis for surface ocean optical products. Machine learning (ML) algorithms (KNN, decision tree regressors) were applied to determine relationships between Lw and top-of-atmosphere (Lt)/Rayleigh (Lr) radiances within VIIRS training data, and then applied to test cases for (1) the Marine Optical Buoy (MOBY) in Hawaii and (2) the AErosol RObotic Network Ocean Color (AERONET-OC), Venice, Italy. For the test cases examined, ML-based methods appeared to improve statistical results when compared to alternative dark spectrum fitting (DSF) methods. The results suggest that ML-based sensor convolution techniques offer a viable path forward for the oceanographic application of nanosatellite data streams.

Curtailing fake news creation and dissemination in Nigeria: Twitter social network and sentiment analysis approaches

Influencers create and facilitate dissemination of information in a network and can shape the attitudes and beliefs of members of a social network. Identifying the influencers and their relations, as well as the sentiment of tweets being disseminated in the network on some selected keywords can help curtail fake news creation and dissemination in the network. This study uses a sequential mixed-methods design with a quantitative method followed by qualitative methods. The quantitative data were collected using Mozdeh big data analysis software. Mozdeh software was used to collect tweets through Twitter’s Streaming application programming interface to build a corpus of tweets, collected from 1 January 2016 to 30 June 2021. The study found two major actors/influencers involved in the creation and dissemination of fake news on the tweeter social network studied. The study further found overall Av. Pos. − Av. Neg. was −0.9935. Data collected were on some specific trending keywords on a particular region in Nigeria. Identifying and monitoring the tweets of influencers in a network can aid in debunking fake news immediately after dissemination and discourage the use of offensive words in tweets. The results revealed major influencers responsible for creating and disseminating fake news on some trending issues in Nigeria.

Public Figure Vaccination Rhetoric and Vaccine Hesitancy: Retrospective Twitter Analysis.

Social media has emerged as a critical mass communication tool, with both health information and misinformation now spread widely on the web. Prior to the COVID-19 pandemic, some public figures promulgated anti-vaccine attitudes, which spread widely on social media platforms. Although anti-vaccine sentiment has pervaded social media throughout the COVID-19 pandemic, it is unclear to what extent interest in public figures is generating anti-vaccine discourse. We examined Twitter messages that included anti-vaccination hashtags and mentions of public figures to assess the connection between interest in these individuals and the possible spread of anti-vaccine messages. We used a data set of COVID-19-related Twitter posts collected from the public streaming application programming interface from March to October 2020 and filtered it for anti-vaccination hashtags "antivaxxing," "antivaxx," "antivaxxers," "antivax," "anti-vaxxer," "discredit," "undermine," "confidence," and "immune." Next, we applied the Biterm Topic model (BTM) to output topic clusters associated with the entire corpus. Topic clusters were manually screened by examining the top 10 posts most highly correlated in each of the 20 clusters, from which we identified 5 clusters most relevant to public figures and vaccination attitudes. We extracted all messages from these clusters and conducted inductive content analysis to characterize the discourse. Our keyword search yielded 118,971 Twitter posts after duplicates were removed, and subsequently, we applied BTM to parse these data into 20 clusters. After removing retweets, we manually screened the top 10 tweets associated with each cluster (200 messages) to identify clusters associated with public figures. Extraction of these clusters yielded 768 posts for inductive analysis. Most messages were either pro-vaccination (n=329, 43%) or neutral about vaccination (n=425, 55%), with only 2% (14/768) including anti-vaccination messages. Three main themes emerged: (1) anti-vaccination accusation, in which the message accused the public figure of holding anti-vaccination beliefs; (2) using "anti-vax" as an epithet; and (3) stating or implying the negative public health impact of anti-vaccination discourse. Most discussions surrounding public figures in common hashtags labelled as "anti-vax" did not reflect anti-vaccination beliefs. We observed that public figures with known anti-vaccination beliefs face scorn and ridicule on Twitter. Accusing public figures of anti-vaccination attitudes is a means of insulting and discrediting the public figure rather than discrediting vaccines. The majority of posts in our sample condemned public figures expressing anti-vax beliefs by undermining their influence, insulting them, or expressing concerns over public health ramifications. This points to a complex information ecosystem, where anti-vax sentiment may not reside in common anti-vax-related keywords or hashtags, necessitating further assessment of the influence that public figures have on this discourse.

Stream Applications: A Consistent Active Management Approach for Multi-Core Cache

This paper proposes a novel method for managing cache consistency in multi-core systems when executing stream applications. The method involves arranging a mark cache for private data caches, which includes an optional integrality descriptor for shared reading and writing data states and shared data manipulation positions. The integrality descriptor identifies the current mode of operation for shared data in the private data cache. Additionally, the method utilizes a two-dimensional array register, referred to as the shared data manipulation position, with width N and depth M, where N distinguishes between different cache blocks and locking territories, while M corresponds to the number of cache blocks. This enables the identification of the cache capable or block corresponding to shared data during read and write operations. The proposed method offers simplicity, ease of operation, low hardware implementation cost, good extensibility, and strong configurability, ultimately improving system effectiveness.

BUSINESS COMMUNICATION STRATEGY ON LIVE STREAMING APPLICATION IN INDONESIA

This study examines how the business communication strategy of the live streaming application in Indonesia. This research used a qualitative approach and a case study at Booyah Live Streaming applications. Booyah Live is a live-streaming and community platform designed for gaming enthusiasts worldwide, including Indonesia. This study elaborated business communication strategies by using the Shannon and Weaver communication model. The finding showed that live-streaming business communication is carried out online and offline. They used online media and the company's internal systems that can be accessed by each local team in every country. This platform always pays attention to every component of strategy in running a live streaming platform business. These are quality, product, process, and facility.

A Novel Strategy to Achieve Video Transcoding Using Cloud Computing

One of the fundamental challenges faced in deploying multimedia systems is delivering smooth and uninterrupted audio-visual information anywhere and anytime. In such systems, multimedia content is compressed within a certain format, this requires format conversion for various devices. Thus, a transcoding mechanism is required to make the content adaptive for various devices in the network. Video transcoding converts one digitally encoded format into another, this involves translating any file format containing video and audio at the same time. This is an essential feature for devices that do not support a specific format of media or have limited storage that requires a reduced file size. Through this paper, we provide a novel way of transcoding the block-based video coding schemes using cloud architecture by establishing a video pipelining architecture. The solution discussed in this paper would enable the end users to extract videos in any format and resolution seamlessly, combined with the scalability, reliability, and cost-effectiveness of the cloud. The proposed idea would be lucrative for all the video streaming applications that are currently relying on their legacy infrastructure for video transcoding.

Streaming Erasure Codes Over Multi-Access Relayed Networks

Many emerging multimedia streaming applications involve multiple users communicating under strict latency constraints. In this paper we study streaming codes for a network involving two source nodes, one relay node and a destination node. In this paper’s setting, each source node transmits a stream of messages, through the relay, to a destination, who is required to decode the messages under a strict delay constraint. For the case of a single source node, a class of streaming codes has been proposed by Fong et al., using the concept of delay-spectrum. The current paper presents a novel framework, which constructs streaming codes for a relayed multi-user setting by sequentially constructing the codes for each link. This requires a characterization of the set of all achievable delay spectra for a given rate, blocklength and number of erasures, beyond the specific choice considered by Fong et al. This characterization is presented in the paper for systematic codes. Using this novel framework, the first proposed scheme involves greedily selecting the rate on the link from relay to destination and using properties of the delay-spectrum to find feasible streaming codes that satisfy the required delay constraints. A closed form expression for the achievable rate region is provided, and conditions for when the proposed scheme is optimal are established by a natural outer bound. The second proposed scheme builds upon this approach, but uses a numerical optimization-based approach to improve the achievable rate region over the first scheme. Experimental results show that the proposed schemes achieve significant improvements over baseline schemes based on single-user codes.

An efficient congestion control in multimedia streaming using adaptive BRR and fuzzy butterfly optimization

AbstractRecently, various video streaming platforms such as YouTube, Netflix and so forth have achieved great growth in the media industry. However, uncertain dynamic activities such as low bandwidth, packet loss and rapid fluctuations, quality, and interrupted flow are still creating several complications in multimedia streaming applications and it is necessary to provide enhanced throughput and minimum frame delivery delay. Therefore, the significant objective of this article is to attain enhanced bandwidth allocation as well as a high packet scheduling rate. In this article, an adaptive BBR technique is proposed for transmitting real‐time video with minimum transmission delay and packet loss. The adaptive BBR mainly uses the pacing gain update mechanism to maintain different flow rates and minimal data in the bottleneck queue. Also, a fuzzy butterfly optimization (FBO) with a ranking strategy is proposed to attain optimal bandwidth allocation. The FBO algorithm with ranking strategy computes the minimal bandwidth needed by the model to satisfy the service requirement of every queue in a particular round. Then, the packet scheduling is done by employing an intelligent time slice‐based round‐robin (ITSBRR) technique. The ITSBRR algorithm is employed to enhance the throughput and end‐to‐end delay by implementing the time quantum‐based scheduling mechanism. Finally, the experimental analysis is carried out for various parameters namely single path transmission delay, packet loss; frame delay is computed for various techniques to compute the efficiency of the proposed system.

Improving the performance of web-streaming by super-resolution upscaling techniques

In recent years, we have seen significant progress in advanced image upscaling techniques, sometimes called super-resolution, ML-based, or AI-based upscaling. Such algorithms are now available not only in form of specialized software but also in drivers and SDKs supplied with modern graphics cards. Upscaling functions in NVIDIA Maxine SDK is one of the recent examples. However, to take advantage of this functionality in video streaming applications, one needs to (a) quantify the impacts of super-resolution techniques on the perceived visual quality, (b) implement video rendering incorporating super-resolution upscaling techniques, and (c) implement new bitrate+resolution adaptation algorithms in streaming players, enabling such players to deliver better quality of experience or better efficiency (e.g. reduce bandwidth usage) or both. Towards this end, in this paper, we propose several techniques that may be helpful to the implementation community. First, we offer a model quantifying the impacts of super resolution upscaling on the perceived quality. Our model is based on the Westerink-Roufs model connecting the true resolution of images/videos to perceived quality, with several additional parameters added, allowing its tuning to specific implementations of super-resolution techniques. We verify this model by using several recent datasets including MOS scores measured for several conventional up-scaling and super-resolution algorithms. Then, we propose an improved adaptation logic for video streaming players, considering video bitrates, encoded video resolutions, player size, and the upscaling method. This improved logic relies on our modified Westerink-Roufs model to predict perceived quality and suggests choices of renditions that would deliver the best quality for given display and upscaling method characteristics. Finally, we study the impacts of the proposed techniques and show that they can deliver practically appreciable results in terms of the expected QoE improvements and bandwidth savings.

Topographic Mapping Capability Analysis of Moderate Resolution Imaging Camera (MoRIC) Imagery of Tianwen-1 Mars Mission

The Moderate Resolution Imaging Camera (MoRIC) onboard the orbiter of China’s Tianwen-1 Mars mission aims to map the surface of Mars globally and to explore its topography and geomorphology. This camera’s wide field of view can be used to overlap adjacent images sufficiently for topographic reconstruction. This study aimed to assess the topographic mapping potential of MoRIC images via a systematic accuracy analysis of MoRIC stereo mapping based on theoretical calculation and experimental validation. Experimental analyses were conducted using digital elevation models (DEMs) based on MoRIC images covering the Zhurong rover landing area and Mars Chart 11 region. Comparisons between these DEMs and reference DEMs revealed that the elevation accuracy of the former was approximately one ground sampling distance of the MoRIC image, which is consistent with the theoretical accuracy. Based on these results, MoRIC can be used to effectively map the Martian surface via stereo photogrammetry. These results serve as a useful guideline for topographic mapping based on MoRIC images and their application in various streams of research.