Relevant bibliographies by topics / Geospatial AI enabled Apps

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Academic literature on the topic 'Geospatial AI enabled Apps'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "Geospatial AI enabled Apps"

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Dr., Christine Wacta, and Edugaming Geo. "Project Matrix: a Virtual Experience Platform for RE-Conciling Human Ecology & Urban Ecology." INTERNATIONAL JOURNAL OF MULTIDISCIPLINARY RESEARCH AND ANALYSIS 06, no. 03 (2023): 850–58. https://doi.org/10.5281/zenodo.7687887.

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A flashback to previous centuries testifies extraordinary human-lead technological prowess; from the invention of the telescope in the 17th-century, the telephone in 19th-century, the Worldwide-Web and technological revolution of 20th-21st century; it is undeniable that Humans constantly strive to overcome challenges through technological innovation by empowering/advancing humanity. Yet, we’ve witnessed a global disruption by a pandemic that exposed unforeseen weaknesses and exacerbated existing issues (human negligence or not). Suddenly all well-structured and reliable system-organization failed, exposing inadequacies, causing chaos and harmful damages of interconnected components including the flows of people, health, matters, information, economy, politic, materials, communication networks, deadlines, etc. Covid-19 spread across the globe defeated technological advancements. How could that happen in such technological era? Is Human in control of digital evolution or is the technology we invent holding us on leash? Was technology in place to leverage and tackle risks? Where we not prepared to it, or were we focused on the wrong tracks? In any case, whether by failure or negligence, Covid-19 has made history and will continue generating ink flow for decades to come. It is to help address these multi layered-multidimensional and highly complex socio-technical and temporal systems that this project intends to develop a series of applications in alignment with the SDGs. The focus is to blend the capabilities of environment scanning Apps with that of the human habitus-emotions capture, tracing and feedback collector in time and place. It requires advance analytical studies to fully understand interdependencies while respecting the relationships of all subsystems.
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Li, Xinyi, Annabelle Yin, Ha Young Choi, Virginia Chan, Margaret Allman-Farinelli, and Juliana Chen. "Evaluating the Quality and Comparative Validity of Manual Food Logging and Artificial Intelligence-Enabled Food Image Recognition in Apps for Nutrition Care." Nutrients 16, no. 15 (2024): 2573. http://dx.doi.org/10.3390/nu16152573.

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For artificial intelligence (AI) to support nutrition care, high quality and accuracy of its features within smartphone applications (apps) are essential. This study evaluated popular apps’ features, quality, behaviour change potential, and comparative validity of dietary assessment via manual logging and AI. The top 200 free and paid nutrition-related apps from Australia’s Apple App and Google Play stores were screened (n = 800). Apps were assessed using MARS (quality) and ABACUS (behaviour change potential). Nutritional outputs from manual food logging and AI-enabled food-image recognition apps were compared with food records for Western, Asian, and Recommended diets. Among 18 apps, Noom scored highest on MARS (mean = 4.44) and ABACUS (21/21). From 16 manual food-logging apps, energy was overestimated for Western (mean: 1040 kJ) but underestimated for Asian (mean: −1520 kJ) diets. MyFitnessPal and Fastic had the highest accuracy (97% and 92%, respectively) out of seven AI-enabled food image recognition apps. Apps with more AI integration demonstrated better functionality, but automatic energy estimations from AI-enabled food image recognition were inaccurate. To enhance the integration of apps into nutrition care, collaborating with dietitians is essential for improving their credibility and comparative validity by expanding food databases. Moreover, training AI models are needed to improve AI-enabled food recognition, especially for mixed dishes and culturally diverse foods.
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Alzahrani, Adel Saeed, Valerie Gay, Ryan Alturki, and Mohammad J. AlGhamdi. "Towards Understanding the Usability Attributes of AI-Enabled eHealth Mobile Applications." Journal of Healthcare Engineering 2021 (December 21, 2021): 1–8. http://dx.doi.org/10.1155/2021/5313027.

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Mobile application (app) use is increasingly becoming an essential part of our daily lives. Due to their significant usefulness, people rely on them to perform multiple tasks seamlessly in almost all aspects of everyday life. Similarly, there has been immense progress in artificial intelligence (AI) technology, especially deep learning, computer vision, natural language processing, and robotics. These technologies are now actively being implemented in smartphone apps and healthcare, providing multiple healthcare services. However, several factors affect the usefulness of mobile healthcare apps, and usability is an important one. There are various healthcare apps developed for each specific task, and the success of these apps depends on their performance. This study presents a systematic review of the existing apps and discusses their usability attributes. It highlights the usability models, outlines, and guidelines proposed in previous research for designing apps with improved usability characteristics. Thirty-nine research articles were reviewed and examined to identify the usability attributes, framework, and app design conducted. The results showed that satisfaction, efficiency, and learnability are the most important usability attributes to consider when designing eHealth mobile apps. Surprisingly, other significant attributes for healthcare apps, such as privacy and security, were not among the most indicated attributes in the studies.
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Tseng, Hsiao-Ting, Chia-Lun Lo, and Chun-Chih Chen. "The Moderation Role of AI-Enabled Service Quality on the Attitude Toward Fitness Apps." Journal of Global Information Management 31, no. 1 (2023): 1–20. http://dx.doi.org/10.4018/jgim.318694.

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Artificial intelligent technology is rapidly transforming the fitness apps landscape by applying data mining technologies within given parameters. These wide-ranging AI-enabled services improve user interface and enhance customers' experience when exercising with the fitness apps. The current study integrated the four antecedents—technological functions, intrinsic information quality, perceived enjoyment, and social connection—to investigate the moderating influence of AI-enabled service quality on users' attitude toward physical activity. PLS-SEM was used to analyze and validate a sample of 170 participants. The findings posited that individuals' attitude toward physical activity is encouraged by the (1) technological functions (2) intrinsic information quality, and (3) perceived enjoyment. Further, the moderating role of AI-enabled service positively influencing the attitude toward physical activity with technological functions was also established.
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Yue, Peng, Kaixuan Wang, Hanwen Xu, Jianya Gong, and Longgang Xiang. "From Geospatial Data Cube to AI Cube: the Open Geospatial Engine (OGE) Approach." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4-2024 (October 18, 2024): 441–46. http://dx.doi.org/10.5194/isprs-annals-x-4-2024-441-2024.

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Abstract. The Earth Observation (EO) analytics are moving from local systems to online cloud computing platforms such as Google Earth Engine (GEE) and Open Geospatial Engine (OGE). A typical approach in existing efforts is to leverage geospatial data cubes with cloud computing to support large-scale big EO data analytics in Digital Earth systems. While online analytical processing (OLAP) can be enabled using the cube approach, it is still not clear how geospatial artificial intelligence (GeoAI) can be incorporated in data cubes to benefit the cube infrastructure. Such an investigation can consolidate the vision of an AI-ready SDI (Spatial Data Infrastructure). The paper presents a systematic approach to incorporate GeoAI models into geospatial data cubes to help create an AI Cube. It covers on-demand model retrieval, cube data and model integration, and distributed model inference. The approach is demonstrated in OGE, which is an EO cloud computing platform layered on the GeoCube implementation. The results show that such an AI Cube enriches a cube infrastructure with GeoAI capabilities, facilitates the on-demand coupling of cube data and GeoAI models, and improves the performance of GeoAI inference.
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Taneja, Avina. "AI enabled right swipe: Enhancing user experience in dating apps." Journal of Global Communication 17, no. 2 (2024): 117–25. https://doi.org/10.5958/0976-2442.2024.00024.0.

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Kirti, Vasdev. "Enhancing Network Security with GeoAI and Real-Time Intrusion Detection." International Journal on Science and Technology 16, no. 1 (2025): 1–8. https://doi.org/10.5281/zenodo.14802799.

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GeoAI (Geospatial Artificial Intelligence) combines geospatial data analysis with AI capabilities to enhance decision-making. Its application in network security offers a revolutionary approach to detecting and mitigating cyber threats. This paper explores the integration of GeoAI with real-time intrusion detection systems (IDS), discussing theoretical foundations, practical applications, and challenges. Case studies illustrate GeoAI’s role in identifying geographically contextualized cyber threats. We also examine the integration of machine learning, geospatial analytics, and real-time processing to improve network resilience. Challenges like data privacy and system complexity are discussed, alongside future trends in GeoAI-enabled network security.
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Sofianopoulos, Sofianos, Antigoni Faka, and Christos Chalkias. "SDI-Enabled Smart Governance: A Review (2015–2025) of IoT, AI and Geospatial Technologies—Applications and Challenges." Land 14, no. 7 (2025): 1399. https://doi.org/10.3390/land14071399.

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This paper presents a systematic, narrative review of 62 academic publications (2015–2025) that explore the integration of spatial data infrastructures (SDIs) with emerging smart city technologies to improve local governance. SDIs provide a structured framework for managing geospatial data and, in combination with IoT sensors, geospatial and 3D platforms, cloud computing and AI-powered analytics, enable real-time data-driven decision-making. The review identifies four key technology areas: IoT and sensor technologies, geospatial and 3D mapping platforms, cloud-based data infrastructures, and AI analytics that uniquely contribute to smart governance through improved monitoring, prediction, visualization, and automation. Opportunities include improved urban resilience, public service delivery, environmental monitoring and citizen engagement. However, challenges remain in terms of interoperability, data protection, institutional barriers and unequal access to technologies. To fully realize the potential of integrated SDIs in smart government, the report highlights the need for open standards, ethical frameworks, cross-sector collaboration and citizen-centric design. Ultimately, this synthesis provides a comprehensive basis for promoting inclusive, adaptive and accountable local governance systems through spatially enabled smart technologies.
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Arjun Varma, Jampana Venkata. "YOLOv8-Enabled Real-Time Crop Health Monitoring with Conversational Diagnosis and Geospatial Support." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47655.

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Abstract—Agriculture is a cornerstone of global economies, supplying food, employment, and raw materials for numerous industries. Yet, one of the sector’s enduring challenges is crop disease, which can drastically reduce yields and threaten food security. Traditional approaches to identifying plant diseases rely on manual inspections and expert evaluations, which are often slow, costly, and vulnerable to human error. Without early diagnosis, diseases can spread uncontrollably, leading to major economic setbacks for farmers and decreased crop output. To overcome these issues, this project introduces an AI- powered system for detecting and diagnosing crop diseases. It combines advanced deep learning, natural language processing (NLP), and geospatial mapping technologies. At its core is YOLOv8 (You Only Look Once, version 8), a powerful Convolu- tional Neural Network (CNN) designed for real-time image-based detection. Trained on a robust, annotated dataset from Roboflow, the model accurately identifies a variety of diseases affecting key crops such as rice, wheat, and maize. Beyond detection, the system includes an intelligent chatbot powered by Large Language Models (LLMs). This virtual as- sistant offers instant, tailored advice on diagnosis, treatment options, and preventive strategies. It provides farmers with user- friendly guidance in natural language, making it accessible even to those with limited technical knowledge. The chatbot serves as a virtual agricultural consultant, recommending effective pesticides, organic treatments, and disease management practices. A standout feature of this project is its geospatial mapping capability. By integrating OpenStreetMap’s Overpass API, the system helps farmers locate nearby agricultural supply stores after a disease is identified. This allows quick access to the necessary products like pesticides or fertilizers, helping farmers respond promptly to disease outbreaks. Overall, the system presents a comprehensive AI-driven approach to crop disease management by combining image- based detection, interactive chatbot support, and location-based resource mapping. By reducing the dependency on manual inspection, enhancing decision-making, and streamlining access to agricultural inputs, it promotes more efficient, tech-enabled farming. This real-time, intelligent solution not only boosts productivity but also minimizes economic losses, paving the way for a more sustainable and resilient agricultural future. Keywords: Plant Disease Detection, YOLOv8 CNN, Agricul- tural AI, Intelligent Chatbot, LLMs in Farming, Plant Pathology AI, GIS Mapping, OpenStreetMap API, Smart Farming, AI- Enhanced Agriculture.
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Ghosh, Samadrita, Stephanie Ness, and Shruti Salunkhe. "The Role of AI Enabled Chatbots in Omnichannel Customer Service." Journal of Engineering Research and Reports 26, no. 6 (2024): 327–45. http://dx.doi.org/10.9734/jerr/2024/v26i61184.

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Currently, organizations are progressively embracing artificial intelligence (AI) and chatbots to transform omnichannel customer service in the modern digital age. This study examines the revolutionary impact of artificial intelligence (AI) and chatbots in providing seamless, personalized, and efficient consumer experiences across various communication channels. AI technology, such as machine learning and natural language processing, enable organizations to analyze large volumes of data, predict client requirements, and offer immediate support. Chatbots, functioning as artificial intelligence-powered virtual assistants, have a key function in interacting with customers through natural language conversations and providing immediate assistance through several channels, including websites, mobile apps, and messaging platforms. Through the utilization of artificial intelligence (AI) and chatbots, organizations may optimize efficiency, responsiveness, and personalization in consumer interactions, resulting in heightened satisfaction and loyalty. Nevertheless, the implementation of AI and chatbots in omnichannel customer service also gives rise to ethical concerns, including data protection, transparency, and fairness, which need to be resolved in order to guarantee appropriate utilization of these technologies. Notwithstanding these difficulties, the capacity of AI and chatbots to revolutionize the customer service industry is unquestionable, providing organizations with novel prospects to distinguish themselves and provide extraordinary experiences in the digital era. This article offers valuable insights into the developing patterns, difficulties, and possibilities linked to AI and chatbots in Omnichannel customer service. It emphasizes the revolutionary influence of these technologies on the future of managing customer experience.
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Books on the topic "Geospatial AI enabled Apps"

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Choudhury, Tanupriya, Bappaditya Koley, Anindita Nath, Jung-Sup Um, and Atul Kumar Patidar, eds. Geo-Environmental Hazards using AI-enabled Geospatial Techniques and Earth Observation Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-53763-9.

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Nath, Anindita, Bappaditya Koley, and Atul Kumar Patidar. Geo-Environmental Hazards Using AI-Enabled Geospatial Techniques and Earth Observation Systems. Springer, 2024.

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Book chapters on the topic "Geospatial AI enabled Apps"

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Pandey, Sanskriti, Sharvari Phand, Suraj Sawant, and Amit Joshi. "Enhancing Environmental Sustainability: AI-Enabled Plastic Waste Classifications." In Sustainable Development and Geospatial Technology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65683-5_14.

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Yanyan, Zhang, Noorminshah A.Iahad, and Ahmad Fadhil Yusof. "Artificial Intelligence (AI)-Enabled Mobile Fitness Apps and Goal Attainment: Systematic Literature Review." In Studies in Computational Intelligence. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-75091-5_10.

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Halvadia, Nirav, and Aarathi Vaitheeswaran. "Expectation Confirmation and User Satisfaction in AI-Enabled Mobile Health Apps: Development of Theoretical Framework." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-1744-9_41.

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Darwish, Dina. "Geospatial AI Concepts and Fundamentals." In Advances in Geospatial Technologies. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-8054-3.ch001.

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In terms of its progress and the ways in which it might be applied in our everyday lives, geospatial artificial intelligence (Geo-AI) is an intriguing issue. When it comes to the economic and social growth of a region or country, one of the factors that contributes to this development is spatial planning. The primary focus of this investigation is based on spatial data, with the objective of maximizing the efficiency of land use of spatial data on the region as upstream data and developing GIS-based urban planning apps that automatically present the findings of analysis and predictions of urban objects. Also, the plans for urban areas for both spatial and land use can be made, and geospatial AI generates a significant amount of spatial data based on the characteristics of the environment and the geography. This is done in order to maximize the revenue that is generated by tourists. In the context of urban planning and the tourism industry, this Geo-AI can offer many advantages and benefits. This chapter discusses several topics related to geospatial AI, including geo-computation, geospatial AI applications, challenges facing it, and its future prospects.
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Mahajan, Yash, Muskan Sharma, Alba Meça, Narayan Vyas, and Rakesh Sharma. "11 Mathematical Optimization for Enhanced AI-Enabled Geospatial Intelligence." In Math Optimization for Artificial Intelligence. De Gruyter, 2025. https://doi.org/10.1515/9783111436180-011.

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Shankar, Sahana P., Deepak Varadam, Aryan Bharadwaj, Tanvi Saxena, Ronit Mohta, and Anirudh Shankar. "Artificial Intelligence for Defence." In Advances in Geospatial Technologies. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-7319-1.ch012.

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Technology is rapidly improving.The advancements in use have enabled research in previously unexplored areas.These include military, Big Data, Robotics, Blockchain, and other fields. In these areas, we employ AI and machine learning. One might think that AI and ML are the same, but they are not. Artificial intelligence is defined as a computer or robot's ability to perform any task assigned to humans, thereby simplifying our work. Once the work is completed, one must ensure that it is accurate and precise. This was accomplished through Machine Learning, a type of AI. As previously stated, AI and ML use various settings, including the military, hospitals, banking, video games, and others. In this paper, the use of AI and ML is mainly concentrated in the military. As a result, we have focused on the developments of AI and ML in the military rather than the trends of AI and ML in other areas in this chapter. With a basic understanding of how everything works, we provide a brief overview of military operations and developments in a few countries, such as China, Russia,United States
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Andriani, Sekar Putri, Padmanaba Adhyanacarira, Anis Fuad, and Ariani Arista Putri Pertiwi. "Comparison of Non-AI and AI-Enabled M-Health Platforms for COVID-19 Self Screening in Indonesia." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220703.

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This study aimed to analyze and differentiate the role of AI and no AI-supported m-health platforms for COVID-19 self-screening in Indonesia. We utilized a mysterious shopping method to develop four standardized cases with various severity levels of COVID-19 tested in Indonesia’s most popular mHealth platforms. We selected seven apps from the top 200 free mHealth apps in the “Medical” category in the Google Play Store equipped with COVID-19 symptom checkers. A total of 36 teleconsultations were performed in four chatbot-based, two apps supported with AI combined with a human-based approach, and three apps with the human-based process. Teleconsultations were recorded, classified, and analyzed compared with the COVID-19 guideline by the MoH of Indonesia. The study indicated that most of the self-screening provided questions that had consistently led to the COVID-19 condition such as cough, fever, and shortness of breath and followed the guideline from the national health authority.
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Paramarthalingam, Arjun, Ashokkumar Janarthanan, Amirthasaravanan Arivunambi, Srinivas Saravanan Ariyangavu, Hariprasath Senthamaraikannan, and Rekha Ganapathy. "AI-Powered Virtual Mouse Control Through Hand Gestures With Computer Vision." In Advances in Geospatial Technologies. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-8104-5.ch003.

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This article introduces an Artificial Intelligence (AI) enabled virtual mouse system that utilizes hand gestures and fingertip detection to operate computer mouse functions through AI and computer vision techniques. It serves as a convenient alternative to traditional physical mouse, offering users increased flexibility and accessibility in navigating and controlling their devices. In this article, the proposed work uses three modules such as OpenCV, MediaPipe and PyautoGUI to create the virtual mouse system. OpenCV library is used for its real-time computer vision functionality to help us to capture the hand using the web camera. The MediaPipe framework is used to detect the hand region using KLT Tracking Algorithm and then the K-cosine border tracking algorithm is used to identify various types of hand gesture movements to mimic the computer mouse cursor movement and scrolling operations. The PyAutoGUI module is used to perform appropriate mouse actions based on the recognized hand gestures.
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Aggarwal, Divya, Priya Bansal, and Anisha Goyal. "Nursing Cadre Skill Development for AI-Enabled Hospitals." In Advances in Healthcare Information Systems and Administration. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-5523-7.ch012.

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In the advancements-driven environment, the developing setting of healthcare in information and communication technology (ICT) is representing a transformative change in nursing practice. Nurses, known as critical in attaining Sustainable Development Goals (SDGs), are poised to influence new technologies for patient care delivery, mainly in developing nations. There is need to incorporate electronic health records (EHR), mobile apps, big data analytics, and artificial intelligence (AI) in nursing care across various positions. The chapter focuses on the implementation of AI in hospital settings for better patient outcomes. The building blocks of smart hospitals and their challenges are also highlighted. It is necessary to train the workforce in tech-related operations from time-to-time in order to have seamless operations.
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Matindike, Rumbidzai, and Danmore Ngoni Matutu. "AI for Community Engagement and Environmental Awareness." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0680-3.ch013.

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The use of artificial intelligence (AI) is becoming more powerful for transformative community engagement and raising environmental awareness, particularly in the Global South. This chapter examines how Ai-driven technologies can promote sustainable practices in resource-limited environments. To enable local communities make knowledgeable decisions about waste management, energy conservation and sustainable agriculture, the study makes use of case studies and illustrations of Ai applications, including chatbots, mobile apps and predictive analytics. Findings demonstrate that Ai systems effectively disseminate environmental knowledge, support participatory decision-making and drive behavioral change towards sustainability. Ai-enabled platforms improved access to localized environmental information, increasing community participation in conservation efforts. While AI offers immense potential to address ecological challenges, the discussion highlights barriers, including limited technological infrastructure, lack of digital literacy, and the need for culturally relevant solutions.
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Conference papers on the topic "Geospatial AI enabled Apps"

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Jin, Hulin, Zhiran Jin, and Yong-Guk Kim. "AI-Enabled Real-Time Vehicle Tracking Model with Mobile Apps." In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63445.2024.10762949.

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Shi, Zhonghao, Daeun Chung, Yao Du, Jiaxin Zhang, Shivani Raina, and Maja Mataric. "Is AI Ready to Support Speech Therapy for Children? A Systematic Review of AI-Enabled Mobile Apps for Pediatric Speech Therapy." In IDC '25: Interaction Design and Children. ACM, 2025. https://doi.org/10.1145/3713043.3728841.

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Joseph, Binu Thomas, Roberto Astesani, and Harish Maliekkal. "An Innovative and Intelligent Journey Management System for the Energy Industry." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22627-ea.

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Abstract Land transport management remains a significant HSE challenge for any organization involved in energy industry operations. With many remote operational locations and involvement of land transport contractors, achieving sustainable driving performance improvement is reliant on consistent implementation of robust journey management practices. This paper discusses an innovative journey management system app, which provides an intelligent and effective solution incorporating quality control assurance and optimization processes while providing the convenience of digital mobility. The features of this HSE digitally enabled solution include the use of a progressive web app (PWA)and artificial intelligence (AI) engine developed by in-house resources. The PWA was used to develop a user-friendly interface in seven different languages. AI and an interactive drag and drop map feature were introduced to reduce human error in workflow checks and provide functional flexibility. The solution provides corporate and personnel data security for any mobile user, whether internal to the company or contractors. Other incorporated features include an intelligent driver inactivity identifier and a simple to use pre-departure vehicle safety control checklist. The first release of the enhancements to the existing app was provided to approximately 15% of the global journey management mobile app users and additional contractor driver users. Use of the enhanced app is monitored using a dashboard to show adoption rate and trip information data. The app allows drivers to create their own trip in the app, reducing the reliance on company supervisors to create trips for them. In under a year of deployment, by incorporating contractor drivers into the digital journey management process there has been a reduction of 828,000 phone calls and associated manual trip management processing time, replaced by the same number of application transactions. The digital journey management app continues to enable effective and safety journey management processes are maintained in remote operations and pandemic conditions, in addition significant cost savings were achieved. The following are the key innovative functions the mobile apps brings: Flexibility and Security - Provides corporate and personnel data security for all users.Innovative architecture Allows easy app access and intuitive use.Multi-Language Support Enables use in multiple languages.Interactive drag and drop- Incorporated map feature, reduces data entry.Intelligent inactivity identifier- Drivers who have not used the app within a specified time period are automatically provided a refresher of the IOGP life-saving rule video.Safety Control Check User-friendly vehicle inspection checklist for every trip. The adoption and effective use of the new enhancements to the journey management app improve journey management efficiency and ultimately driving safety performance. The app is designed using resilient digital architecture to provide the user with additional features, and an intuitive user experience of these services to reduce the potential for human error in data entry.
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