Artificial Intelligence in Support of National Land Administration and Build-Back-Better Policies: A Technical and Policy Assessment of the Hellenic Cadastre and the Cross-Sectoral Reuse of Geospatial Infrastructure (HEPOS)
Aug 2026· Land· Vol 15, pp. 1545· 0 citations· 12 references
TL;DR
The Greek case is unique institutionally rather than technically: it repurposed a national CORS network for a citizen-facing train tracking platform as a short-term crisis response, alongside an incomplete ETCS rollout.
Abstract
In April 2024, the Hellenic Cadastre became one of Europe’s first land registries to use a generative AI model (a large language model served through Azure OpenAI) for the legal review of property deeds. Unlike similar European initiatives using classical NLP, Greece applied state-of-the-art generative AI to a massive legacy issue: 390 historical mortgage registries holding an estimated 600 million to one billion paper pages. By April 2026, the system had processed 310,000 acts, reducing the average per-act review time from about thirty minutes to under ten; a very large per-act cost reduction is also reported by the implementation partner, which we treat as a vendor-stated figure. Additionally, the cadastre’s geodetic infrastructure found a second use following the 2023 Tempi rail disaster. In 2026, the Hellenic Positioning System (HEPOS), a 98-station GNSS reference network, began providing corrections for Greece’s real-time train tracking platform. While satellite-based train positioning is not novel in Europe, where consortia such as CLUG have run a decade of research and pilots, this marks its operational deployment in Greece. The Greek case is unique institutionally rather than technically: it repurposed a national CORS network for a citizen-facing train tracking platform as a short-term crisis response, alongside an incomplete ETCS rollout. This paper documents both deployments, measures their impact, maps them onto the nine FELA pathways, and identifies transferable practices. Greece is not presented as a technological frontier, but as an example of how a country can put existing geospatial infrastructure and AI to rapid use in delivering build-back-better policies for the public, in line with the UN 2030 Agenda.
The global energy sector is going through a big change from Industry 5.0, which focuses on people working together, to Industry 6.0, which is based on cognitive automation. Artificial Intelligence (AI) has become a key part of making operations more resilient, streamlining workflows, cutting carbon emissions, while saving costs. International operators have shown that AI can cut seismic interpretation times by more than 90% and save an average of $38 million per asset per year in unplanned downtime costs. However, the roadmap for achieving these results in Africa's indigenous energy sector is still not well developed. This paper explores CypherCrescent Limited's strategic integration of AI, establishing a technical benchmark for digital transformation across Nigeria and Africa's energy value chain.
We introduce a secure-by-design methodology for embedding AI into the SEPAL Enterprise Planning Solution (EPS), CypherCrescent's economic modeling and financial analytics platform. To address industry concerns about data sovereignty and model reliability, the implementation uses the Model Context Protocol (MCP) as a standard architectural layer. This method allows Large Language Models (LLMs) and proprietary legacy databases to interact in a controlled and governed way without revealing sensitive raw data. By incorporating MCP-compliant SDKs, the system supports physics-informed machine learning and Natural Language Processing (NLP) for contextual querying of long-term organizational datasets, inevitably advancing basic digitization to cognitive autonomy.
Quantitative outcomes from these deployments indicate significant operational enhancements, including decreases in non-productive time (NPT) and optimised operational expenditure (OPEX). These results prove that AI can help improve asset safety and integrity while also dealing with problems with regional infrastructure. The paper also suggests a framework that can be used by other operators in Africa and can be adapted to their needs. It stresses the need to move from reactive legacy systems to predictive, data-driven ecosystems. This study also laid out a phased adoption strategy and strong data protection measures, giving a practical guide for how to meet ESG standards and stay competitive in the energy market in 2035. The findings emphasise that intelligent, autonomous, and secure operations are the "price of admission" for the future of energy.
Justus Uzoma Igwe, Olayemi Samuel Ogundele, Woriayibapri Hearty Alapher et al.· SPE Nigeria Annual Internati...· 0 citations
In terms of day-to-day work, artificial intelligence has not only been an invaluable resource to working professionals, as it has become essential to be able to use such technology to deal with the constantly changing cyber world. However, an unmanaged or inadequately monitored technology-driven workspace can pose a significant risk to stakeholders. There is a significant risk of bias because AI can think as closely as a human. Instances where AI has impersonated a human during a call and obtained funds sent to an account without authorization demonstrate that AI has presented significant risks. One such case occurred at a British energy company in March 2019, in which a telephone call appeared to come from the chief executive of the parent company demanding the immediate transfer of approximately EUR 220,000. There are also cases of photographs of people altered by AI being shared on social media; in December 2025 the Bombay High Court ordered the removal of AI-generated images of the actor Shilpa Shetty. The swift growth of digital marketplaces and data-driven government has elevated the security of personal data to a fundamental constitutional, economic, and human rights issue. The first comprehensive, legally enforceable and AI-specific legislation in the world, the European Union Artificial Intelligence Act (2024), controls AI using a systematic, risk-based approach. It divides AI systems into unacceptable risk, high risk, limited risk and minimal risk categories. It also places stringent compliance requirements on high-risk systems, especially those employed in the healthcare, education, employment and law enforcement sectors. While simultaneously regulating sophisticated general-purpose AI models, the Act places a strong emphasis on responsibility, transparency, human oversight and the defence of fundamental rights. India's Digital Personal Data Protection Act, 2023 is the country's first complete regulatory framework managing digital personal data, adopting a digital-first, risk-based strategy adapted to India's governance circumstances. India nevertheless lacks a dedicated AI-specific statute. Although the Digital Personal Data Protection Act, 2023 addresses data privacy concerns, it does not comprehensively regulate AI systems, risk classification, algorithmic accountability, or systemic harms such as deep fakes and automated decision-making biases. Consequently, India's AI governance remains fragmented and largely policy-driven, revealing a significant regulatory gap when compared to the structured and rights-based framework adopted by the European Union. This study assesses whether India needs a specific AI law by comparing the advantages of the EU's comprehensive legislative approach with India's current legal framework. It concludes by offering suggestions for creating a fair, innovative and rights-protective AI regulatory framework that is in line with India's constitutional ideals, socioeconomic conditions and technical goals.
Nimisha Mishra, T. Rajesh· International Journal of Law...· 0 citations
With the advancement of technology and growing climate crisis, artificial intelligence has emerged as a significant tool for Epredicting change in the climate and natural calamities with precision. AI models, today process and analyse large data sets to provide minute details regarding a slight rise in the sea level, extreme changes in weather and increased carbon emissions that traditional physics-based models fail to recognize. However, the integration of artificial intelligence into climate prediction introduces legal challenges that remain largely unaddressed by international as well as domestic frameworks. The core issue addressed in this paper is the responsibility gap created by the ‘black-box’ nature of AI- based climate predictions. When policy-oriented decisions such as urban zoning, investments in infrastructure and emergency evacuations are based on algorithm that later proves to be biased or inaccurate on the basis of data stored in the model, the problem of accountability arises. Furthermore, the paper examines the friction surrounding data governance and the importance of ‘right to information’ for public climate adaptation. The doctrinal analysis of emerging legislations such as the EU AI Act and the India’s Digital Personal Data Protection Act, 2023 will be done in order to evaluate how precautionary principle of environmental law can be implemented within the artificial intelligence framework. This paper proposes Sustainability by Design framework along with other suggestions. This framework advocates for mandatory transparency in training data, standardizing audit protocols for AI based climate model and a multifaceted liability framework to ensure that AI serves as a reliable instrument for climate justice.
Varalika Nigam, Suryanshi Gupta· International journal of com...· 0 citations
Low-emission zones (LEZs) require cities to define policy rules, predict effects before deployment, and verify outcomes afterwards, yet mobility, emissions, meteorology, exposure, data governance, and public communication are commonly handled in separate systems. This paper presents envair360, a Physical Intelligence architecture and a four-stage, evidence-gated LEZ methodology connecting project definition, baseline feasibility, digital-twin design, deployment, and verified impact closure. A design-science method is combined with an operational case study of Cartagena, Spain, because the research object is both a socio-technical artefact and a context-dependent municipal deployment. The technology chain is selected to bridge complementary scales and functions: SUMO for link- and vehicle-level traffic, WRF and CHIMERE for meteorology and regional chemistry, MUNICH and street-canyon parameterisation for computationally tractable street resolution, model-output calibration anchored to measurements, and FIWARE/NGSI-LD for governed context exchange. The manuscript distinguishes city observations, peer-reviewed component validation, demonstrated platform capabilities, and policy or engineering targets. A Murcia component study reports lower hourly than daily agreement after deep-learning calibration (NO2: r=0.79 hourly and 0.94 daily; O3: r=0.85 hourly and 0.97 daily), illustrating the importance of temporal aggregation and transfer limits. Digitisation of the prior Madrid ozone-density figure indicates modal shifts of approximately +32.0 and +27.8 source-axis units at two stations; the supplied source does not permit a numerical NOx bias estimate. A separate six-city export audit covers 24,384 records and 4064 street segments and demonstrates a common model-output schema, not predictive validation. In Cartagena, project documentation reports elevated PM10/PM2.5, urban heat and solar-radiation stress, and a plausible role for dry-climate dust resuspension, supporting a superblock-oriented LEZ proposal with a long-term 30% vehicular CO2 reduction target. The paper’s specific contribution is the governed orchestration, evidence taxonomy, quality gates, reproducible lineage, explicit policy-scenario representation, and portable city-onboarding protocol; it does not claim that the individual scientific models, the Cartagena deployment, or the cited project targets originated in this manuscript.
Iris Cuevas Martínez, Antonio J. Jara, Jesualdo Tomás Fernández Breis· Sustainability· 0 citations
Artificial intelligence has moved from a specialised technical concern to a central object of international economic and security policy, yet global governance arrangements remain fragmented across competing regulatory models. This article addresses how policymakers can reconcile innovation, competitiveness, human rights, security and sustainability within a coherent governance architecture for artificial intelligence operating across national, regional and multilateral levels. The goals include the review of the governance theory and current practices applicable to AI regulations, analysis of stakeholders' interests and influence, estimation of the possible economic, social, legal, technological and environmental effects of such an arrangement, as well as the design of a feasible multi-level governance system with monitoring and evaluation mechanisms. The article utilises a qualitative comparative policy analysis based on primary legal documents, which include Regulation (EU) 2024/1689, OECD Recommendation on Artificial Intelligence (as amended in 2024), UN Resolution A/RES/79/325 of 2025, and the Global Digital Compact of 2024, in addition to the academic literature on the subject from peer-reviewed sources and books. This paper proposes a framework that takes into account the multi-level and adaptive governance approaches with a particular emphasis on digital sovereignty, thereby creating a three-tier architecture that will include global normative coordination, regional and plurilateral regulatory clusters, and national or sectoral implementation, illustrated by the case study of Kenya, which has created its National Artificial Intelligence Strategy 2025-2030. The main findings in the paper suggest that regulation fragmentation among the European Union, the United States, and China is growing rather than converging, that multilateral instruments do not possess any kind of binding enforcement mechanism, and that low- and middle-income countries like Kenya experience capacity limitations even when developing a proactive national strategy. This paper argues that a layered subsidiarity approach, with specific financing for capacity-building and technical standards that work across the board, is more likely to deliver effective global AI governance than calls for a binding treaty.
Asher Odhiambo Ojuok, Julius Murumba, E. Micheni· East African Journal of Info...· 0 citations
This review paper argues that Vision-Language Models (VLMs), a paradigm whose rapid maturation is evidenced by recent comprehensive surveys offer a transformative solution for rail asset management, and provides a focused overview of the limitations of current CV systems.
Ashley Varghese, Mohammadjavad Ghorbanalivaki, Gunho Sohn· The International Archives o...· 0 citations
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