AI and the Future of Geospatial Tech: Implications for Fit-for-Purpose Land Administration and Tenure Security

Editorial — Artificial Intelligence (AI) is no longer a distant concept for the geospatial sector — it is already shaping the way land data is captured, processed, and applied. The technology has moved beyond experimental prototypes to practical applications, where high-resolution satellite imagery, advanced sensors, and machine learning models are enabling faster, more accurate, and scalable solutions for land administration.

Participants attend a workshop during FOSS4G 2024 in Belém, where the Brazil Data Cube (BDC) team demonstrated cutting-edge tools for satellite image time series analysis and Earth Observation data cubes. As highlighted in Brazil’s new geospatial strategy, BDC powers AI-driven land monitoring that is revolutionising land use planning, cadastral management, and environmental compliance across Brazil and beyond. (Photo: FOSS4G 2024)

Machine learning algorithms now make it possible to automate feature extraction, classify land cover with unprecedented precision, and detect boundary changes over time. These advances reduce the cost and duration of large-scale mapping initiatives, making it feasible to document land rights across entire regions in a fraction of the time once required.

Kees de Zeeuw, Principal Consultant at Esri Global, speaking during the 31 July webinar “Overcoming Resistance to Fit-for-Purpose Land Administration” organised by the FIG Commission 7 FFPLA Working Group, emphasised that AI’s role in “automatic feature extraction, data integration, and interoperability” offers a pathway to modernise Fit-for-Purpose Land Administration (FFPLA) systems.

He stressed that such innovation enables geospatial professionals to design approaches that are not only technologically advanced but also financially and institutionally scalable. For countries grappling with the complexities of extending secure tenure to all citizens, these capabilities are a critical enabler.

AI Meets Fit-for-Purpose Land Administration

The FFPLA model is grounded in three core principles: flexibility, focus on purpose, and incremental improvement. By design, it seeks to adapt land administration systems to the unique contexts in which they operate, prioritising accessibility, affordability, and speed over the rigid and expensive high-precision methods favoured by conventional systems.

AI has the potential to enhance each of these principles in tangible ways. In terms of scalability, AI-powered image recognition and automated classification allow for the rapid surveying of vast territories, covering both formal and informal settlements without the prohibitive costs of traditional field surveys.

When it comes to inclusivity, emerging AI-driven data integration tools can merge and harmonise information from diverse sources — ranging from community-led mapping exercises to non-conventional survey methods — thereby addressing the longstanding gap in national land information systems, particularly in countries where customary tenure is dominant.

In the realm of policy and decision-making, predictive analytics powered by AI can simulate the likely social, economic, and environmental impacts of different tenure interventions. Such modelling can support governments and policymakers in adopting pro-poor land strategies that are not only evidence-based but also responsive to local realities.

However, the technology cannot be viewed in isolation. Professor Eugene Chigbu, who also spoke at the 31 July webinar, pointed out that FFPLA already suffers from policy deficits and institutional resistance. Without deliberate legislative reform and stronger political will, the benefits of AI-enhanced FFPLA may remain confined to isolated pilot projects rather than being embedded within national land governance frameworks.

Resistance, Risks, and Opportunities

Resistance to FFPLA — and by extension to the AI technologies that can support it — emerges from multiple quarters. In many countries, surveyors and lawyers perceive the model as a direct threat to their professional standing and income streams.

Ms. Frances Birungi, Executive Director of the Uganda Community-Based Association for Women and Children’s Welfare (UCOBAC), explained during the FIG Commission 7 session that many land laws remain outdated and rigid, providing no legal recognition for non-conventional surveying methods. This legislative gap means that FFPLA outputs, even when technically sound, may not be legally enforceable.

AI could play a role in overcoming such resistance by providing compelling evidence of its efficiency gains. Demonstrating tangible reductions in cost and time can help shift perceptions among professionals who are wary of reform.

By improving the accuracy and credibility of data generated through pro-poor approaches, AI can also address concerns about quality and reliability. Moreover, AI-generated visualisations and change detection analyses offer clear, accessible evidence that can strengthen advocacy efforts with decision-makers.

Yet, caution is warranted. Dr. Rosalie Kingwill, another speaker at the 31 July event, highlighted that technology alone cannot dismantle entrenched power dynamics in land administration.

In heterogeneous communities, where local power structures and patronage networks determine access to land, AI-enhanced FFPLA risks being co-opted unless paired with governance reforms that address inequities. Without tackling these structural barriers, technological innovation could inadvertently reinforce existing disparities.

The global development community, including the United Nations, has set an ambitious goal: achieving secure land tenure for all by 2030. AI-driven geospatial technology offers a potential breakthrough in making this aspiration achievable. By accelerating the documentation of land rights and reducing the costs of mapping, AI can make nationwide land registration campaigns viable, particularly in low- and middle-income countries.

However, as the FIG Commission 7 webinar discussions underscored, technology is only part of the solution. Legal frameworks must be reformed to recognise the validity of non-conventional, AI-supported survey outputs. Stakeholder engagement — particularly with professional bodies, local governments, and community organisations — remains critical to building acceptance.

Equally important is capacity building, ensuring that local officials, technicians, and community members can operate and maintain AI-enabled systems. Without these parallel investments, AI’s contribution to the tenure security agenda will be limited.

Looking Ahead: Building an AI-Enabled FFPLA Future

The trajectory of AI in geospatial technology suggests its role within FFPLA will expand significantly over the next decade. This shift will likely move AI from the periphery — supporting pilot projects and isolated innovations — to the centre of nationwide land administration systems. For this transition to succeed, four areas will require focused attention.

First, there must be policy alignment. National legal frameworks need to incorporate AI-enabled FFPLA processes, ensuring that outputs have the same legal weight as those generated through conventional methods. Second, innovation in business models is essential. Stakeholders who currently view FFPLA as a threat must see clear pathways to sustain or even grow their professional relevance within AI-enhanced systems.

Third, capacity development at all levels — national, regional, and local — is vital to ensure that technology does not outpace the ability of institutions to implement and maintain it. Finally, ethical safeguards must be built into AI deployments, covering data privacy, security, and equitable access to tools and outputs.

De Zeeuw’s call during the FIG Commission 7 event to continually innovate FFPLA remains critical. The approach cannot be static; it must evolve in step with technological advancements and the shifting needs of the communities it serves. AI presents a powerful opportunity to accelerate tenure security, but its promise will only be realised if it is embedded within inclusive, legally grounded, and politically supported reforms.