Artificial intelligence (AI) has been increasingly dominating headlines. Most of this attention, however, is bestowed on large language models (LLMs) such as ChatGPT. Much has been written about issues such as hallucinations, accountability, and data provenance in these systems. While these concerns are real and merit careful consideration, they risk overshadowing another field of AI that is quietly reshaping development practice: geospatial artificial intelligence, or GeoAI.

GeoAI applies AI techniques—including machine learning, deep learning, and computer vision—to spatial and Earth observation data. These systems can detect patterns, classify features, and generate predictions from satellite imagery, aerial photography, and other georeferenced datasets. For evaluators, GeoAI offers unique advantages: it enables large-scale coverage across countries or regions, frequent monitoring for near–real-time observation of environmental or urban changes, and insights into areas where conventional data collection is costly, infeasible, or unsafe. At the same time, it raises ethical and transparency challenges that cannot be addressed with the same tools developed for text-focused AI systems.

In this post, we explore why GeoAI deserves its own approach to ethics and transparency, drawing on real-world examples from IEG evaluations to highlight both its promise and its pitfalls.

How IEG Uses GeoAI in its Evaluations

The World Bank Group’s Independent Evaluation Group (IEG) has been experimenting with GeoAI across a variety of evaluation contexts, using it to generate granular insights that were previously difficult—or even impossible—to obtain.

One way IEG uses GeoAI is by incorporating large-scale, model-generated datasets as analytical inputs. In the Biodiversity for a Livable Planet evaluation, for example, Dynamic World land cover data provided global insights across terrestrial and hybrid protected areas, enabling consistent, high-frequency monitoring of land cover changes across diverse ecological zones. Similarly, in the Tanzania Country Program Evaluation, Microsoft/Bing’s global building footprint dataset was used to analyze settlement morphology and patterns of built-up expansion, producing interpretable spatial indicators of urbanization.

Beyond using pre-generated datasets, IEG has also applied computer vision models directly to raw satellite imagery and digital streetscape images to derive bespoke indicators tailored to specific evaluation questions. In the Tirana Learning Engagement, for instance, semantic segmentation models were used to measure urban greenness and sky-view, allowing for neighborhood-scale analysis of environmental quality and urban form.

These applications illustrate how GeoAI is becoming increasingly embedded in evaluation workflows, contributing spatial evidence that directly shape evaluation findings.

Ethical and Practical Challenges in GeoAI

GeoAI opens up exciting possibilities for evaluation and development research, but it also brings risks that are unique to spatial AI. These models can fail in subtle, often silent ways that are difficult to detect, and when they do, the consequences can affect fairness, reliability, and the interpretation of results.

A major challenge in GeoAI lies in the quality of the training data and how well it represents different contexts. GeoAI systems are highly sensitive to the geographic, temporal, and socioeconomic characteristics of their training data. Models trained in well-documented urban areas, for instance, often struggle when applied to rural, informal, or ecologically distinct regions. Even datasets that appear globally consistent can mask large variations in reliability, and misalignments between algorithmic labels and real-world concepts can make seemingly straightforward results misleading.

Another key challenge is transparency. Many deep learning models used in GeoAI operate like black boxes, producing predictions through complex interactions that are difficult to trace. This makes it hard for evaluators and researchers to understand why a model succeeded—or failed—or to clearly explain results to policymakers and stakeholders. While interpretability tools can provide insights into these processes, they rarely offer a complete picture and do not resolve the fundamental challenges posed by model complexity and scale.

Bias and representation are also important considerations. Uneven geographic and socioeconomic representation in training data leads GeoAI systems to systematically underrepresent informal settlements, rural communities, and marginalized populations. These distortions risk reinforcing existing data gaps and shaping development narratives around what is most visible rather than most vulnerable.

Emerging challenges around data integrity and synthetic imagery add another layer of complexity. Advances in generative modeling now make it possible to create manipulated or entirely synthetic imagery, such as satellite images, that can conceal or simulate changes on the ground. As these synthetic geospatial datasets become more accessible, distinguishing between authentic and synthetic images becomes a growing challenge for reproducible spatial analysis.

Finally, the infrastructure needed to run GeoAI models raises both practical and ethical questions in terms of environmental sustainability and equitable access. GeoAI systems require substantial computational infrastructure, high-resolution imagery, and specialized expertise, creating barriers to participation and scrutiny. Energy-intensive training and deployment further raise sustainability concerns. These structural constraints concentrate analytic capacity in a small number of institutions, limiting transparency, replication, and broader engagement with GeoAI-based evidence.

Toward Responsible GeoAI

Addressing these challenges requires a thoughtful approach to transparency and ethical practice that is tailored specifically to GeoAI. This means carefully documenting datasets and methods, testing models across different geographic and social contexts, validating results in a disaggregated way, reporting on interpretability, and ensuring the provenance and reproducibility of geospatial data. Integrating these practices into GeoAI workflows isn’t just about methodological rigor—it’s essential for maintaining trust in AI-enabled evidence and making sure the insights drawn from geospatial methodologies are reliable and equitable.

GeoAI is more than a technical tool. It is reshaping how evaluators and development researchers understand environmental and social challenges. As GeoAI becomes increasingly embedded in evaluation, it is essential that transparency, fairness, and interpretability are central to the design and deployment of these technologies. They cannot be afterthoughts. By taking these concerns seriously, we can harness the power of GeoAI while minimizing risks, producing spatial insights that are both impactful and trustworthy.

In addition to developing GeoAI tools, IEG has advocated for more accurate and comprehensive project location data – a critical precondition for applying geospatial methods effectively. Learn more by reading our recent working paper published with other World Bank Group colleagues.