Geospatial Artificial Intelligence (GeoAI) represents a transformative force in spatial analysis, combining artificial intelligence with geospatial data to address critical urban and environmental challenges. This study seeks to answer the research question: how can GeoAI contribute to reducing territorial inequalities and promoting more sustainable cities? The research examines GeoAI applications in urban planning, infrastructure monitoring, and environmental risk management, highlighting its potential to revolutionise how spatial data is analysed and used for decision-making.

The theoretical approach is grounded in an interdisciplinary framework, synthesising insights from human geography, computational sciences, and sustainability studies. It draws on concepts of spatial justice and resilience to explore the socio-economic and ecological dimensions of GeoAI’s integration into urban systems. By bridging these fields, the study aims to uncover the transformative capacity of GeoAI in addressing disparities between urban centres and peripheral regions.

Methodologically, the study employs a mixed-method approach. Quantitative analysis leverages advanced machine learning models, including deep learning algorithms for satellite image processing, spatial data visualisation, and predictive modelling. These techniques are applied to big geodata to uncover patterns and dynamics in urban development and environmental change. Complementing this, qualitative methods involve case studies and a critical literature review to explore ethical implications, operational challenges, and governance frameworks for GeoAI adoption. Data sources include open-access geospatial datasets and high-resolution satellite imagery from programs such as Copernicus, ensuring robust and diverse data inputs.

The findings are expected to contribute to the growing body of research on GeoAI’s role in shaping urban futures. The study highlights opportunities for GeoAI to optimise urban planning, enhance disaster preparedness, and address territorial inequalities. However, it also underscores the need to address challenges, such as data biases, accessibility issues, and regulatory gaps. By critically assessing these dimensions, this research seeks to provide actionable insights for policymakers and practitioners aiming to harness GeoAI for sustainable and inclusive urban development.

Land-use/land-cover (LULC) change prediction depends on input variables, but the importance of each variable may vary based on geographical location, time, and the targeted changes in the study area. Most environmental variables are derived from remote sensing data. With advancements in artificial intelligence (AI), particularly machine learning (ML), various change detection models have been developed that effectively assess the importance of input variables based on these criteria. Such weightings are critical for layer embedding in Markov models to predict future changes. In this research, we predict land-use changes over a 30-year period using the Land Change Modeler (LCM) with different transitional models, including Multi-Layer Perceptron (MLP), Decision Forest (DF), and Support Vector Machine (SVM), for the Krkonoše Protected Area in the Czech Republic. We utilized satellite images from the TM and ETM+ sensors of Landsat 5 and 7 and the OLI sensor of Landsat 8 for the years 1990, 2000, 2010, and 2020, respectively. Additionally, the ASTER Digital Elevation Model (DEM) and spatial data related to human and natural factors were incorporated as comprehensive input variables for our transitional ML models. Satellite images were classified in Google Earth Engine into eleven classes. The LCM Markov model predicted land-use changes, comparing our applied ML models. According to the results, from 1990 to 2020, the classes of deciduous, coniferous, and mixed forests increased respectively by +0.74%, +7.11%, and +6.15%. Conversely, transition forests and clearings, areas with sparse vegetation, decreased by -9.81% and -1.20%, respectively. Land-use change predictions for 2020, generated by the applied models, were validated against the actual 2020 land-use map using the overall accuracy method. The models achieved accuracy levels of MLP: 83.38%, SVM: 86.72%, and DF: 89.88%. This study demonstrates the effectiveness of combining machine learning techniques with the LCM Markov model for LULC prediction. The findings emphasize the dynamic nature of forest ecosystems and the need for accurate models to ensure sustainable resource management.

Experts in the geographic domain, like those in many other fields, are exploring the potential of artificial intelligence (AI) for various applications. AI holds the promise of deepening our understanding of spatial phenomena and developing innovative solutions to address pressing challenges in areas such as urban development, environmental management, and disaster response. The primary advantage of AI lies in its ability to analyze large and complex datasets, automate tasks, and generate novel insights.

However, for users to make decisions based on AI-generated insights, they must trust its responses. This is particularly challenging because AI systems, as stochastic models, can occasionally produce hallucinations, exhibit biases, or fail to recognize logical connections, despite adhering to laws and regulations designed for trustworthy AI. Beyond technological constraints, the quality of AI-generated answers depends significantly on the expertise of the user. This includes:

1. The formulation of questions and the depth of prompts, and

2. The interpretation and reflection on AI-generated responses.

Thus, building trust in AI—especially in geo-communication—ultimately depends on the operator's knowledge and the quality of interaction with the system.

The Role of Effective Communication in Building Trust

Trust in any interaction, particularly those involving AI, requires effective communication. According to communication theory, certain key principles—clarity, transparency, relevance, and consistency—are essential for establishing trust. These principles include:

• Clarity: The use of clear, concise, and understandable language, free from jargon and ambiguity.

• Transparency: Users must understand how the AI generates its responses.

• Relevance: AI-generated answers must address the user’s needs effectively.

• Consistency: AI systems should provide consistent responses over time.

By embedding these principles into AI design, such as enhancing stochastic models with logical reasoning, we can create systems that are not only intelligent but also trustworthy and effective communicators.

Geonames as Cornerstones of Trustworthy AI in Geo-Communication

Geographical names (geonames) and their semantics serve as fundamental components of reliable AI and trustworthy geo-communication. Geonames enhance trust in geospatial interactions through their key characteristics:

1. Anchoring: Geonames link information to specific locations, providing concrete reference points and making information more tangible.

2. Familiarity: Known geonames evoke trust by triggering familiarity, experiences, and mental imagery for users.

3. Significance: Proper use of geonames demonstrates cultural sensitivity, builds rapport with audiences, and fosters trust by reflecting cultural and historical importance.

4. Authority: Official, widely recognized geonames lend credibility to geo-communication by embedding precision and authority within the reference framework.

By leveraging these characteristics, geonames facilitate better acceptance of information and foster a deeper understanding of geospatial environments. Strategic use of geonames enhances both the trustworthiness and engagement of geo-communication.

Enhancing Geospatial Trustworthiness with AI

In this contribution, the authors examine geonames and their semantics as key elements for reliable AI and effective geo-communication. They explore selected challenges through practical examples, combining large language models (LLMs) with logical frameworks to demonstrate how AI systems can enhance geospatial trustworthiness. These efforts aim to support the development of reliable AI-based geo-communication that fosters greater understanding and trust in geospatial contexts.

The MOSAIK project introduces an innovative framework to capture and analyze socioeconomic transformations in Vienna at the micro level. By harnessing diverse image data—from Google Street View and Kappazunder to aerial photography—we employ AI-based techniques to automatically extract urban features such as facade structures, roof conditions, and ground floor uses. Neural networks detect building facades and roofs, while supervised machine learning models classify ground-level activities. This enables the comprehensive mapping of diverse built-functional areas across the entire urban space: the usage patterns of ground floor zones, the structural condition of facades, as well as roof conditions and attic expansions. Geostatistical analyses, including hotspot and cluster detection, further delineate areas of urban appreciation and depreciation. By integrating these spatial metrics with socioeconomic indicators like education, household income, and migration background it obtains a comprehensive overview of the socioeconomic transformation in Vienna. Moreover, MOSAIK aims to clarify whether social structures and dynamics can be automatically extracted from image data. This presentation will discuss our methodology, first findings, and the broader implications of street view image analysis for human geography.