Conference Agenda

We present an approach to ensure trustworthy, high-quality GeoAI which requires a coordinated effort across academia, industry, government, and standards bodies. The ISPRS community, in partnership with organizations such as ISO, OGC, the World Geospatial Industry Council (WGIC), the International Society for Digital Earth (ISDE), and other international initiatives is uniquely positioned to host this dialog. By aligning emerging GeoAI practices with established data-quality standards and ethical-AI frameworks, the community can help shape a future-proof foundation for responsible innovation in geospatial intelligence.

This paper presents research on integrating Data Quality and Licensing metadata into Open Science workflows using ISO and OGC standards and machine-readable profiles to enhance interoperability, transparency, and reusability of scientific data. All if this is possible via ongoing development of modular building blocks, validation frameworks, and engagement with standards bodies to support FAIR principles and scalable data reuse across domains. Our approach demonstrates the possible integration of concept schemes and measures defined in the ISO 19157 multipart standard.

This paper presents the ISO 19157-3 Data quality measures register, discusses its design and implementation and illustrates its the utility to the ISPRS and wider geocommunity. In this paper we highlight the importance of providing geographic metadata about quality, the evolution of international standards to support this, and a novel implementation of a human readable and machine-actionable web register for geographic data quality measures.

The rapid growth of geospatial data, fueled by advancements in satellite imagery, IoT sensors, and mobile services, presents significant opportunities in sectors like urban planning and environmental monitoring. However, these data are also vulnerable to cyber threats, emphasizing the need for strong protection mechanisms. This paper introduces a modular, hybrid architecture that addresses security challenges by integrating post-quantum cryptography, decentralized storage, and access control via Blockchain. It employs AES-GCM for the secure encryption of large datasets and Kyber for enhanced key protection against quantum threats. Encrypted data is stored securely in the Interplanetary File System (IPFS), with access managed by smart contracts on a private Ethereum blockchain. The architecture utilizes FastAPI for back-end processes, microservices for cryptographic services, and React for the user interface. Performance assessments show good scalability and resilience, paving the way for secure geospatial data sharing while harmonizing data sovereignty, quantum security, and decentralized management.

High-Resolution Global Land Cover (HRLC) products are essential for monitoring Earth’s surface dynamics and supporting policy frameworks like the Sustainable Development Goals. Recent global products such as ESA WorldCover, ESRI LULC, FROM-GLC, and Dynamic World offer 10–30 m resolution maps, but their interoperability remains limited due to differences in input data, class legends, and validation protocols. This lack of harmonization hampers cross-comparison and integrated use for environmental monitoring. Although advances in remote sensing, AI, and cloud computing have enabled more frequent and detailed mapping, they have also introduced new challenges for ensuring data consistency and comparability.

Validation of HRLC products is hindered by the absence of a common benchmark dataset, as current accuracy metrics are derived from heterogeneous reference samples and class definitions. Traditional validation methods are costly and time consuming, while temporal inconsistencies and cloud contamination further increase uncertainty. ISO 19157-3 offers a standardized framework to describe and automate quality measures such as positional accuracy and thematic correctness, supporting transparent and reproducible evaluation across datasets.

A sustainable solution involves establishing an international benchmarking framework with standardized reference data, legends, and sampling strategies. As a practical interim approach, the Map of Land Cover Agreement (MOLCA) combines multiple HRLC products to identify spatial consensus and disagreement, offering a proxy for thematic reliability. Although MOLCA measures consistency rather than absolute accuracy, its integration into ISO 19157-3 would advance data quality assessment, fostering transparency, interoperability, and confidence in HRLC-derived environmental analyses.