![\"Alan](\"https:\/\/fm-connect1.fortmindz.in\/wp-ogc\/wp-content\/uploads\/2025\/10\/alan-testimonial.png\")
\n\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\tAlan Leidner<\/span>Board of Directors Member, NYC GISMO<\/span><\/cite>\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\n\n\nConsider the catastrophic flooding in Germany in 2021. Despite advanced warning systems, questions emerged about the accuracy and timeliness of the data behind flood prediction models\u2014contributing to delays in evacuations and the loss of over 180 lives. In Australia, during the 2020 bushfires, gaps and inaccuracies in satellite imagery made it harder for emergency responders to plan and deploy resources effectively.<\/p>\n\n\n\n
The stakes are just as high in national security and public information. In 2022, a deepfake video of a prominent political leader went viral, briefly shaking public confidence and prompting urgent questions about authenticity and attribution. During the COVID-19 pandemic, misinformation and inconsistent data reporting sowed confusion and complicated efforts to coordinate an effective public health response. These are not fringe scenarios\u2014they are a preview of what happens when data integrity, provenance, and trust aren\u2019t built into our systems by design.<\/p>\n\n\n\n
That\u2019s why the Open Geospatial Consortium (OGC) is developing a comprehensive framework for Integrity, Provenance, and Trust (IPT)<\/strong> in geospatial data. This work aims to create a foundation that organizations around the world can rely on\u2014and build upon\u2014to ensure their data is not only useful, but trustworthy.<\/p>\n\n\n\n This is the start of a year-long initiative to define what trustworthy geospatial data really looks like\u2014and how we, as a global community, can ensure it underpins the systems we all depend on.<\/p>\n\n\n\n Organizations today rely on an increasingly vast and interconnected data ecosystem\u2014often ingesting data from external providers or processed via black-box AI models. But this convenience comes at a cost: can we be sure that the data is what it claims to be?<\/p>\n\n\n\n We\u2019ve already seen fabricated satellite imagery, AI-generated 3D urban models, and synthetic road networks. These aren\u2019t just theoretical risks\u2014they\u2019re real challenges to public trust, operational accuracy, and accountability.<\/p>\n\n\n\n In high-stakes environments, uncertainty about data integrity or origin can lead to faulty analyses, missed warnings, or even policy failures. It\u2019s time to shift from blind trust to evidence-based trust.<\/p>\n\n\n\n Integrity <\/strong>refers to how data has been handled throughout its lifecycle, including its characteristics such as content, accuracy, and completeness from collection to processing and distribution. Integrity can be compromised by tampering, errors, or undocumented changes, making safeguards essential to ensure reliability.<\/p>\n\n\n\n Provenance<\/strong> refers to the origin of the data, how it has been modified, and who or what performed those modifications. Provenance is compromised when operations that alter the data go unrecorded, affecting both traceability and integrity.<\/p>\n\n\n\n When Integrity and Provenance are well-documented, consistent, and unalterable, there is a foundation for Trust <\/strong>in data. Trust does not simply mean that the user is comfortable that the data meets their expectations, but also that the source, evolution, and suitability of the data can be unambiguously described so that others can also use the data.<\/p>\n\n\n\n For instance, a satellite with known camera parameters captures an image. That image is passed directly to an organization that adjusts it to fit a terrain model using a well-defined algorithm. The adjusted image is then used to calculate the size of a lake knowing that the source has a certain pixel resolution and that the processing included manipulation of the data to retain that original resolution. If each of these transactions\u2014from collection to processing to delivery\u2014is documented using standardized and unambiguous parameters, then users can trust that the data are suitable for their needs and that analyses can be validated and reproduced.<\/p>\n\n\n\n OGC and others have made significant progress in developing Standards<\/a>, tools, and frameworks that enhance IPT in geospatial data. These efforts provide a foundation for ensuring data reliability, traceability, and usability across industries. Some aspects of the IPT framework are already in place or soon to emerge, such as the following.<\/p>\n\n\n\n Metadata Standards define the original and transformed data. There are well-established Standards for recording metadata, but guidance on effective use of common terminology to allow \u201capples-to-apples\u201d comparisons needs to be developed.<\/p>\n\n\n\n OGC\u2019s sensor model <\/a>registry ensures consistent descriptions of sensor capabilities. This registry solves one part of the problem, but similar sets of definitions to describe other sources and capture methods for data need to be developed or integrated.<\/p>\n\n\n\n OGC\u2019s Training Data Markup Language for AI <\/a>standardizes training and validation datasets. New work is being proposed in OGC to develop specialized metadata to describe the AI models used in geospatial data processing.<\/p>\n\n\n\n OGC and ISO\u2019s data quality measures registry establishes quantifiable, consistent descriptions of data quality. These quality measures are applicable to describing the integrity of the data and the impact of processing that makes up the provenance of the information.<\/p>\n\n\n\nWhy It Matters Now<\/strong><\/h2>\n\n\n\n
Let\u2019s start with some definitions and an example to set the perspective of OGC in building this framework.<\/strong><\/h2>\n\n\n\n
BUILDING TRUST IN GEOSPATIAL DATA: PROGRESS AND NEXT STEPS<\/strong><\/h2>\n\n\n\n
Integrity<\/strong><\/h3>\n\n\n\n
Provenance<\/strong><\/h3>\n\n\n\n