The access to analysis-ready data is a milestone for efficient analyses of remote sensing data. However, without efficient data management and data quality processing, trustworthy analyses are difficult to achieve. The preparation, modelling, management and integration of geometric, topological, semantic and temporal data from a broad range of sources are challenging. Furthermore, data quality segregates and depict the borders between what can be accepted, should reject and moreover to advice the reliability of systems, including the remote sensing operations as it forms a substantial fragment of geospatial data source. Quality aspects and challenges of that can include sensor parameters, signal processing, image preparations and enhancement, data extraction, interpretation, classification, and thus thematic applications.

Especially, when using AI driven processes that include “black box” approaches like Deep Neural Networks for processing, the input data quality is one of the crucial parts to correct and bias free learning. Besides false data, actively compromised data or biases inside data sets represent some of the challenges when working, especially, with large data sources.

In this workshop, we welcome contributions from practice as well as from academia. The workshop provides a platform to discuss new ways and best practices to prepare, model and manage multi-dimensional geospatial and remote sensing data for static and (near) real-time applications. The workshop will be part of the ISPRS Geospatial Week 2025, hosted by Mohammed Bin Rashid Space Centre, Dubai, in parallel with a number of related workshops. The event is a one-day single-track workshop of oral presentations and poster sessions in the context of the ISPRS Geospatial Week 2025.