Conference Agenda

With the rapid advancement of satellite, aerial, and UAV platforms, the daily volume of remote sensing data collected over megacities has grown exponentially. However, only a limited portion of this data can be transformed into usable products in time. Current production workflows remain lengthy and poorly automated, which fails to meet the increasing demand for high-precision and high-timeliness remote sensing products in city management, environmental monitoring, and emergency response.

To address this gap, this study proposes the construction of an standardized, efficient and reusable foundational database system consisting of three key components: image control point database, interpretation sample database, and spectral information database.

The image control point database establishes a unified geometric reference for multi-source data; The interpretation sample database provides large-scale, high-quality labeled data for deep learning-based image analysis; and the spectral database offers standardized spectral features for accurate classification and parameter inversion.

Together, the three databases form a collaborative mechanism that links geometric accuracy, semantic understanding, and spectral consistency, thereby building a complete chain from analysis-ready data (ARD) production to rapid information extraction. Using Shanghai as a case study, this paper presents the design, construction, and collaborate applications of the three databases, demonstrating their effectiveness in supporting refined and sustainable megacity governance.

Disaster Risk Management benefits from innovative techniques including AI and Multi Sensor Fusion. The Fireguard Approach uses such technologies to improve the Wildfire Management works in Saxony, Eastern Germany by supporting standing efforts in Early Warning, Disaster Response and Monitoring. Unmanned Aerial Systems (UAS) play a vital role in providing real-time information via a 5G network to a central information management system that delivers geospatial information to response teams. This study highlights the potential of combining UAS, AI, geospatial solutions and existing data for real-time wildfire monitoring and risk assessment systems. The preliminary study successfully shows the potential of the provided solution to enhance Wildfire early detection, response and monitoring to address immediate and long-term needs of response teams.

This paper presents a multi-agent geospatial application that enables users to interact with spatial data through natural language, called MapEcho Copilot. The system integrates large language model (LLM) reasoning, semantic embedding search, and spatial analytics within a unified architecture. A vector embedding database is constructed to index diverse open-source geospatial datasets. Upon receiving a user query such as “show all tennis courts in downtown of Toronto” or “find habitats of grizzly bears in Canada” the system performs semantic retrieval to identify relevant datasets, followed by geospatial filtering and reasoning through specialized agents via an interactive and friendly interface. The multi-agent framework coordinates between semantic understanding, data retrieval, and spatial computation layers to deliver map-based responses in real time. The Results demonstrate the system’s ability to process both semantic and geospatial queries with high accuracy and interpretability, providing an intuitive bridge between natural language and spatial intelligence.

Point clouds have proven over the years to be a suitable spatial representation of scenes and objects at varying scales and levels of complexity, making them widely used across several scientific domains and applications. Advancements in sensor technology, computer vision, and data science have produced high‑resolution point clouds and advanced analytical approaches, leading to broader adoption for spatial information extraction to support decision making. However, traditional point cloud management systems for organizing and distributing data throughout the point cloud lifecycle often create significant duplication at each stage. This causes data fragmentation as multiple copies and versions are scattered across different processing steps, workgroups, and storage locations, further limiting cross‑domain applications.

In this paper, we propose a unified point cloud data management (PCDM) approach that supports the principles of findability, accessibility, interoperability, and reusability (FAIR) across domains at scale. The proposed approach aims to support diverse point cloud retrieval for cross-domain analysis by leveraging a single, reusable PCDM system built on a shared data model. Our approach improves on existing frameworks and provides a foundation for point cloud data management and data spaces.

This contribution presents a rigorous mathematical and computational examination of confidence ellipses applied to bivariate spatial distributions, with a specific focus on their implementation in the open-source LFTools plugin for QGIS. Confidence ellipses are widely used in geography, environmental sciences, public health, criminology, and spatial statistics to summarize central tendency, dispersion, and directional trends of point-based datasets. Although conceptually well established, their practical reliability depends on correct numerical implementation and statistical consistency—an aspect rarely evaluated in detail.

The study first revisits the formal mathematical foundations of confidence ellipses, including covariance-matrix geometry, eigen-decomposition, and Chi-Square-based scaling for different confidence levels. It then analyses the computational workflow adopted in LFTools and validates its correctness using 100,000 simulated Gaussian random points, demonstrating near-perfect adherence (<0.05% deviation) to theoretical confidence intervals.

To assess performance on real-world data, the method is applied to the Groundwater Well Database for Brazil (GWDBrazil), comprising more than 350,000 groundwater wells. Confidence ellipses at the national and regional levels reveal strong anisotropy, clustered patterns, and non-Gaussian spatial structures, confirming both the robustness of the tool and the complexity inherent to real geospatial phenomena.

Results indicate that the LFTools implementation is mathematically sound, statistically reliable, and suitable for scientific applications. The study highlights the relevance of reproducible open-source tools and outlines future directions involving spatial–temporal extensions, non-parametric approaches, and multi-scale territorial analysis, applicable in Brazil and worldwide.

Through theoretical analysis and empirical research, this study thoroughly examines the theoretical foundations and practical applications of zero-defect sampling inspection schemes, revealing significant differences between inspecting large lots as a whole versus splitting them into sub-lots in terms of consumer's risk control. The findings indicate that although the zero-defect sampling scheme (Ac=0) adopted in the GB/T 24356-2023 standard shifts quality control from "post-production spot checks" toward "in-process prevention", it exhibits notable deficiencies in controlling consumer's risk, resulting in an unacceptably high level of risk for consumers. Empirical analysis demonstrates that, for large lots with relatively poor quality, e.g., when the product's defect rate is 10%, the inspection plan (100, 10, 0) still carries a 33.3% probability of erroneously accepting the lot, which significantly exceeds the risk level typically acceptable to consumers and thus imposes excessive quality risk on them. Furthermore, the study reveals that inspecting small lots or subdividing large lots benefits producers, highlighting an imbalance in the current standard's risk allocation mechanism. These insights provide more reliable theoretical support and practical guidance for quality management of surveying and mapping products.