Conference Agenda

Urban development in the Greater Toronto Area faces significant challenges because zoning abbreviations and terminology vary widely between municipalities. This provides the background and motivation for the study, as labels such as “R2” in Toronto and “R2 S” in Markham appear similar yet represent different permissions and development standards, creating confusion and slowing planning workflows in a region with growing housing pressures. The problem addressed in this research is the absence of a unified, machine-readable framework that standardizes zoning terminology across municipalities, which limits automated compliance checking, GIS integration, and cross municipal comparison. The objective of this work is to create Zonology, an ontology-based framework that harmonizes zoning abbreviations, permitted land uses, and development standards, beginning with the City of Toronto and the City of Markham. The methodology follows the Linked Open Terms approach, using the Web Ontology Language to encode zoning by laws, land use categories, development standards, and spatial relationships. The model is evaluated through reasoning tasks, competency questions, and semantic alignment tests to ensure clarity, consistency, and interoperability. The results show that Zonology successfully aligns more than sixty zoning categories and over one hundred fifty land use permissions, enabling consistent semantic interpretation and cross municipal queries. The overall significance of this work is that the ontology improves regulatory clarity, strengthens data driven planning, and provides a scalable foundation for harmonized zoning governance across the Greater Toronto Area.

Urban systems are increasingly complex, interconnected, and dynamic, yet most geospatial data models continue to represent them as static geometric layers with limited support for explicit relationships and semantics. This restricts advanced spatial reasoning, network analysis, and AI-driven applications.

This paper introduces GeoGraphJSON, a lightweight semantic data model that extends GeoJSON by integrating spatial geometry with graph-based connectivity. The framework represents spatial entities as nodes and explicitly encodes relationships as typed edges, enabling unified representation of geometry, topology, and semantics within a single interoperable structure. A hierarchical Unique Identifier (UID) system ensures consistent lineage and cross-layer integration across administrative, transportation, and urban asset datasets.

The approach is validated using a large-scale urban dataset from Riyadh, comprising over 10,000 nodes and 13,000 edges. Graph-based analysis demonstrates realistic spatial patterns, including right-skewed degree distribution, strong network connectivity, and identifiable community structures. These results highlight the ability of GeoGraphJSON to capture hierarchical organization and functional relationships while supporting efficient analytical workflows.

By bridging geometry-centric GIS models and graph-based approaches, GeoGraphJSON provides a scalable foundation for urban analytics, digital twins, and GeoAI applications, enabling geospatial systems to evolve from static representations toward intelligent, relationship-aware spatial models.

Urban morphology quantitatively reveals how distinct historical and functional drivers shape city form. This study employs a computational morphometric approach using the Momepy library to analyze and compare the urban structures of Cairo, Egypt, and Makkah, Saudi Arabia. These cities represent paradigmatic cases: Cairo exemplifies long-term, organic layering, while Makkah demonstrates rapid, purpose-driven transformation for religious pilgrimage. We calculated key metrics—including tessellation area, convexity, elongation, equivalent rectangular index, and edge betweenness centrality—for building footprints and street networks sourced from OpenStreetMap. Results show Cairo possesses a heterogeneous, polycentric fabric with complex plot shapes and a distributed street network, reflecting its layered history. Conversely, Makkah exhibits a more monocentric, consolidated form with standardized building geometries and a hierarchical street network channeling movement toward its core. The findings demonstrate that quantitative morphology effectively captures how Cairo's organic evolution and Makkah's centralized planning produce fundamentally different, yet equally revealing, urban spatial structures, offering a replicable framework for cross-city analysis in the region

Urban illumination, as captured through Nighttime Light (NTL) data, serves as a powerful indicator of human activity, infrastructure development, and socioeconomic progress in rapidly growing cities. However, previous studies on Delhi have largely focused on temporal NTL trends without integrating multi-year statistical and spatial analyses to reveal underlying urban and socioeconomic dynamics. This study investigates the spatiotemporal dynamics of urban illumination and development over Delhi using VIIRS Day/Night Band (DNB) NTL data for the years 2015, 2020, and 2025. NTL intensity was used as a proxy for urbanization and socioeconomic activity. Monthly composite datasets for January of each year were processed, clipped to the Delhi administrative boundary, and analyzed using statistical, temporal, and correlation-based methods. The results revealed a slight decline in mean NTL intensity from 26.34 in 2015 to 24.95 in 2025, indicating stabilization in overall light emissions may be due to the adoption of energy-efficient technologies. However, the maximum and range values increased markedly (166.85 to 228.04), signifying intensified illumination in high-activity commercial and infrastructural zones. Temporal change analysis showed balanced positive and negative illumination shifts, with over 50% of pixels exhibiting moderate growth during 2020–2025. Strong Pearson and Spearman correlations (r = 0.83–0.92; ρ = 0.910.95) confirmed the temporal consistency of illumination distribution. The socioeconomic assessment highlighted spatial disparities in light intensity might be corresponding to varying economic activity levels. Overall, the study demonstrates that VIIRS-derived NTL data provide an effective and robust approach for monitoring urban growth, socioeconomic variability, and sustainable lighting transitions in metropolitan environments.

The research investigates artificial intelligence as a device for the automatic interpretation of landscape, reframing representation not as a neutral reproduction but as a cognitive operation in which perception, description, and evaluation converge. Moving from the assumption that landscape is not an objective given but a culturally and perceptually constructed form, the study proposes a fully data-driven methodology based on geolocated images. Through a systematic grid sampling, street-level panoramic views are collected and processed within a multimodal pipeline integrating visual analysis, language models, and multi-agent evaluation. Images are first translated into textual descriptions and semantically clustered, allowing territorial classes to emerge from the data rather than from predefined taxonomies. In parallel, a simulated cognitive framework, structured through four agent profiles, produces evaluative scores and textual judgments, later analysed through sentiment detection. The integration of these layers generates a georeferenced dataset from which a perceptual cartography of the territory is constructed. Applied to the urban context of San Giustino (Italy), the method reveals a continuous gradient from dense urban cores to rural landscapes, while exposing differentiated perceptual readings across observer profiles. Within this framework, artificial intelligence does not replace human interpretation; it operates as an epistemic extension, transforming the landscape into a distributed field of comparable perceptions, where representation becomes a computable form of shared knowledge.

This study focuses on bridging usability information between data providers and data users through standardized metadata. By further integrating standardized metadata with geographic information system operation design, the operations gain automated and awareness capabilities, allowing usability information based on data specifications and quality considerations to be incorporated into relevant processes, thereby avoiding erroneous decisions. The research references international standards such as ISO 19115 and ISO 19157 to meet the requirements of open geographic information technologies.