Conference Agenda

This invited talk showcases two linked advances in Canadian urban remote sensing from the University of Waterloo. The first work presents large-scale 3D urban scene reconstruction and point-cloud densification using Gaussian Splatting with Google Earth Studio imagery. It recovers geometry and photorealistic radiance for the Kitchener–Waterloo region, benchmarking against NeRF baselines and achieving higher view-synthesis quality with faster training. The study demonstrates practical pipelines for city-scale digital twins and urban analytics.

The second study advances building-level reconstruction through the Gaussian Building Mesh (GBM) framework. GBM automatically extracts metrically accurate 3D building meshes from open-access imagery using segmentation models such as SAM2 and GroundingDINO, combined with Gaussian Splatting for dense, photorealistic surface generation. This pipeline enables efficient, data-driven modeling of urban structures, supporting applications from municipal infrastructure documentation to heritage reconstruction.

Together these contributions deliver scalable 3D reconstruction, object-level meshing, and data-driven urban modeling. They strengthen Canada’s leadership in remote sensing research and support resilient urban planning, infrastructure monitoring, and Earth observation–driven decision systems for Canadian cities.

Fine-resolution urban land-cover (ULC) mapping is essential for understanding intra-urban heterogeneity and monitoring rapid land-use change. However, large-area mosaics from CubeSat constellations such as PlanetScope often suffer from strong radiometric inconsistencies caused by varying sensor calibration, viewing geometry, and illumination, leading to unreliable classification and visual artifacts. This study introduces a Semantic-Aware Harmonization Model (SAHM) that jointly addresses spectral and semantic inconsistencies across multi-source imagery. SAHM integrates two synergistic components: a Spectral Harmonization Module (SHM) for radiometric alignment between PlanetScope and Sentinel-2 imagery, and a Semantic Consistency Module (SCM) inspired by prompt-based architectures to enforce category-level coherence. Through bidirectional interaction, semantic features guide spectral correction, while harmonized representations improve segmentation reliability. Applied to the Toronto and Region Conservation Authority area (TRCA), SAHM achieved an overall accuracy of 91.9%, with F1-scores exceeding 94% for impervious surfaces and 97% for agriculture. Harmonized PlanetScope mosaics demonstrated high spectral fidelity (PSNR = 34.2 dB, SSIM = 0.93) and reduced inter-scene NDVI/NDWI bias (< 0.05). The results highlight SAHM’s capability to produce spatially coherent, semantically reliable urban maps from radiometrically inconsistent high-resolution imagery. This framework offers a scalable solution for consistent urban monitoring across CubeSat constellations, paving the way toward semantic-driven harmonization in next-generation Earth observation.

Urban trees enhance the urban environment through various ecosystem services. Individual tree delineation and species classification provide information on the location, structure, and species of each tree from remote sensing datasets, offering valuable data support for efficient and refined urban greening management. However, existing individual tree delineation algorithms developed based on single-source remote sensing datasets struggle to address the complexity of urban green landscapes, such as conifer-broadleaf mixtures, tree-shrub complexes. Additionally, the relationship between classification accuracy and individual tree delineation quality remains unclear.

This study integrates the Segment Anything Model (SAM) and Marker-Controlled Watershed Segmentation (MCWS), combining imagery and LiDAR features, to optimize individual tree delineation in complex urban landscapes. Species classification was then performed on crown datasets from different algorithms to investigate how classification accuracy responds to varying crown qualities.

The results demonstrate that the proposed SAM-WS algorithm effectively enhances individual tree delineation accuracy, achieving the highest F1-Score of 0.75, with improvements of 0.20 and 0.27 over SAM and MCWS, respectively. The classification accuracy based on SAM-WS crowns was the highest among all algorithm-derived crown datasets, with an Overall Accuracy (OA) of 0.79 and a Kappa of 0.64. As the average F1-Score of crown delineation dropped from 1.00 to 0.48, the OA for classification decreased from 0.86 to 0.74, and Kappa from 0.77 to 0.38. Additionally, the classification accuracy of conifers and shrubs was more sensitive to the crown quality. This research offers new methodologies and insights into the application of remote sensing-based urban vegetation monitoring.

This study develops a satellite-based framework for detecting invasive shrub presence, focusing on common buckthorn (Rhamnus cathartica), across urban woodland environments in southern Ontario. Invasive shrubs exhibit extended leaf phenology compared to native understory species, leafing out earlier in spring and retaining foliage later into fall. Leveraging this phenological contrast, the workflow integrates multi-season Sentinel-2 MSI composites with higher-resolution PlanetScope imagery, combined with 2025 field observations collected across mixed-canopy woodlands in the Greater Toronto Area.

Spectral features (NDVI, EVI, NDWI, red-edge indices, Tasseled Cap transformations) and contextual variables (distance to woodland edges, canopy-openness metrics) are incorporated into a Random Forest classifier designed to distinguish buckthorn presence under complex understory conditions. A presence-background training strategy and spatially blocked cross-validation are implemented to reduce label uncertainty and spatial autocorrelation.

Preliminary results show that early-spring and late-fall imagery substantially improve detection sensitivity, with late-season spectral indices supporting the hypothesis that extended leaf persistence is a reliable cue for invasive shrub identification. This cost-effective workflow highlights the potential of multi-sensor satellite data to support early warning, invasion-risk mapping, and more efficient allocation of ground-validation efforts in urban conservation planning.

Drone remote sensing offers unique potential for capturing fine-scale variations in land surface temperature and vegetation condition, two tightly coupled variables that jointly regulate surface energy balance, evapotranspiration, and local microclimates. Understanding their interactions is crucial for assessing ecosystem function, evaluating the impacts of land use, and informing nature-based climate adaptation strategies. Yet, despite growing interest, UAV-based thermal and multispectral data have largely been used individually, and their integration for quantifying coupled seasonal dynamics in vegetation function and surface temperature remains limited. To address this gap, this study introduces a commercial off-the-shelf dual-drone multisensory data collection framework. The system integrates thermal infrared and multispectral imaging to analyze seasonal variations in surface temperature and vegetation health. The study area is a suburban-naturalized mixed landscape located at the University of Toronto Mississauga, Canada. Ten monthly drone flights were conducted from August 2024 to August 2025, with thermal and Normalized Difference Red Edge (NDRE) indices mosaiced for analysis. Results revealed distinct seasonal patterns, with impervious surfaces consistently exhibiting the highest surface temperatures, followed by vegetation and water, which were the coolest. NDRE values exhibited summer maxima and winter minima, aligning with the expected phenological cycles of vegetation. Regression analyses indicated that higher NDRE generally corresponded to lower surface temperatures, particularly for maintained trees and evergreen vegetation, highlighting the role of vegetation in moderating local heat. The developed workflow demonstrates the potential of drone-based remote sensing for cost-effective, fine-scale, multi-temporal environmental monitoring. It provides an adaptable framework for future applications in microclimate assessments.