Conference Agenda

Megacities—defined as Functional Urban Areas (FUAs) of more than 10 million inhabitants—are global hotspots of population growth, economic activity, and environmental pressure. Their development trajectories shape regional and global emission patterns, yet a comprehensive understanding of how urban expansion, air pollution, and economic development interact over time has remained limited. While prior research has examined either urban growth or atmospheric pollution trends, an integrated analysis linking both dimensions within a socio-economic framework is still lacking. This study addresses this gap by leveraging long-term Earth Observation (EO) datasets to systematically analyze settlement growth and tropospheric nitrogen dioxide (NO₂) pollution across 38 megacities between 1996 and 2015. Using the World Bank income classification, we evaluate whether observed environmental and urbanization patterns align with the Environmental Kuznets Curve (EKC)—a hypothesis that posits a non-linear (inverted U-shaped) relationship between environmental degradation and economic development.

Urban green space accessibility represents a critical dimension of sustainable planning and public health outcomes. This research quantifies compliance with the "3-30-300" framework - requiring residents to view three trees from home, neighborhoods to maintain 30% canopy coverage, and proximity to public green space within 300 meters across Montreal Island and Quebec City. While this policy has gained substantial theoretical traction, empirical implementation assessment in Canadian urban contexts remains limited. Employing high-resolution remote sensing imagery, deep learning-based land cover classification, and LiDAR-derived canopy data, we conducted comprehensive spatial analysis of both municipalities. Road network data from OpenStreetMap enabled walkability assessment. Integrated compliance metrics (I330300) revealed stark disparities: Montreal achieved 20.93% compliance, while Quebec City registered merely 2.69%. These findings underscore substantial green space accessibility deficits across both municipalities, with particular concentration in peripheral neighborhoods. Spatial statistical analysis identified pronounced clustering of non-compliance zones, demonstrating heterogeneous distribution of environmental amenities. Population demographic analysis revealed significant correlations between socioeconomic indicators and green space availability, suggesting environmental inequity patterns. Such disparities raise critical equity concerns regarding differential access to environmental services and associated health benefits. These results directly advance United Nations Sustainable Development Goal 11 objectives for establishing inclusive, sustainable cities. The quantitative assessment methodology demonstrates the efficacy of integrating remote sensing, machine learning, and spatial analysis for evidence-based urban environmental policy evaluation. Findings provide empirical foundations for targeted interventions addressing green space deficits in underserved urban communities, enabling data-driven municipal planning strategies that prioritize equitable environmental resource distribution and enhanced public health outcomes.

This presentation examines heat stress and mitigation capacity around transit stops during an extreme heat wave in Vancouver, Canada. Using hyperlocal microclimate modelling and high-resolution urban geometry data, we estimate “feels-like” Mean Radiant Temperature and shade availability to develop two new indicators: the Transit Stop Heat Stress Index and the Transit Stop Mitigation Capacity Index. Results reveal strong spatial and socio-economic disparities, with higher heat exposure and fewer mitigation features in lower-income neighbourhoods. The study demonstrates how microclimate data can guide climate-responsive, equitable transit planning under intensifying heat conditions.

Landfills are among the largest anthropogenic sources of methane, yet accurately detecting and quantifying their emissions remains challenging due to diffuse release patterns, complex terrain, and weather-driven variability. This presentation introduces a drone-based monitoring approach that uses a path-integrated Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor to detect and quantify methane emissions at a municipal landfill in London, Canada. Methane measurements collected throughout the year, together with on-site meteorological observations, were integrated into an inverse atmospheric plume-dispersion model to estimate emission rates. This contribution demonstrates the potential of drone-based TDLAS measurements to provide practical, high-resolution landfill methane monitoring and to reduce uncertainties in greenhouse gas reporting and mitigation efforts.

Climate change and urbanization transform life globally, with direct impacts on each other, yet they are rarely studied together across disciplines. The Synergy Grant urbisphere (https://urbisphere.eu), funded by the European Research Council (ERC), aims to forecast feedbacks between climate and cities. With new synergies between four disciplines (spatial planning, remote sensing, modelling and ground-based observations), urbisphere incorporates city dynamics and human behaviour into climate forecasts/projections, focusing on within-city dynamics of peoples’ activities and how these can be up-scaled to cities globally. urbisphere is studying inter/intra-city form and function (demographics, mobility, climate adaptation and vulnerability planning typologies), exploring human/socio-economic vulnerability, exposure, risk perception, coping/adaptive measures to climatic stressors and settlement/building typologies.

urbisphere is developing new ways to represent city dynamics for weather/climate models. These models are informed by the urbisphere developed Earth Observation system, using space-borne/airborne and ground based sensors with near real-time data transmission, processing and visualization of data from 500+ sensors, including a network of ceilometers, scintillometers, Doppler wind lidars, flux towers combined with street-level and indoor sensors. Combined these measure the 3-dimensional state of the atmosphere and the surface. These observations are providing both new understanding of urban surface-atmosphere processes and datasets for model evaluation at unprecedented detail.

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.

This talk will highlight the future Canadian radar satellite mission, currently named the Terrestrial Snow Mission, under development by Environment and Climate Change Canada, in partnership with the Canadian Space Agency and Natural Resources Canada.

The mission concept will be presented, as well as recent scientific advancements made in the field of snow radar remote sensing, modeling and data assimilation, to continue the advancement of the mission's science readiness level.

This Canadian radar mission will provide weekly coverage of the northern hemisphere with Ku-band SAR data, and, coupled with modeled data, will provide daily snow water equivalent data, to assist hydrological applications and decision-making.

River systems originating from the Southern Alberta Canadian Rocky Mountains provide snowpack meltwater to an extensive downstream reservoir and irrigation network. Future water supplies have the potential to be significantly decreased due to changes in climate and reduced winter snowpack melt regimes. Estimating accurate water volumes in mountain regions is especially challenging. Current practices for estimating snow water equivalent (SWE) over a large mountain region use single point field-based snow measurements generally at valley or sub-alpine elevations. These field measurements are not spatially representative of basin-wide snowpack variability. The Alberta River Forecast Centre uses the Raven hydrological modelling framework to estimate daily winter snow water equivalent (SWE). To address the need for more accurate simulations of spatially explicit SWE, a combined airborne lidar and field snowpack sampling and modelling framework was compared with a Raven Model simulation. “Single point in time” SWE estimates were obtained between 2014 to 2021 using a combination of a) airborne lidar snow depth models, and b) public field sampled snow density. However, annual water yields cannot be generated from this type of snow sampling. The goal of this study was to improve spatialized Raven modelled SWE using the spatially-explicit lidar-based gridded SWE estimates across the West Castle Watershed (WCW, approximately 100 km^2). Results indicated Raven modelled SWE outputs were underestimated in comparison to the lidar-derived SWE with the largest deviation in the sub-alpine forested and grassland areas. Further research aims to use these comparative data to improve Raven-simulated wintertime headwater SWE estimates.

This study evaluates water surface elevation (WSE) retrievals from the Surface Water and Ocean Topography (SWOT) mission and investigates lake dynamics in the karst influenced environment of Bruce Peninsula National Park, Ontario. SWOT derived WSE measurements are validated against high frequency in situ depth logger data referenced to a consistent vertical datum using GNSS. The analysis compares multiple SWOT products, quality filtering approaches, and pixel aggregation methods to determine optimal workflows and assess performance under varying surface conditions, including open water, small surface area (<1km2), vegetation, and ice cover. Results demonstrate that SWOT accuracy is strongly dependent on surface conditions and lake characteristics, with reduced performance in smaller or vegetated systems. The study also examines spatial correlations in lake level variability to identify potential karst influences on hydrological connectivity. These findings provide guidance for the effective use of SWOT in monitoring inland water systems and highlight its potential and limitations for hydrological applications in complex environments.

Comparing airborne lidar datasets collected during snow-free and snow-covered ground conditions enables snow depth mapping at high accuracy and resolution (Hopkinson et al. 2004, Deems et al. 2013). Imputation of snow depth samples combined with field-based or modeled density can produce SWE for small to meso-scale (~100 km2) watersheds (Barnes et al, Submitted, Cartwright et al. 2020, Hopkinson et al. 2012). The goal of this study was to test lidar-based sampling and imputation in an operational regional (>20,000 km2) basin-scale SWE and runoff forecasting framework. Following initial tests in the winter of 2023, two lidar sensors were flown in March (Teledyne Optech Galaxy) and April (Teledyne Optech Titan) 2024 (and again in 2025 and 2026 – results not reported here), to collect 76 snow depth transects (~1 km wide, >2,000 km2) over the Bow and Oldman River Basin headwaters (>400 km north-south, >50 km east-west) near coincident with field samples at 28 sites. For 85 transect intersections, snow depth covariance was high (r2 0.70, RMSE 0.12m), with a small but acceptable bias of -0.04m or -5% (r2 0.94, n 198). An online digital twin platform is being developed to host the snow depth modeling results, as well as real-time weather telemetry and landscape change for public education and data dissemination purposes.

Surface soil moisture (SSM), representing the moisture content within the top layer of soil, provides valuable information and plays an important role in agricultural management. This study presents a deep learning (DL)-based method to estimate field-scale SSM time series over vegetated agricultural areas in Manitoba, Canada, by combining microwave and optical remote sensing (RS) data with auxiliary information. The input dataset was built using Sentinel-1 Synthetic Aperture Radar (SAR) and Harmonized Landsat Sentinel-2 (HLS S30) optical imagery, together with meteorological variables, soil temperature, crop type, topography, and soil texture. Since Sentinel-1 and HLS images were not acquired simultaneously, temporal interpolation was applied to align optical feature values with SAR acquisition times. Features were extracted at 30 m around nine Real-Time In-Situ Soil Monitoring for Agriculture (RISMA) stations.

A one-dimensional convolutional neural network (1D-CNN) was developed to learn local temporal patterns from the multi-source input dataset. The model was trained on multi-year data from 2016 to 2024 and externally validated on 2017 and 2021. On the validation dataset, the model achieved strong accuracy, with R² = 0.815, RMSE = 0.036 m³/m³, and MAE = 0.026 m³/m³. Model interpretation using Shapley additive explanations (SHAP) highlighted a physically coherent set of predictors, including vegetation cover and structure indices, radar backscatter features, solar radiation, minimum air temperature, and precipitation. Overall, the proposed DL framework provides accurate and interpretable field-scale SSM estimates suitable for agricultural monitoring and downstream water-management applications.

Surface water monitoring is critical due to increasing climate impacts, yet small reservoirs (0.01–1 km²) often lack the in-situ infrastructure required for consistent observation. This study evaluates the reliability of the Surface Water and Ocean Topography (SWOT) satellite mission for monitoring such water bodies by integrating UAV-based Digital Elevation Models (DEMs) and traditional gauge station data. A UAV survey was conducted at Recentino Lake (Umbria, Italy) in December 2024 to generate a high-resolution DEM (1.56 cm/pixel) with a vertical accuracy of 3.4 cm. Parallelly, SWOT data were processed by strictly retaining high-quality flags and applying a temporal outlier removal filter based on water level change velocity. The water surface elevation (WSE) derived from the DEM was compared with the processed SWOT data and in-situ gauge records. Results indicated high consistency between the UAV-DEM and SWOT-derived levels (110.78 m and 110.76 m, respectively) after harmonizing height reference frames. Conversely, comparisons with the gauge station revealed significant systematic biases (+18 cm vs. DEM; +44 cm vs. SWOT), attributed to the gauge’s undefined vertical datum. Despite this bias, the SWOT and gauge time series showed a reasonable correlation. These findings demonstrate the applicability of SWOT data for monitoring small reservoirs but underscore the critical challenge of vertical inconsistency across observing systems. Also, the study highlights the urgent need for unified vertical reference frames to ensure the accurate integration of heterogeneous hydrological data from different sources (satellite, aerial, and ground).

This presentation focuses on the challenge of atmospheric correction for high-resolution optical satellites (Sentinel-2) in the presence of adjacency effects, a major source of radiometric bias over small inland water bodies. Because water reflectance is extremely low in the visible and near-infrared, even small contributions of photons scattered from surrounding land surfaces can distort surface reflectance estimates of the observed water body. Traditional physics-based models such as 6SV offer radiative consistency but are limited by assumptions of atmospheric homogeneity and Lambertian surfaces, while empirical and semi-empirical approaches struggle to generalize across diverse atmospheric and geometric conditions.

This project addresses these limitations by developing a Physics-Informed Machine Learning (PIML) pipeline. We emulate heavy 3D Monte Carlo simulations to generate synthetic point-spread function (PSF) datasets. These datasets feed a tabular foundation model (TabPFN), leveraging In-Context Learning to capture the adjacency effect's non-linear dynamics without architectural retraining. We compare TabPFN against classical machine learning (XGBoost) using Sentinel-2 and in situ data. Results demonstrate TabPFN's superiority in resolving complex higher-order scattering, offering a rapid, physically consistent operational pipeline.