Conference Agenda

Accurate and spatially detailed soil information is essential for sustainable land management, agriculture, and environmental monitoring, yet existing soil maps often lack the resolution required to represent fine-scale soil texture patterns. This study investigates a hierarchical modelling framework that integrates high-resolution remote sensing data, including Sentinel-2 imagery and LiDAR-derived terrain attributes, with soil texture predictions from the provincial SIIGSOL dataset. The approach is evaluated across three contrasting regions in Quebec, eastern Canada, selected for their diverse landscape conditions and soil variability. Two modelling strategies were compared: a model based solely on Sentinel-2 and LiDAR predictors, and a hierarchical model that incorporates SIIGSOL covariates to examine their added value. The findings show that integrating multi-source information improves the representation of soil texture patterns and enhances model stability. This work highlights the potential of hierarchical, multi-scale approaches for producing more accurate digital soil maps. Future efforts will extend this modelling framework across the broader landscape to support high-resolution soil mapping for land management applications.

Crop type mapping is essential for agricultural monitoring, food security assessment, and regional management, yet large-scale operational mapping remains challenging. Reliance on a single modality and the absence of explicit spatio-temporal constraints limit existing methods from fully capturing diverse crop-rotation patterns and phenological trajectories over the growing season. To address this limitation, we propose a multi-source, multi-temporal crop mapping framework. Multi-epoch Sentinel-2 and Sentinel-1 observations are preprocessed in Google Earth Engine to produce co-registered optical and SAR time series, including spectral and vegetation indices as well as radar backscatter descriptors. The proposed model couples cross-sensor interaction with seasonal dynamics: an intermodal Mamba fusion mechanism exploits the complementarity between optical vegetation signals and SAR structural information to strengthen parcel boundaries and reduce sensor-specific artefacts, while a temporal Mamba module explicitly models crop development over time, capturing phenological evolution and differences in the diagnostic value of individual observation dates. Decoding the spatiotemporal representation yields the final crop type map. We evaluate our framework for the Federal State of Brandenburg in Germany, where results demonstrate field-aligned, spatially coherent predictions and robust suppression of speckle- and cloud-induced artifacts, validating joint multi-sensor, multi-temporal modeling for operational crop mapping.

Urban green infrastructure plays a crucial role in supporting ecological connectivity, enhancing climate resilience, and promoting human well-being. As cities densify, maintaining functional green networks increasingly depends on understanding the structural continuity of vegetation within complex urban fabrics. Morphological Spatial Pattern Analysis (MSPA) provides a practical framework for quantifying green infrastructure structure; however, its sensitivity to spatial resolution remains insufficiently examined—particularly at metropolitan scales, where high-resolution data are becoming increasingly available. This study examines the impact of spatial resolution on MSPA outputs for mapping and interpreting urban green connectivity in Miami-Dade County, USA. Two scenarios were compared using 10-m canopy data and 2-m high-resolution canopy data processed across 23 tiles. The workflow integrated vegetation preprocessing, MSPA classification, and quantitative and visual comparisons of structural classes to assess scale effects. Results demonstrate that fine-resolution MSPA (2 m) preserves continuous canopy structures and narrow vegetated corridors that the 10-m analysis tends to fragment or omit. High-resolution outputs provide a more realistic representation of neighborhood-scale connectivity, especially in tree-dense areas such as Coral Gables, while also revealing the computational demands of metropolitan-scale MSPA processing. The findings confirm that MSPA results are inherently scale-dependent and that the choice of resolution critically shapes the interpretation of connectivity. This research provides an operational foundation for incorporating high-resolution morphological analyses into urban resilience planning, nature-based solutions, and socio-ecological equity assessments.

Accurate Land Use and Land Cover (LULC) data are vital for effective land planning and management. This study evaluates the U-Net model for LULC mapping using high-spatial-resolution (2 m) imagery from the WPM sensor on the CBERS 04A satellite. The research focuses on the Servidão River Basin in Rio Claro, Brazil, an urban watershed susceptible to flooding. A pseudo-labeling framework is proposed to reduce reliance on manually annotated training data. Training samples were automatically generated by integrating spectral indices (NDVI, NDWI, SOCI, CI, NISI), Principal Component Analysis, and unsupervised Iso-Cluster classification. Several U-Net configurations were evaluated, with a ResNet-34 backbone with class weighting achieving the highest performance. The model was then retrained using a manually refined reference dataset to enhance the representation of spectrally complex classes. Accuracy assessment resulted in an Overall Accuracy of 0.93, average Precision and Recall of 0.92, and a mean Intersection over Union (IoU) of 0.86. These findings indicate that the proposed pseudo-labeling strategy, combined with a U-Net, offers a robust approach for LULC mapping in complex urban environments using freely available CBERS 04A imagery.

A first-order branch modelling method based on bidirectional searching was proposed, the key steps included skeletonization using local separators, trunk extraction based on path straightness and first-order branch extraction using bidirectional searching. The method was tested on ForestSemantic dataset, and results showed that the extraction precision was 80.29%, and RMSE of the pitch angle estimation was 9.74°, indicating that the method can effectively recover the topological structure of branches.

Long-term and high-resolution terrestrial water storage (TWS) monitoring is critical for water-resource management, climate adaptation, and understanding hydroclimatic variability. Satellite gravimetry missions such as GRACE and GRACE-FO provide unprecedented observations of TWS but are limited by coarse spatial resolution, short observational records, and temporal gaps. This study presents an integrated deep-learning framework for reconstructing and downscaling GRACE/GRACE-FO data to produce century-scale, high-resolution TWS datasets. We apply RecNet and an enhanced RecNet (ERecNet) to reconstruct historical TWS anomalies in the Sudd Wetland, Lake Victoria Basin, and Nile River Basin, leveraging climate variables and lake-level observations. To overcome spatial limitations, we develop DownGAN, a novel generative adversarial network with a high-to-high downscaling strategy, producing fine-scale TWS patterns while maintaining mass consistency. The fusion of reconstruction and downscaling enables detailed, long-term monitoring of wetland dynamics, droughts, and hydroclimatic variability. Reconstructed datasets reveal multi-decadal wetting/drying phases and strong links between TWS fluctuations and climate teleconnections such as ENSO and the Indian Ocean Dipole. This framework advances the application of GRACE/GRACE-FO for climate resilience, ecosystem monitoring, and water-resource management in data-scarce regions, demonstrating the potential of deep learning to extend satellite-based hydrological observations both spatially and temporally.