Faramarz Famil Samavati

Discrete global grid systems (DGGSs) organize geographic data as indexed cells whose neighborhoods support spatial queries. We study exact halo construction on a triangular DGGS and its use in buffer coverage. Rather than store refined connectivity, the architecture reconstructs adjacency using compact integer triangular indices and an Atlas of Connectivity Maps. A graphics processing unit (GPU) resident hash frontier admits each new cell once. Probe reduction and reusable output staging reduce repeated work and delivery costs. Measurements through level 28 compare serial and twenty thread central processing unit (CPU) baselines using flat hash tables, on an Intel Core i7 12700K and NVIDIA RTX 4070. The largest halo contains 10.8 million cells and reaches page locked (pinned) host memory in 13.3 ms; its returned container copy is measured separately. Small queries favor CPU execution. Independent mesh references validate Pentakis adjacency, while the performance runs check cumulative output agreement. A stopping argument connects complete rings to conservative distance coverage, conditional on valid source and distance bounds. A separate World Geodetic System 1984 boundary buffer study includes source covering and distance filtering; CPU execution is faster in all seven cases. Numerical tests do not certify floating point distance bounds.

By partitioning agricultural fields into units with similar yield-limiting factors, Management Zone (MZ) delineation provides the spatial basis for variable-rate application of inputs such as nitrogen, seed, and irrigation. To evaluate the operational implementation of MZ methodologies, this paper analyzes 137 peer-reviewed papers published between 2000 and 2025, extracting data on agronomic contexts, sensing inputs, computational workflows, and validation strategies. Our analysis reveals a clear methodological shift: while early studies relied heavily on data such as soil properties, recent literature is dominated by multisource data fusion that combines static soil proxies (e.g., apparent electrical conductivity) with dynamic remote sensing vegetation indices. Methodologically, the literature relies heavily on similarity-based clustering, specifically fuzzy c-means and k-means, often applied to raw spatial grids or Principal Component Analysis (PCA) transformations. Although machine learning and optimization-based approaches have increased in recent years, rigorous agronomic and economic validation remains limited, while internal cluster validity indices (e.g., FPI, NCE) and inferential statistical tests (e.g., ANOVA) are widely used to assess delineated zones, only 13 of the reviewed papers explicitly evaluated the economic or environmental net returns of the delineated zones. To transition MZ delineation from a classification problem to an operational decision-support tool, the current literature suggests a need to shift validation efforts away from internal clustering metrics toward multi-year yield stability assessments and direct economic cost–benefit analyses.

Surface‐based pattern simulations have proven valuable for texture design and scientific visualization, but existing methods face several limitations. Most simulations either target a narrow range of pattern types (e.g. spots, branching) or support a broad range of patterns at the cost of time‐consuming parameter tuning. In either case, local and global control over the character of patterns is desirable, but often not supported. Additionally, transferring 2D simulations to 3D surfaces can introduce distortions, and is sensitive to mesh topology and quality. Finally, colourization further complicates the use of simulations for texturing, often relying on ad hoc mapping of simulation values to colours. To address these challenges, we introduce a unified framework for generating expressive, controllable patterns that are naturally embedded on curved surfaces. We reformulate Physarum polycephalum slime mold simulations in terms of continuous rates and PDEs, allowing greater consistency across varying space and time discretizations. We introduce agent‐based stochastic chemical kinetics to regulate agent turnover, which permits direct control over the uniformity of final patterns. Together, these modifications enable fine‐grained control of pattern synthesis using spatially varying parameter maps, directional biases, stimuli, and agent sinks/sources. We demonstrate that our approach allows for the generation of new pattern classes in Physarum slime mold simulations, including stripes, branching, and hierarchical structures. To eliminate distortion and artifacts, we re‐purpose intrinsic triangulations proposed for geometry processing to dynamic simulations. Finally, we introduce a simple colourization method to transfer colours from an exemplar image to simulation results. Notably, while demonstrated through slime mold simulations, our framework generalizes to other patterning models (e.g. reaction‐diffusion), thus providing a versatile tool for complex, controllable surface‐based pattern synthesis.

Digital resources associated with socially significant public buildings and heritage sites range from basic information (e.g., names, functions, room numbers) to extensive archives, including images, oral histories, and videos—typically accessed through websites, mobile apps, or AR/VR platforms. However, these screen-based interfaces lack the tactile and spatial benefits of physical models. Physicalization supports embodied engagement, but physical models alone cannot convey the depth of digital records. This research explores hybrid physical– digital interfaces that link tangible models with digital resources to foster deeper engagement with physical settings. Our prototype integrates 3D printing, conductive paint, and touch sensors with a web service that links hotspots on a model to associated geometry, narration, and video displayed on large screens or mobile devices.We deployed the system in an exhibition at the University of Calgary and conducted a user study. Empirical findings indicate that participants considered the design easy to use and engaging, and that hybrid interaction enhanced both access to digital information and perceived understanding of each location’s history.

Understanding spatial variability is central to precision agriculture, yet terrain features are often overlooked in remote sensing workflows that inform agronomic decision-making. This work introduces a terrain-aware visual analytics approach that integrates landform classification with crop performance analysis to better support field-level decisions. Terrain features are an important contributor to yield variability, alongside environmental conditions, soil properties, and management practices. However, they are rarely integrated systematically into performance analysis and decision-making workflows—limiting the potential for terrain-aware insights in precision agriculture. Addressing this gap requires approaches that incorporate terrain attributes and landform classifications into agricultural performance analysis and management zone (MZ) delineation—ideally through visual analytics that offer interpretable insights beyond the constraints of purely data-driven methods. We introduce an interactive focus+context visualization tool that integrates multiple data layers—including terrain features, vegetation index–based performance metric, and management zones—into a unified, expressive view. The system leverages freely available remote sensing imagery and terrain data derived from Digital Elevation Models (DEMs) to evaluate crop performance and landform characteristics in support of agronomic analysis. The tool was applied to eleven agricultural fields across the Canadian Prairies under diverse environmental conditions. Fields were segmented into depressions, hilltops, and baseline areas, and crop performance was evaluated across these landform groups using the system’s interactive visualization and analytics. Depressions and hilltops consistently showed lower mean performance and higher variability (measured by coefficient of variation) compared to baseline regions, which covered an average of 82% of each field. We also subdivided baseline areas using slope and the Sediment Transport Index (STI) to investigate soil erosion effects, but field-level patterns were inconsistent and no systematic differences emerged across all sites. Expert evaluation confirmed the tool’s usability and its value for field-level decision support. Overall, the method enhances terrain-aware interpretation of remotely sensed data and contributes meaningfully to refining management zone delineation in precision agriculture.

One widely used method for analyzing vegetation growth from satellite imagery is the Normalized Difference Vegetation Index (NDVI), a key metric for assessing vegetation dynamics. NDVI varies not only spatially but also temporally, which is essential for analyzing vegetation health and growth patterns over time. High-resolution, cloud-free satellite images, particularly from publicly available sources like Sentinel, are ideal for this analysis. However, such images are not always available due to cloud and shadow contamination. To address this limitation, we propose a model that integrates both the temporal and spatial aspects of the data to approximate the missing or contaminated regions. In this method, we separately approximate NDVI using spatial and temporal components of the time-varying satellite data. Spatial approximation near the boundary of the missing data is expected to be more accurate, while temporal approximation becomes more reliable for regions further from the boundary. Therefore, we propose a model that leverages the distance transform to combine these two methods into a single, weighted model, which is more accurate than either method alone. We introduce a new decay function to control this transition. We evaluate our spatiotemporal model for approximating NDVI across 16 farm fields in Western Canada from 2018 to 2023. We empirically determined the best parameters for the decay function and distance-transform-based model. The results show a significant improvement compared to using only spatial or temporal approximations alone (up to a 263% improvement as measured by RMSE relative to the baseline). Furthermore, our model demonstrates a notable improvement compared to simple combination (up to 51% improvement as measured by RMSE) and Spatiotemporal Kriging (up to 28% improvement as measured by RMSE). Finally, we apply our spatiotemporal model in a case study related to improving the specification of the peak green day for numerous fields.

The effective management of spatiotemporal Earth observation data is a significant challenge due to their growing size and scale, geometric distortion, temporal gaps, and restricted access. In this article, we introduce a novel methodology utilizing a Discrete Global Grid System (DGGS) to address a set of challenges related to spatiotemporal data storage with a live updating mechanism, the multiresolution processing of an arbitrary region of interest (ROI) in real time, and the approximation of missing data in a smooth, continuous manner. We use reverse Chaikin subdivision and B-spline curve fitting to handle temporal data gaps, allowing for real-time updates. Additionally, our work presents a triangular wavelet scheme to incorporate a flexible, tensor-based multiresolution storage scheme for spatiotemporal raster data. The case study we present uses data from the RADARSAT Constellation Mission (RCM) of the Canadian Space Agency (CSA). Our system enables the dynamic retrieval and visualization of time-varying data for a user-defined ROI. The obtained results demonstrate that our method ensures high data fidelity while making spatiotemporal data more accessible across various practical applications in Earth observation.

Recent advancements in geospatial technologies have significantly expanded the volume and diversity of geospatial data, unlocking new and innovative applications that require novel Geographic Information Systems (GIS). (Discrete) Global Grid Systems (DGGSs) have emerged as a promising solution to further enhance modern geospatial capabilities. Current DGGSs employ a simple, low-resolution polyhedral approximation of the Earth for efficient operations, but require a projection between the Earth’s surface and the polyhedral faces. Equal-area DGGSs are desirable for their low distortion, but they fall short of this promise due to the inefficiency of equal-area projections. On the other hand, efficiency-first DGGSs need to better address distortion. We introduce a novel mesh-based DGGS (MBD) which generalizes efficient operations over watertight triangular meshes with spherical topology. Unlike traditional approaches that rely on Platonic or Catalan solids, our mesh-based method leverages high-resolution spherical meshes to offer greater flexibility and accuracy. MBD allows high-resolution polyhedra (HRP) to be used as the base polyhedron of a DGGS, significantly reducing distortion. To address the operational challenges, we introduce a new hash encoding method and an efficient barycentric indexing method (BIM). MBD extends Atlas of Connectivity Maps to the BIM to provide efficient spatial and hierarchical traversal. We introduce several new base polyhedra with lower areal and angular distortion, and we experimentally validate their properties and demonstrate their efficiency. Our experimentation shows that we achieve constant-time operations for high-resolution MBD, and we recommend polyhedra to be used as the base polyhedron for low-distortion DGGSs, compact faces, and efficient operations.