Analysis of aerial images provided by satellites enables continuous monitoring and is a central component of many applications, including precision farming. Nonetheless, this analysis is often impeded by the presence of clouds and cloud shadows, which obscure the underlying region of interest and introduce incorrect values that bias analysis. In this paper, we outline a method for cloud shadow detection, and demonstrate our method using Canadian farmland data obtained from the Sentinel-2 satellite. Our approach builds on other object-based cloud and cloud shadow detection methods that generate preliminary shadow candidate masks which are refined by matching clouds to their respective shadows. We improve on these components by using ray-casting and inverse texture mapping methods to quickly identify cloud shadows, allowing for the immediate removal of false positives during image processing. Leveraging our ray-casting-based approach, we further improve our results by implementing a probability analysis based on the cloud probability layer provided by the Sentinel-2 satellite to account for missed shadow pixels. An evaluation of our method using the average producer (82.82%) and user accuracy (75.55%) both show a marked improvement over the performance of other object-based methods. Methodologically, our work demonstrates how incorporating probability analysis as a post-processing step can improve the generation of shadow masks.

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.