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Visualizing the large-scale datasets output by HPC resources presents a difficult challenge, as the memory and compute power required become prohibitively expensive for end user systems. Novel view synthesis techniques can address this by producing a small, interactive model of the data, requiring only a set of training images to learn from. While these models allow accessible visualization of large data and complex scenes, they do not provide the interactions needed for scientific volumes, as they do not support interactive selection of transfer functions and lighting parameters. To address this, we introduce Volume Encoding Gaussians (VEG), a 3D Gaussian-based representation for volume visualization that supports arbitrary color and opacity mappings. Unlike prior 3D Gaussian Splatting (3DGS) methods that store color and opacity for each Gaussian, VEG decouple the visual appearance from the data representation by encoding only scalar values, enabling transfer function-agnostic rendering of 3DGS models. To ensure complete scalar field coverage, we introduce an opacity-guided training strategy, using differentiable rendering with multiple transfer functions to optimize our data representation. This allows VEG to preserve fine features across a dataset’s full scalar range while remaining independent of any specific transfer function. Across a diverse set of volume datasets, we demonstrate that our method outperforms the state-of-the-art on transfer functions unseen during training, while requiring a fraction of the memory and training time. 

Flood extent mapping is crucial for disaster response and damage assessment. While Earth imagery and terrain data (in the form of DEM) are now readily available, there are few flood annotation data for training machine learning models, which hinders the automated mapping of flooded areas. We propose ALFA, an interactive active-learning-based approach to minimize the annotators' efforts when preparing the ground-truth flood map in a satellite image. ALFA calibrates the prediction consistency of a segmentation model (1) across training cycles and (2) for various data augmentations. The two consistencies are integrated into the design of both the acquisition function and the loss function to enhance the robustness of active learning with limited annotation inputs. ALFA recommends those superpixels that the underlying model is most uncertain about, and users can annotate their pixels with minimal clicks with the help of elevation guidance. Extensive experiments on various regions hit by flooding show that we can improve the annotation time from hours to around 20 minutes. ALFA is open sourced at https://github.com/saugatadhikari/alfa.