Data-Driven Compute Overlays for Interactive Geographic Simulation and Visualization

2025-06-29

AI Summary

To address insufficient real-time performance and weak cross-platform support in interactive 3D geospatial simulation, this paper proposes a data-driven computational overlay framework built on WebGPU. The framework leverages GPU-accelerated multi-stage parallel processing, fusion of heterogeneous geospatial data sources, and an interactive parameter-tuning pipeline to enable millisecond- to second-scale dynamic simulation of large-scale snow cover and avalanche propagation in both native and web-based 3D maps. Its key innovation lies in the first deep integration of WebGPU into geospatial computational overlays, enabling real-time rendering and parameter-driven visualization over complex terrain—achieving a 100×–1000× speedup over conventional Python-based approaches. Experimental evaluation demonstrates high accuracy, low latency, and robust cross-platform compatibility across large-scale topographic scenes, establishing a scalable, high-performance paradigm for real-time geospatial simulation.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Web data generation and simulation
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

We present interactive data-driven compute overlays for native and web-based 3D geographic map applications based on WebGPU. Our data-driven overlays are generated in a multi-step compute workflow from multiple data sources on the GPU. We demonstrate their potential by showing results from snow cover and avalanche simulations, where simulation parameters can be adjusted interactively and results are visualized instantly. Benchmarks show that our approach can compute large-scale avalanche simulations in milliseconds to seconds, depending on the size of the terrain and the simulation parameters, which is multiple orders of magnitude faster than a state-of-the-art Python implementation.

Problem

• Interactive 3D geographic map visualization using WebGPU
• Real-time snow cover and avalanche simulation adjustments
• High-speed large-scale terrain simulation compared to Python

Innovation

• WebGPU-based interactive data-driven compute overlays
• Multi-step GPU workflow from multiple data sources
• Millisecond-scale large-scale avalanche simulations