arXiv:2605.22556v1 Announce Type: new Abstract: Digital elevation models (DEMs) underpin terrain analysis in Geographic Information Systems (GIS), but in their common raster form, they rely on interpolation for off-grid sampling and finite-difference operators for derivative-based analysis. Implicit neural representations (INRs) offer a continuous alternative, but prior terrain INRs lack explicit frequency control, neglect the gradient structure of terrain, and remain too large and costly to train for practical deployment. We present ImplicitTerrainV2, which advances terrain INRs toward a comp
The continuous evolution of AI and machine learning techniques, specifically implicit neural representations, is now being applied to long-standing challenges in spatial data processing like Digital Elevation Models.
This development allows for more accurate, efficient, and continuous representation and analysis of terrain data, critical for applications in simulation, environmental modeling, and autonomous systems.
Traditional raster-based DEMs, with their reliance on interpolation and finite-difference operators, will be complemented or potentially superseded by more flexible and computationally efficient continuous implicit representations.
- · GIS software developers
- · Autonomous vehicle companies
- · Environmental modeling researchers
- · Gaming and simulation industries
- · Legacy GIS data providers reliant on raster formats
Improved fidelity and real-time processing of geographic and topographic data become widely accessible.
Advanced planning and simulation for infrastructure, urban development, and disaster response become more precise and dynamic.
The integration of highly detailed, continuous terrain models could accelerate the maturity of complex autonomous systems operating in highly dynamic physical environments.
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Read at arXiv cs.LG