Prompt2DEM: High-Resolution DEMs for Urban and Open Environments from Global Prompts Using a Monocular Foundation Model

• URL: http://arxiv.org/abs/2507.09681v2
• Date: Mon, 21 Jul 2025 13:29:28 GMT
• Title: Prompt2DEM: High-Resolution DEMs for Urban and Open Environments from Global Prompts Using a Monocular Foundation Model
• Authors: Osher Rafaeli, Tal Svoray, Ariel Nahlieli,
• Abstract summary: High-resolution elevation estimations are essential to understand catchment and hillslope hydrology, study urban morphology and dynamics, and monitor the growth, decline, and mortality of terrestrial ecosystems.<n>We present here a framework for the estimation of high-resolution DEMs as a new paradigm for absolute global elevation mapping.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-resolution elevation estimations are essential to understand catchment and hillslope hydrology, study urban morphology and dynamics, and monitor the growth, decline, and mortality of terrestrial ecosystems. Various deep learning approaches (e.g., super-resolution techniques, monocular depth estimation) have been developed to create high-resolution Digital Elevation Models (DEMs). However, super-resolution techniques are limited by the upscaling factor, and monocular depth estimation lacks global elevation context, making its conversion to a seamless DEM restricted. The recently introduced technique of prompt-based monocular depth estimation has opened new opportunities to extract estimates of absolute elevation in a global context. We present here a framework for the estimation of high-resolution DEMs as a new paradigm for absolute global elevation mapping. It is exemplified using low-resolution Shuttle Radar Topography Mission (SRTM) elevation data as prompts and high-resolution RGB imagery from the National Agriculture Imagery Program (NAIP). The approach fine-tunes a vision transformer encoder with LiDAR-derived DEMs and employs a versatile prompting strategy, enabling tasks such as DEM estimation, void filling, and updating. Our framework achieves a 100x resolution gain (from 30-m to 30-cm), surpassing prior methods by an order of magnitude. Evaluations across three diverse U.S. landscapes show robust generalization, capturing urban structures and fine-scale terrain features with < 5 m MAE relative to LiDAR, improving over SRTM by up to 18%. Hydrological analysis confirms suitability for hazard and environmental studies. We demonstrate scalability by applying the framework to large regions in the U.S. and Israel. All code and pretrained models are publicly available at: https://osherr1996.github.io/prompt2dem_propage/.

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