自动化地质图数字化系统，提升矿产资源评估效率
DIGMAPPER: A Modular System for Automated Geologic Map Digitization
- 分模块架构集成深度学习模型，实现地图布局分析与要素提取
- 在100+张标注地图上实现高精度多类型要素识别与地理配准
- 适合地质调查、资源评估人员使用，支持国家尺度矿产分析
历史地质图包含岩石单元、断层、褶皱和层面等丰富的地理空间信息，对评估可再生能源、电动汽车及国家安全所需的矿产资源至关重要。然而，地图数字化仍是一项耗时费力的任务。我们提出DIGMAPPER，一种与美国地质调查局（USGS）合作开发的模块化、可扩展系统，用于自动化地质图数字化。该系统采用全容器化、工作流编排的架构，整合了先进的深度学习模型，用于地图版面分析、特征提取与地理配准。针对训练数据有限和视觉内容复杂等问题，系统引入创新方法，包括大语言模型的上下文学习、合成数据生成及基于Transformer的模型。在DARPA-USGS数据集超过100张标注地图上的评估表明，系统在多边形、线状和点状要素提取方面均表现高精度，地理配准性能可靠。该系统已部署于USGS，显著加速了分析就绪地理空间数据集的创建，支撑全国范围关键矿产评估与更广泛的地球科学应用。
原文摘要 · Abstract (English)
Historical geologic maps contain rich geospatial information, such as rock units, faults, folds, and bedding planes, that is critical for assessing mineral resources essential to renewable energy, electric vehicles, and national security. However, digitizing maps remains a labor-intensive and time-consuming task. We present DIGMAPPER, a modular, scalable system developed in collaboration with the United States Geological Survey (USGS) to automate the digitization of geologic maps. DIGMAPPER features a fully dockerized, workflow-orchestrated architecture that integrates state-of-the-art deep learning models for map layout analysis, feature extraction, and georeferencing. To overcome challenges such as limited training data and complex visual content, our system employs innovative techniques, including in-context learning with large language models, synthetic data generation, and transformer-based models. Evaluations on over 100 annotated maps from the DARPA-USGS dataset demonstrate high accuracy across polygon, line, and point feature extraction, and reliable georeferencing performance. Deployed at USGS, DIGMAPPER significantly accelerates the creation of analysis-ready geospatial datasets, supporting national-scale critical mineral assessments and broader geoscientific applications.
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