ARTEMIS: A real-time efficient ortho-mapping and thematic identification system for UAV-based rapid response

Abstract

Rapid response to natural and human-made disasters requires both real-time mapping and identification of key targets-of-interest (TOIs)—capabilities missing in conventional Structure-from-Motion (SfM)-based unmanned aerial vehicle (UAV) mapping frameworks. While Simultaneous Localization and Mapping (SLAM)-based mapping systems offer real-time capability, they heavily depend on GPUs and reliable GNSS to process the challenging UAV imagery with high-resolution ($>$  10 megapixels) and low-overlap (60%–90%). However, these prerequisites are often unavailable in resource-constrained post-disaster deployments. To address these limitations, we introduce ARTEMIS, a CPU-centric, real-time ortho-mapping system with direct map interpretation capability. Key innovations include: (1) A projection-error-guided window search strategy, derived from generalized stereo geometry, that enables robust and efficient feature matching using lightweight descriptors (e.g., ORB) on challenging aerial data. (2) A novel, lightweight matching confidence metric that enables adaptive weighting within Bundle Adjustment (BA), prioritizing high-quality matches to enhance accuracy without tight GNSS reliance. (3) An end-to-end workflow that outputs thematic analysis automatically, using integrated state-of-the-art deep learning models (supervised and zero-shot) to identify key TOIs within the resulting Digital Orthophoto Maps (DOMs). To the best of our knowledge, this is the first study to develop and validate such an end-to-end system on real-world disaster datasets collected by first responders, covering geophysical (e.g., earthquakes), hydrological (e.g., debris flows), climatological (e.g., wildfires), and meteorological (e.g., hurricanes) events. Extensive experiments show that ARTEMIS performs up to 58× faster than SfM methods (e.g., COLMAP) in sparse reconstruction and 22× faster than commercial solutions (e.g., ContextCapture) in DOM generation, while maintaining $<$  0.5 m absolute positioning error. In mission-critical tasks like damage assessment, its thematic analysis achieves results (e.g., F1-scores and mIoU) directly comparable to those from offline, post-processed baselines. By bridging the gap between raw data collection and trustworthy intelligence, ARTEMIS demonstrates significant potential to empower immediate, informed decision-making in UAV-assisted emergency response.