TERRA: Topological Extraction for Rapid Response Analysis – A Literature Survey

Abstract: Up-to-date road networks are essential for rescue and relief operations after earthquakes, floods and wildfires, yet roads are often unmapped, occluded or damaged when they are needed most. This paper presents a literature survey carried out as the groundwork for TERRA (Topological Extraction for Rapid Response Analysis), a project on extracting road networks from satellite imagery for rapid response. Sixteen recent studies are reviewed and organised into five themes: encoder–decoder segmentation networks, graph- and topology-based road extraction, rural and emergency road mapping, disaster damage assessment, change detection and road condition monitoring, and disaster-aware path planning. For each study the methodology, reported results and limitations are summarised and compared. The survey shows that segmentation networks achieve good pixel-level accuracy but fragment narrow and occluded roads, that graph-based methods improve topological correctness at higher cost or with extra prerequisites, and that few works connect road extraction with damage-aware routing. Common research gaps are consolidated and a direction for TERRA is outlined.

Keywords: Road Extraction, Satellite Imagery, Deep Learning, Semantic Segmentation, Road Topology, Graph Neural Networks, Disaster Management, Emergency Response.