Verifiable Grounded Generation for Road Damage Reports with Revisable Evidence Contracts

Road photographs can support maintenance decisions only when suspected damage is translated into reports that explain what is observed and what remains uncertain. This translation is difficult because a detector may overlook nearby defects, while a written description may claim more than its predictions justify. We investigate these challenges through RoadTrust, a workflow connecting detector evaluation, targeted retraining, and evidence-based report generation. Candidate detectors are compared under a common evaluation protocol, and the selected architecture is trained and tested separately within each image collection. Controlled augmentation studies examine how object size and arrangement affect the visual evidence available for reporting. RoadTrust retains detections alongside available depth readings and optional location metadata, then checks generated descriptions against these records. Its reporting method distinguishes fidelity to an observation record from correctness about the physical road. Executable evidence contracts bind statements to their record identities, required fields, and canonical wording, providing a mechanism for checking which statements remain supported when evidence changes. An intervention audit examines altered wording, record substitutions, withdrawn evidence, and unrelated edits. A complementary writer-verifier workflow and human assessment examine whether natural-language claims are supported and whether their evidence is correct. This design places detector behavior, claim verification, and human judgment within a common inspection workflow. RoadTrust provides a practical framework for studying the full path from image to report, while its usefulness to inspectors and municipal maintenance requires field assessment.