Semantic Change Detection of Roads and Bridges: A Fine-grained Dataset and Multimodal Frequency-driven Detector

TL;DR

This paper introduces a new fine-grained dataset and a multimodal frequency-based framework for detecting semantic changes in roads and bridges, addressing challenges in urban infrastructure monitoring.

Contribution

It provides the first specialized dataset for semantic change detection of roads and bridges and proposes a novel frequency-domain multimodal detector leveraging wavelet transforms and semantic priors.

Findings

MFDCD achieves state-of-the-art results on RB-SCD and other datasets.

The RB-SCD dataset offers detailed annotations for 11 change categories.

The proposed method effectively models linear structure continuity and semantic ambiguities.

Abstract

Accurate detection of road and bridge changes is crucial for urban planning and transportation management, yet presents unique challenges for general change detection (CD). Key difficulties arise from maintaining the continuity of roads and bridges as linear structures and disambiguating visually similar land covers (e.g., road construction vs. bare land). Existing spatial-domain models struggle with these issues, further hindered by the lack of specialized, semantically rich datasets. To fill these gaps, we introduce the Road and Bridge Semantic Change Detection (RB-SCD) dataset. As the first benchmark to systematically target semantic change detection of roads and bridges, RB-SCD offers comprehensive fine-grained annotations for 11 semantic change categories. This enables a detailed analysis of traffic infrastructure evolution. Building on this, we propose a novel framework, the Multimodal Frequency-Driven Change Detector (MFDCD). MFDCD integrates multimodal features in the frequency domain through two key components: (1) the Dynamic Frequency Coupler (DFC), which leverages wavelet transform to decompose visual features, enabling it to robustly model the continuity of linear transitions; and (2) the Textual Frequency Filter (TFF), which encodes semantic priors into frequency-domain graphs and applies filter banks to align them with visual features, resolving semantic ambiguities. Experiments demonstrate the state-of-the-art performance of MFDCD on RB-SCD and three public CD datasets. The code will be available at https://github.com/DaGuangDaGuang/RB-SCD.