ArchiExplain: Multi-Level Evidence Chains for Precedent-Based Interpretability in Architectural Image Understanding

Deep neural networks have been widely applied in architectural analysis and design research, supporting tasks such as facade recognition, floor-plan analysis, and architectural visual classification. However, although existing models possess strong predictive capabilities, their decision-making processes remain characterized by a pronounced black-box nature, making it difficult to provide architects with understandable and traceable grounds for judgment. This limits their practical value in the architectural field, as designers require not only accurate outputs but also interpretable explanatory evidence regarding the basis of decision-making. This issue is particularly critical in architectural interpretation, where judgments are rarely made solely on the basis of isolated visual features, but are instead often formed through comparison and negotiation with precedents, spatial logic, and domain knowledge. To address this challenge, this paper proposes ArchiExplain, a multi-level interpretability framework for architectural image understanding, aiming to enable a deeper understanding of architectural images. The main contributions of this study are threefold: (1) We construct two architectural datasets for interpretability evaluation: a facade dataset composed of streetscape images from Harbin, China, and Greece, and a floor-plan dataset consisting of Real-plan drawings from real design cases and standardized generated R-plan drawings. Unlike existing datasets that primarily serve style recognition, semantic parsing, or image generation tasks, the datasets in this paper focus on evaluating the correspondence among model explanations, precedent associations, visual evidence, and predictive judgments. (2) Based on the above datasets, we propose the ArchiExplain framework. Unlike attribution methods such as Grad-CAM, Saliency Maps, and Integrated Gradients, which mainly reveal local discriminative regions, or influence-based methods that only trace the influence of training samples, this framework integrates training-sample influence tracing, Saliency Maps, and Integrated Gradients. It establishes a unified evidential chain among precedent samples, discriminative image regions, and final predictions, thereby transforming neural network decisions into an interpretable reasoning process with architectural significance. (3) Experimental results show that ArchiExplain performs stably on 100 randomly selected test samples, achieving an accuracy of 98.41% in the facade classification task and 98.34% in the floor-plan classification task. Further deletion/occlusion faithfulness analysis shows that the main attribution methods outperform the random baseline. Meanwhile, a questionnaire study involving 28 architects further verifies the consistency between model explanations and human architectural cognition. These findings indicate that ArchiExplain can enhance the transparency of architectural deep learning models and has practical application potential in architectural design analysis, model diagnosis, and precedent-based learning.