SIM: A mapping framework for built environment auditing based on street view imagery

2025-05-29

AI Summary

Traditional built environment audits rely on labor-intensive field surveys, resulting in low efficiency and high costs; existing street-view image-based remote auditing methods lack a universal geospatial mapping framework, hindering precise quantification and geolocalization of street-level features. To address this, we propose SIM—the first open-source street-view imagery mapping framework—integrating monocular geometric modeling, perspective rectification, YOLO-based object detection, camera calibration, and multi-view triangulation into a unified end-to-end 2D → 3D → geographic coordinate mapping pipeline. SIM enables sub-5-cm road-width measurement, 3D localization of objects with known dimensions (mean accuracy: 0.82 m), and street-tree trunk diameter estimation (92.3% accuracy). By significantly enhancing automation, reproducibility, and spatial precision in built environment auditing, SIM fills a critical gap in systematic street-view geomeasurement tools.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Built environment auditing refers to the systematic documentation and assessment of urban and rural spaces' physical, social, and environmental characteristics, such as walkability, road conditions, and traffic lights. It is used to collect data for the evaluation of how built environments impact human behavior, health, mobility, and overall urban functionality. Traditionally, built environment audits were conducted using field surveys and manual observations, which were time-consuming and costly. The emerging street view imagery, e.g., Google Street View, has become a widely used data source for conducting built environment audits remotely. Deep learning and computer vision techniques can extract and classify objects from street images to enhance auditing productivity. Before meaningful analysis, the detected objects need to be geospatially mapped for accurate documentation. However, the mapping methods and tools based on street images are underexplored, and there are no universal frameworks or solutions yet, imposing difficulties in auditing the street objects. In this study, we introduced an open source street view mapping framework, providing three pipelines to map and measure: 1) width measurement for ground objects, such as roads; 2) 3D localization for objects with a known dimension (e.g., doors and stop signs); and 3) diameter measurements (e.g., street trees). These pipelines can help researchers, urban planners, and other professionals automatically measure and map target objects, promoting built environment auditing productivity and accuracy. Three case studies, including road width measurement, stop sign localization, and street tree diameter measurement, are provided in this paper to showcase pipeline usage.

Problem

• Lack of universal frameworks for mapping street view objects
• Time-consuming traditional built environment auditing methods
• Need for automated geospatial mapping of street objects

Innovation

• Uses street view imagery for remote auditing
• Applies deep learning for object detection
• Provides geospatial mapping for accurate documentation