Estimating urban noise along road network from street view imagery

Reconstructing Urban Road Traffic Noise Sources Using High-Resolution Satellite Imagery and Vehicle-Based Traffic Activity Estimation

Urban-scale road traffic noise mapping is frequently constrained by limited access to spatially continuous traffic observations, particularly in data-scarce cities. This study developed an integrated framework for reconstructing road-segment traffic activity from high-resolution satellite imagery and applying it to phy...

NSVD: Vehicle Image Dataset from Northern Slovakia Under Diverse Weather Conditions

Existing vehicle detection models, typically trained on general-purpose or non-regional datasets, frequently underperform when applied to local traffic monitoring systems that rely on fixed roadside cameras. Changes in viewpoint, object size, local vehicle types and road conditions creates a domain mismatch between the...

Drive-by Urban Freight Sensing: Large-Scale Fine-Grained Truck Classification Using Google Street View Imagery and Deep Learning

A novel drive-by sensing framework using Google Street View imagery (GSV) combined with advanced computer vision and deep learning methods to generate fine-grained truck classification data at scale in urban areas and demonstrates the potential of computer vision and drive-by sensing technologies for scalable urban fre...

From Street View Imagery to Street Quality Indicators: Vision Language Inference for the Suburban 15-minute City

This paper presents a planning-oriented assessment of streetscape qualities in the north-eastern periphery of Nice using the latest release of SAGAI (Streetscape Analysis with Generative AI), an open-source workflow that leverages vision-language models for large-scale streetscape analysis from Google Street View image...

TDE-RYOLO: haze environment in traffic density estimation using deep learning-based REP-YOLOV9 network

Investigating the impact of Sentinel-2 image super-resolution on urban road detection in Dubai

Road segmentation from satellite imagery is critical for urban planning and transportation analysis, but is often limited by the low spatial resolution of publicly available data and the high cost of high-resolution alternatives. This study evaluates the impact of super-resolution (SR) on urban road network extraction...