Urban Sensing, Video Imagery and Active Measurement

Marzo 2026. € 15,00 Italy only. Data di uscita: 05/03/2026. Periodico mensile Monthly periodical. Europe € 25,00 / Switzerland CHF 27,00 / United Kingdom £ 24,95 / USA $ 24,95 / Deutschland – France € 28,00. Poste Italiane S.p.A. Spedizione in Abbonamento Postale D.L.353/2003 (conv. in Legge 27/02/2004 n.46), Articolo 1, Comma 1, DCB-Milano 1110 March 2026 Non più dominio di pochi, ma un processo condiviso a cui chiunque può prendere parte No longer the domain of a select few, but a shared process in which anyone can take part L’architettura è partecipazione / Architecture is participation 1110 Marzo / March 2026 EDITORIALE / Editorial Ma Y ansong L’architettura non è architettura: è partecipazione / 1 Architecture is not architecture: it is participation PUNTI DI VIST A / Points of view Sulla partecipazione / On participation 6 Jason Ho (Mapping Workshop), Guangzhou, CN Nasios Varnavas, Era Savvides (Urban Radicals), Londra London, UK; Nicosia, CY Liu Yuelai, Tongji, CN Cass R. Sunstein, Concord, US Xu Weichao, Pechino Beijing, CN Collectif Etc, Marsiglia Marseille, FR Chao Deng, Shanghai, CN Long Ying, Qi Hao, Pechino Beijing, CN May East,Edimburgo Edinburgh, UK Jude Barber (Collective Architecture), Glasgow, UK;

Continuous and accurate monitoring of agricultural landscapes is crucial for understanding crop phenology and responding to climatic and anthropogenic changes. However, the widely used optical satellite remote sensing is limited by revisit cycles and weather conditions, leading to gaps in agricultural monitoring. To address these limitations, we designed and deployed a Near Surface Camera (NSCam) Network across China, and explored its application in agricultural land monitoring and achieving climate-smart agriculture (CSA). By analyzing the image data captured by the NSCam Network, we can accurately assess long-term or abrupt agricultural land changes. According to the preliminary monitoring results, integrating NSCam data with remote sensing imagery greatly enhances the temporal details and accuracy of agricultural monitoring, aiding agricultural managers in making informed decisions. The impacts of abnormal weather conditions and human activities on agricultural land, which are not captured by remote sensing imagery, can be complemented by incorporating our NSCam Network. The successful implementation of this method underscores its potential for broader application in CSA, promoting resilient and sustainable agricultural practices.

Differentiating transport and recreational walking in public spaces could promote the precise design of walkable public spaces for different walking demands to encourage more walking behaviors. However, previous studies mainly relied on field observations and self-reports, failing to quantitatively distinguish and depict the spatial- level usage of transport and recreational walking of all pedestrians passing by. This study proposed an approach based on the traffic counts and the number of pedestrians to infer transport and recreation walking in public spaces. A comparative experiment using Wi-Fi probes to collect pedestrian data in a gated residential community and a creative center in Beijing, China, was conducted to verify the applicability of this method. The results demonstrated that the transport walking index (the number of pedestrians) could portray the volume of transport walking, and the recreational walking index (average traffic counts of each pedestrian) could depict the proportion of recreational walking in public spaces. Two tests using different time threshold parameters and field observations verified the robustness of the results. Given the low-cost and long-duration observation, this method can potentially support the process of Post Occupancy Evaluation and Environment and Behavior research in more public spaces to make them more walkable.

Traffic noise, characterized by its highly fluctuating nature, is the second biggest environmental problem in the world. Highly dynamic noise maps are indispensable for managing traffic noise pollution, but two key difficulties exist in generating these maps: the lack of large amounts of fine-scale noise monitoring data and the ability to predict noise levels in the absence of noise monitoring data. This study proposed a new noise monitoring method, the Rotating Mobile Monitoring method, that combines the advantages of stationary and mobile monitoring methods and expands the spatial extent and temporal resolution of noise data. A monitoring campaign was conducted in the Haidian District of Beijing, covering 54.79 km of roads and a total area of 22.15 km 2 , and gathered 18,213 A-weighted equivalent noise (LAeq) measurements at 1-s intervals from 152 stationary sampling sites. Additionally, street view images, meteorological data and built environment data were collected from all roads and stationary sites. Using computer vision and GIS analysis tools, 49 predictor variables were measured in four categories, including microscopic traffic composition, street form, land use and meteorology. Six machine learning models and linear regression models were trained to predict LAeq, with random forest performing the best (R 2 = 0.72, RMSE = 3.28 dB), followed by K-nearest neighbors regression (R 2 = 0.66, RMSE = 3.43 dB). The optimal random forest model identified distance to the major road, tree view index, and the maximum field of view index of cars in the last 3 s as the top three contributors. Finally, the model was applied to generate a 9-day traffic noise map of the study area at both the point and street levels. The study is easily replicable and can be extended to a larger spatial scale to obtain highly dynamic noise maps.

This output forms part of the Beijing City Lab research direction on Urban Sensing, Video Imagery and Active Measurement. Download the publication below.

This output forms part of the Beijing City Lab research direction on Urban Sensing, Video Imagery and Active Measurement. Download the publication below.

This output forms part of the Beijing City Lab research direction on Urban Sensing, Video Imagery and Active Measurement. Download the publication below.

This output forms part of the Beijing City Lab research direction on Urban Sensing, Video Imagery and Active Measurement. Download the publication below.

With the advent of the Fourth Industrial Revolution, people have begun to explore the potential for new technologies and new devices in studying the relationship between human behavior and urban design. The emergence of wearable cameras offers more possibilities for monitoring individual behavior in built environments as a kind of “lifelog.” This article explores the applications of wearable cameras in studying the relationship between individual behavior and built environments. Using manual image identification, image recognition with Computer Vision Application Programming Interface (API), and color calculation in Matlab, this study analyzed 8,598 photos recording the volunteer’s behaviors and activities during a week. Based on high-accuracy manual image identification results, the research analyzed the volunteer’s behavior, time use, movement path, and experiencing scenes. The study showed that the big data base of images collected by the wearable cameras contained rich individual activities and spatiotemporal information that could be used to effectively describe the individual behavior in space and further contribute to the study of the relationship between individual behaviors and built environments.

With the advent of the Fourth Industrial Revolution, people have begun to explore the potential for new technologies and new devices in studying the relationship between human behavior and urban design. The emergence of wearable cameras offers more possibilities for monitoring individual behavior in built environments as a kind of “lifelog.” This article explores the applications of wearable cameras in studying the relationship between individual behavior and built environments. Using manual image identification, image recognition with Computer Vision Application Programming Interface (API), and color calculation in Matlab, this study analyzed 8,598 photos recording the volunteer’s behaviors and activities during a week. Based on high-accuracy manual image identification results, the research analyzed the volunteer’s behavior, time use, movement path, and experiencing scenes. The study showed that the big data base of images collected by the wearable cameras contained rich individual activities and spatiotemporal information that could be used to effectively describe the individual behavior in space and further contribute to the study of the relationship between individual behaviors and built environments.

In this paper we consider the usefulness, feasibility and methodology of voluntary participatory urban sensing for contributing to the assessment of alternative urban infrastructure investment plans. Participatory urban sensing in this context means the involvement of individual citizens and community groups in collecting, sifting and using urban sensing data and associated meta-data for a specific purpose or purposes. We further limit the discussion to volunteered sensing data. The review and two online Delphi surveys carried in Beijing show that voluntary participatory urban sensing could potentially fill an important gap regarding inputs into the data-hungry though necessary predictive models. The questionnaire results have provided the first parameters for survey design and implementation [The Delphi work is on-going and we will be able to report insights from a further iteration of the survey at the conference] _______________________________________________________ Y. Jin (corresponding author) • Y. Long • S.Denman • D.B Deng • X.Rong, X. Jiao, Y. Jin Martin Centre for Architectural and Urban Studies, Cambridge University, 1 Scroope Terrace, Cambridge CB2 1PX, UK Beijing Institute of City Planningand Beijing City Lab, NanLi Shi Lu, Beijing, China Y.Jin. Email: yj242@cam.ac.uk CUPUM 2015 119-Paper 1