Treepedia 2.0: Applying Deep Learning for Large-scale Quantification of Urban Tree Cover

Recent advances in deep learning have made it possible to quantify urban metrics at fine resolution, and over large extents using street-level images. Here, we focus on measuring urban tree cover using Google Street View (GSV) images. First, we provide a small-scale labelled validation dataset and propose standard metrics to compare the performance of automated estimations of street tree cover using GSV. We apply state-of-the-art deep learning models, and compare their performance to a previously established benchmark of an unsupervised method. Our training procedure for deep learning models is novel; we utilize the abundance of openly available and similarly labelled street-level image datasets to pre-train our model. We then perform additional training on a small training dataset consisting of GSV images. We find that deep learning models significantly outperform the unsupervised benchmark method. Our semantic segmentation model increased mean intersection-over-union (IoU) from 44.10% to 60.42% relative to the unsupervised method and our end-to-end model decreased Mean Absolute Error from 10.04% to 4.67%. We also employ a recently developed method called gradient-weighted class activation map (Grad-CAM) to interpret the features learned by the end-to-end model. This technique confirms that the end-to-end model has accurately learned to identify tree cover area as key features for predicting percentage tree cover. Our paper provides an example of applying advanced deep learning techniques on a large-scale, geo-tagged and image-based dataset to efficiently estimate important urban metrics. The results demonstrate that deep learning models are highly accurate, can be interpretable, and can also be efficient in terms of data-labelling effort and computational resources.Comment: Accepted and will appear in IEEE BigData Congress 2018 Conference Proceeding

Deep Learning Architect: Classification for Architectural Design through the Eye of Artificial Intelligence

This paper applies state-of-the-art techniques in deep learning and computer vision to measure visual similarities between architectural designs by different architects. Using a dataset consisting of web scraped images and an original collection of images of architectural works, we first train a deep convolutional neural network (DCNN) model capable of achieving 73% accuracy in classifying works belonging to 34 different architects. Through examining the weights in the trained DCNN model, we are able to quantitatively measure the visual similarities between architects that are implicitly learned by our model. Using this measure, we cluster architects that are identified to be similar and compare our findings to conventional classification made by architectural historians and theorists. Our clustering of architectural designs remarkably corroborates conventional views in architectural history, and the learned architectural features also coheres with the traditional understanding of architectural designs.Comment: 22 pages, 5 figures, 4 table

Cyclists’ exposure to air pollution, noise, and greenery: a population-level spatial analysis approach

Urban travel exposes people to a range of environmental qualities with significant health and wellbeing impacts. Nevertheless, the understanding of travel-related environmental exposure has remained limited. Here, we present a novel approach for population-level assessment of multiple environmental exposure for active travel. It enables analyses of (1) urban scale exposure variation, (2) alternative routes’ potential to improve exposure levels per exposure type, and (3) by combining multiple exposures. We demonstrate the approach’s feasibility by analysing cyclists’ air pollution, noise, and greenery exposure in Helsinki, Finland. We apply an in-house developed route-planning and exposure assessment software and integrate to the analysis 3.1 million cycling trips from the local bike-sharing system. We show that especially noise exposure from cycling exceeds healthy thresholds, but that cyclists can influence their exposure by route choice. The proposed approach enables planners and individual citizens to identify (un)healthy travel environments from the exposure perspective, and to compare areas in respect to how well their environmental quality supports active travel. Transferable open tools and data further support the implementation of the approach in other cities.Peer reviewe

Understanding cities with machine eyes: A review of deep computer vision in urban analytics

Modelling urban systems has interested planners and modellers for decades. Different models have been achieved relying on mathematics, cellular automation, complexity, and scaling. While most of these models tend to be a simplification of reality, today within the paradigm shifts of artificial intelligence across the different fields of science, the applications of computer vision show promising potential in understanding the realistic dynamics of cities. While cities are complex by nature, computer vision shows progress in tackling a variety of complex physical and non-physical visual tasks. In this article, we review the tasks and algorithms of computer vision and their applications in understanding cities. We attempt to subdivide computer vision algorithms into tasks, and cities into layers to show evidence of where computer vision is intensively applied and where further research is needed. We focus on highlighting the potential role of computer vision in understanding urban systems related to the built environment, natural environment, human interaction, transportation, and infrastructure. After showing the diversity of computer vision algorithms and applications, the challenges that remain in understanding the integration between these different layers of cities and their interactions with one another relying on deep learning and computer vision. We also show recommendations for practice and policy-making towards reaching AI-generated urban policies

Helsingin vihernäkymien kartoitus Googlen katunäkymäkuvista

Kaupunkikasvillisuutta on perinteisesti kartoitettu kaukokartoitusmenetelmin kuten laserkeilaamalla ja ilmakuvatulkintana. Ylhäältä käsin tehtävä kaukokartoitus ei kuitenkaan aina pysty antamaan todenmukaista kuvaa siitä vihreän kasvillisuuden määrästä, jonka ihminen näkee kadulla liikkuessaan. Perinteisten menetelmien rinnalle on viimeaikaisissa tutkimuksissa esitetty katunäkymäkuvilta havainnoitavaa vihernäkymää. Vihernäkymää mitataan viherindeksillä, joka kertoo vihreän kasvillisuuden prosentuaalisen osuuden katunäkymästä tietyllä sijainnilla. Tämän tutkimuksen tavoitteena oli luoda katunäkymistä laskettu vihernäkymäaineisto Helsingistä, sekä tutkia ihmisen perspektiivin ja ylhäältä päin kuvatun aineiston eroja kaupunkivihreyden kartoituksessa. Tutkimuksen aineistona käytettiin Googlen ohjelmointirajapinnasta ladattuja katunäkymäkuvia Helsingistä. Aineisto rajautui niille alueille, joilta Googlen katunäkymäkuvia oli saatavilla kesäkuukausilta. Perustuen katunäkymäkuvilta laskettuihin viherindeksi arvoihin, laadittiin Helsingistä vihernäkymäkarttoja eri spatiaalisilla tarkastelutasoilla. Jotta voitaisi ymmärtää perspektiiveistä aiheutuvia eroja, vihernäkymää vertailtiin Helsingin seudulliseen maanpeiteaineistoon lineaarisella regressiolla. Alueita, joilla aineistot erosivat toisistaan huomattavasti, tarkasteltiin visuaalisesti katunäkymäkuvien kautta. Osana tutkimusta Helsingin vihernäkymää vertailtiin myös kansainvälisesti kaupunkeihin, joista vastaava aineisto oli saatavilla. Tutkimuksessa ilmeni Helsingin vihernäkymän jakautuvan varsin epätasaisesti. Alhaisimpia viherindeksi arvoja esiintyi erityisesti kantakaupungissa, teollisuusalueilla, sekä lähi- ja liikekeskuksissa. Korkeimpia viherindeksiarvoja havaittiin omakotitalovaltaisilla asuinalueilla. Vertailtaessa maanpeiteaineistoon, viherindeksin havaittiin korreloivan heikosti matalan kasvillisuuden kanssa. Puuston kanssa korrelaatio oli selvästi voimakkaampi. Eroja aineistojen välillä havaittiin olevan erityisesti alueilla, joilla kasvillisuus ei erilaisista syistä näy kadulle. Virhelähteitä aiheuttivat vanhimmat katunäkymäkuvat, sekä kasvillisuuden tunnistusmenetelmään liittyvät virheet, kuten muut vihreät objektit, sekä kirkkaiden valaistusolosuhteiden aiheuttamat varjot. Vaikka Helsingissä on paljon puistoja ja viheralueita, katunäkymä ei aina näyttäydy kovin vihreänä. Tässä tutkimuksessa luotu aineisto auttaa ymmärtämään ihmisten havainnoiman katuvihreyden alueellista jakautumista ja tuo ihmisen näkökulman perinteisten kaukokartoitusaineistojen rinnalle. Yhdistettynä aikaisempiin kaupunkivihreysaineistoihin, vihernäkymäaineisto auttaa rakentamaan kokonaisvaltaisemman kuvan Helsingin kaupunkivihreydestä.Urban vegetation has traditionally been mapped through traditional ways of remote sensing like laser scanning and aerial photography. However, it has been stated that the bird view examination of vegetation cannot fully represent the amount of green vegetation that the citizens observe on street level. Recent studies have raised human perspective methods like street view images and measuring of green view next to more traditional ways of mapping vegetation. Green view index states the percentage of green vegetation in street view on certain location. The purpose for this study was to create a green view dataset of Helsinki city through street view imagery and to reveal the differences between human perspective and aerial perspective in vegetation mapping. Street view imagery of Helsinki was downloaded from Google street view application interface. The spatial extent of the data was limited by the availability of street view images of summer months. Several green view maps of Helsinki were created based on the green view values calculated on the street view images. In order to understand the differences between human perspective and the aerial view, the green view values were compared with the regional land cover dataset of Helsinki trough linear regression. Areas with big differences between the datasets were examined visually through the street view imagery. Helsinki green view was also compared internationally with other cities with same kind of data available. It appealed that the green view of Helsinki was divided unequally across the city area. The lowest green view values were found in downtown, industrial areas and the business centers of the suburbs. Highest values were located at the housing suburbs. When compared with the land cover, it was found that the green view has a weak correlation with low vegetation and relatively high correlation with taller vegetation such as trees. Differences between the datasets were mainly concentrated on areas where the vegetation was not visible from the street by several reasons. Main sources of errors were the oldest street view images and the flaws in image classification caused by other green objects and shadows. Even though Helsinki has many parks and other green spaces, the greenery visible to the streets isn’t always that high. The green view dataset created in this study helps to understand the spatial distribution of street greenery and brings human perspective next to more traditional ways of mapping city vegetation. When combined with previous city greenery datasets, the green view dataset can help to build up more holistic understanding of the city greenery in Helsink

Accessing eye-level greenness visibility from open-source street view images: A methodological development and implementation in multi-city and multi-country contexts

The urban natural environment provides numerous benefits, including augmenting the aesthetic appeal of urban landscapes and improving mental well-being. While diverse methods have been used to evaluate urban greenery, the assessment of eye-level greenness visibility using street-view level images is emerging due to its greater compatibility with human perception. Many existing studies predominantly rely on proprietary street view images provider such as Google Street View (GSV) data; the usage restrictions and lack of alignment with FAIR (Findability, Accessibility, Interoperability, and Reusability) principles present challenges in using proprietary images at scale. Therefore, incorporating Volunteered Street View Imagery (VSVI) platforms, such as Mapillary, is emerging as a promising alternative. In this study, we present a scalable and reproducible methodological framework for utilising Mapillary images for Green View Index (GVI) assessment using image segmentation approach and evaluate the completeness and usefulness of such data in diverse geographical contexts, including eleven cities (i.e., Amsterdam, Barcelona, Buenos Aires, City of Melbourne, Dhaka, Ho Chi Minh, Kampala, Kobe, Mexico City, Seattle, and Tel Aviv). We also evaluate the use of globally available satellite-based vegetation indices (e.g., Normalised Difference Vegetation Index-NDVI) to estimate GVI in locations where street-view images are unavailable. Our approach demonstrates the applicability of Mapillary data for GVI assessments, although revelling considerable disparities in image availability and usability between cities located in developed and developing countries. We also identified that the NDVI could be used effectively to estimate GVI values in locations where direct street-level imagery is limited. Additionally, the analysis reveals notable differences in greenness visibility across cities, particularly in high-density, lower-income cities in Africa and South Asia, compared to low-density, high-income cities in the USA and Europe

Measuring the 3-30-300 rule to help cities meet nature access thresholds

The 3-30-300 rule offers benchmarks for cities to promote equitable nature access. It dictates that individuals should see three trees from their dwelling, have 30 % tree canopy in their neighborhood, and live within 300 m of a high-quality green space. Implementing this demands thorough measurement, monitoring, and evaluation methods, yet little guidance is currently available to pursue these actions. To overcome this gap, we employed an expert-based consensus approach to review the available ways to measure 3-30-300 as well as each measure's strengths and weaknesses. We described seven relevant data and processes: vegetation indices, street level analyses, tree inventories, questionnaires, window view analyses, land cover maps, and green space maps. Based on the reviewed strengths and weaknesses of each measure, we presented a suitability matrix to link recommended measures with each component of the rule. These recommendations included surveys and window-view analyses for the ‘3 component’, high-resolution land cover maps for the ‘30 component’, and green space maps with network analyses for the ‘300 component’. These methods, responsive to local situations and resources, not only implement the 3-30-300 rule but foster broader dialogue on local desires and requirements. Consequently, these techniques can guide strategic investments in urban greening for health, equity, biodiversity, and climate adaptation

A computer vision system for detecting and analysing critical events in cities

Whether for commuting or leisure, cycling is a growing transport mode in many cities worldwide. However, it is still perceived as a dangerous activity. Although serious incidents related to cycling leading to major injuries are rare, the fear of getting hit or falling hinders the expansion of cycling as a major transport mode. Indeed, it has been shown that focusing on serious injuries only touches the tip of the iceberg. Near miss data can provide much more information about potential problems and how to avoid risky situations that may lead to serious incidents. Unfortunately, there is a gap in the knowledge in identifying and analysing near misses. This hinders drawing statistically significant conclusions to provide measures for the built-environment that ensure a safer environment for people on bikes. In this research, we develop a method to detect and analyse near misses and their risk factors using artificial intelligence. This is accomplished by analysing video streams linked to near miss incidents within a novel framework relying on deep learning and computer vision. This framework automatically detects near misses and extracts their risk factors from video streams before analysing their statistical significance. It also provides practical solutions implemented in a camera with embedded AI (URBAN-i Box) and a cloud-based service (URBAN-i Cloud) to tackle the stated issue in the real-world settings for use by researchers, policy-makers, or citizens. The research aims to provide human-centred evidence that may enable policy-makers and planners to provide a safer built environment for cycling in London, or elsewhere. More broadly, this research aims to contribute to the scientific literature with the theoretical and empirical foundations of a computer vision system that can be utilised for detecting and analysing other critical events in a complex environment. Such a system can be applied to a wide range of events, such as traffic incidents, crime or overcrowding