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Sensing urban soundscapes from street view imagery / Tianhong Zhao in Computers, Environment and Urban Systems, vol 99 (January 2023)  

Public [article]  inComputers, Environment and Urban Systems > vol 99 (January 2023) . - n° 101915 Titre : Sensing urban soundscapes from street view imagery Type de document : Article/Communication Auteurs : Tianhong Zhao, Auteur ; Xiucheng Liang, Auteur ; Wei Tu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 101915 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] apprentissage profond [Termes IGN] bruit (audition) [Termes IGN] distribution spatiale [Termes IGN] image Streetview [Termes IGN] paysage sonore [Termes IGN] planification urbaine [Termes IGN] pollution acoustique [Termes IGN] Shenzhen [Termes IGN] Singapour [Termes IGN] ville durable [Vedettes matières IGN] Géovisualisation Résumé : (auteur) A healthy acoustic environment is an essential component of sustainable cities. Various noise monitoring and simulation techniques have been developed to measure and evaluate urban sounds. However, sensing large areas at a fine resolution remains a great challenge. Based on machine learning, we introduce a new application of street view imagery — estimating large-area high-resolution urban soundscapes, investigating the premise that we can predict and characterize soundscapes without laborious and expensive noise measurements. First, visual features are extracted from street-level imagery using computer vision. Second, fifteen soundscape indicators are identified and a survey is conducted to gauge them solely from images. Finally, a prediction model is constructed to infer the urban soundscape by modeling the non-linear relationship between them. The results are verified with extensive field surveys. Experiments conducted in Singapore and Shenzhen using half a million images affirm that street view imagery enables us to sense large-scale urban soundscapes with low cost but high accuracy and detail, and provides an alternative means to generate soundscape maps. reaches 0.48 by evaluating the predicted results with field data collection. Further novelties in this domain are revealing the contributing visual elements and spatial laws of soundscapes, underscoring the usability of crowdsourced data, and exposing international patterns in perception. Numéro de notice : A2023-014 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101915 Date de publication en ligne : 20/11/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101915 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102131 [article]

Using street view images to identify road noise barriers with ensemble classification model and geospatial analysis / Kai Zhang in Sustainable Cities and Society, vol 78 (March 2022)  

Public [article]  inSustainable Cities and Society > vol 78 (March 2022) . - n° 103598 Titre : Using street view images to identify road noise barriers with ensemble classification model and geospatial analysis Type de document : Article/Communication Auteurs : Kai Zhang, Auteur ; Zhen Qian, Auteur ; Yue Yang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 103598 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de groupement [Termes IGN] apprentissage profond [Termes IGN] cartographie du bruit [Termes IGN] Chine [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] distribution spatiale [Termes IGN] image Streetview [Termes IGN] lutte contre le bruit [Termes IGN] milieu urbain [Termes IGN] OpenStreetMap [Termes IGN] planification urbaine [Termes IGN] pollution acoustique [Termes IGN] trafic routier [Termes IGN] ville durable Résumé : (auteur) Road noise barriers (RNBs) are important urban infrastructures to relieve the harm of traffic noise pollution for citizens. Therefore, obtaining the spatial distribution characteristics of RNBs, such as precise positions and mileage, can be of great help for obtaining more accurate urban noise maps and assessing the quality of the urban living environment for sustainable urban development. However, an effective and efficient method for identifying RNBs and acquiring their attributes in large areas is scarce. This study constructs an ensemble classification model (ECM) to automatically identify RNBs at the city level based on Baidu Street View (BSV). Firstly, the bootstrap sampling method is proposed to build a street view image-based train set, where the effect of imbalanced categories of samples was reduced by adding confusing negative samples. Secondly, two state-of-the-art deep learning models, ResNet and DenseNet, are ensembled to construct an ECM based on the bagging framework. Finally, a post-processing method has been proposed based on geospatial analysis to eliminate street view images (SVIs) that are misclassified as RNBs. This study takes Suzhou, China as the study area to validate the proposed method. The model achieved an accuracy and F1-score of 0.98 and 0.90, respectively. The total mileage of the RNBs in Suzhou was 178,919 m. The results demonstrated the performance of the proposed RNBs identification framework. The significance of obtaining RNBs attributes for accelerating sustainable urban development has been demonstrated through the case of photovoltaic noise barriers (PVNBs). Numéro de notice : A2022-241 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.scs.2021.103598 Date de publication en ligne : 20/12/2021 En ligne : https://doi.org/10.1016/j.scs.2021.103598 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100167 [article]

Towards synthetic sensing for smart cities : a machine/deep learning-based approach / Faraz Malik Awan (2022)  

Public Titre : Towards synthetic sensing for smart cities : a machine/deep learning-based approach Type de document : Thèse/HDR Auteurs : Faraz Malik Awan, Auteur ; Noël Crespi, Directeur de thèse ; Roberto Minerva, Directeur de thèse Editeur : Courcouronnes : Télécom SudParis Année de publication : 2022 Importance : 106 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de doctorat de l’Institut Polytechnique de Paris préparée à Telecom SudParis, Spécialité Informatique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle [Termes IGN] analyse comparative [Termes IGN] apprentissage automatique [Termes IGN] apprentissage profond [Termes IGN] classification par arbre de décision [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par Perceptron multicouche [Termes IGN] Espagne [Termes IGN] parking [Termes IGN] plus proche voisin, algorithme du [Termes IGN] pollution acoustique [Termes IGN] pollution atmosphérique [Termes IGN] réseau neuronal récurrent [Termes IGN] système de transport intelligent [Termes IGN] trafic routier [Termes IGN] ville intelligente Résumé : (auteur) We worked on one of the most significant research directions in Smart City, i.e., Intelligent Transportation System (ITS). ITS encapsulates several domains, such as electronic vehicles notification systems, traffic information, smart parking, and environment. However, in this thesis, we target two of its important domains; i) Smart Parking, and ii) Road Traffic. We started our research with Smart Parking use case. Performing literature review, we realized that different Machine Learning (ML) and Deep Learning (DL) approaches have been used for smart parking solutions. In most of these proposed approaches, enclosed parking areas were targeted with different feature sets to predict the "occupancy rate" in parking areas. It inspired us to conduct a comparative analysis to answer following questions; Given the parking prediction use case, how do the traditional ML models perform as compared to complex DL models? Provided big data, can less complex, traditional ML models outperform complex DL models? How well these models can perform to predict the availability of the individual on-street parking spots rather than predicting the overall occupancy rate of an enclosed parking area. To answer these questions, we choose five well-known classical ML algorithms (K-Nearest Neighbours, Random Forest, Decision Tree) and DL algorithm (Multilayer Perceptron). To take our investigation into depth, we train Ensemble Learning Model, in which we combine all the above-mentioned ML and DL models. A huge parking dataset of city of Santander, Spain, has been used which consists of around 25 million records. We also propose to recommend available parking spots based on the current location of the driver. Moving forward with our research goals, we performed literature review on road traffic and found road traffic associated with air pollution and noise pollution often. However, to the best of our knowledge, air pollution & noise pollution have never been use d in traffic prediction problem. In this part of our research, firstly we used air pollution (CO, NO, NO2, NOx, and O3) along with the atmospheric variables, such as wind speed, wind direction, temperature, and pressure to improve the traffic forecasting in the city of Madrid. This successful experiment motivated us to extend our investigation to another factor, which is also strongly correlated with road traffic i.e., noise pollution. Hence, as an extension of our previous work, in this part of our research, we use noise pollution to improve the traffic prediction in the city of Madrid. Note de contenu : 1- Introduction 2- Parking space prediction using classical ML and deep learning models 3- Road traffic prediction improvement using air pollution and atmospheric data 4- Using noise pollution to improve traffic prediction 5- Conclusion and future work Numéro de notice : 20025 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/URBANISME Nature : Thèse française Note de thèse : Thèse de Doctorat : Informatique : Telecom SudParis : 2022 Organisme de stage : SAMOVAR DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-03722891/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101825

Cyclists' exposure to air pollution and noise in Mexico City : contribution of real-time traffic density indicators integrated into GIS / Philippe Apparicio in Revue internationale de géomatique, vol 30 n° 3-4 (juillet - décembre 2020)  

Public [article]  inRevue internationale de géomatique > vol 30 n° 3-4 (juillet - décembre 2020) . - pp 155 - 179 Titre : Cyclists' exposure to air pollution and noise in Mexico City : contribution of real-time traffic density indicators integrated into GIS Type de document : Article/Communication Auteurs : Philippe Apparicio, Auteur ; Jérémy Gelb, Auteur ; Paula Negron-Poblete, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 155 - 179 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse de données [Termes IGN] bicyclette [Termes IGN] dioxyde d'azote [Termes IGN] estimation bayesienne [Termes IGN] Mexico (Mexique) [Termes IGN] pollution acoustique [Termes IGN] pollution atmosphérique [Termes IGN] système d'information géographique [Termes IGN] temps réel [Termes IGN] trafic routier Résumé : (Auteur) Air pollution and road traffic noise are two important environmental nuisances that could be harmful to the health and well-being of urban populations. In Mexico City, as in many North American cities, there has been an upusurge in bicycle ridership. However, Mexico City is also well known for having high levels of noise and air pollution. The purpose of this study is threefold: 1) evaluate cyclists' exposure to air pollution (nitrogen dioxide) and road traffic noise; 2) identify local factors that increase or reduce cyclists' exposure, in paying particular attention to the type of road and bicycle path or lane used; and 3) evaluate the influence of real-time traffic density on cyclists' exposure. A total of 19 bicycle trips made in central Mexico City neighbourhoods were analyzed, representing nearly 11 hours and 137 km. The results of the Bayesian models show that type of road and bicyle infrastructure taken by the cyclist, and proximity to a main artery all have significant impacts on exposure levels. Finally, the variables introduced to control for the traffic encountered by cyclists had a significant positive effect on noise exposure, and a positive but not significant effect on nitrogen dioxide exposure. Numéro de notice : A2020-879 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.3166/rig.2021.00110 En ligne : https://doi.org/10.3166/rig.2021.00110 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100219 [article]

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ALERT: adversarial learning with expert regularization using Tikhonov operator for missing band reconstruction / Litu Rout in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020) Titre : ALERT: adversarial learning with expert regularization using Tikhonov operator for missing band reconstruction Type de document : Article/Communication Auteurs : Litu Rout, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] apprentissage dirigé [Termes IGN] bande spectrale [Termes IGN] cohérence géométrique [Termes IGN] correction d'image [Termes IGN] dégradation d'image [Termes IGN] image Worldview [Termes IGN] pollution acoustique [Termes IGN] qualité d'image [Termes IGN] régularisation de Tychonoff Résumé : (auteur) The Earth observation using remote sensing is one of the most important technologies to assimilate key attributes about the Earth’s surface. To achieve tangible consequence, the internal building blocks of such a complex system must operate flawlessly. However, due to a dynamically changing environment, degradation in sensor electronics, and extreme weather condition remotely sensed images often miss essential information. As the sensors operate over several years in space the likelihood of sensor degradation persists. This results in commonly observed issues, such as stripe noise, missing partial data, and missing band. Various ground-based solutions have been developed to address these technological bottlenecks individually. In this article, we devise a method, which we call ALERT, to tackle missing band reconstruction. The proposed method reconstructs the missing band with the sole supervision of spectral and spatial priors. We compare the proposed framework with state-of-the-art methods and show compelling improvement both qualitatively and quantitatively. We provide both theoretical and empirical evidence of better performance by regularized adversarial learning as compared to complete supervision. Furthermore, we propose a new residual-dense-block (RDB) module to preserve geometric fidelity and assist in efficient gradient flow. We show that ALERT captures essential features such that the spatial and spectral characteristics of the reconstructed band remains preserved. To critically analyze the generalization we test the performance on two different satellite data sets: Resourcesat-2A and WorldView-2. As per our extensive experimentation, the proposed method achieves 20.72%, 13.81%, 1.05%, 15.91%, and 2.94% improvement in the root mean square error (RMSE), SAM, SSIM, PSNR, and SRE, respectively, over the state-of-the-art model. Numéro de notice : A2020-285 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2963818 Date de publication en ligne : 16/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2963818 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95108 [article]

Multiscale Intensity Propagation to Remove Multiplicative Stripe Noise From Remote Sensing Images / Hao Cui in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

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Human mobility, spatiotemporal context, and environmental health / Mei-Po Kwan (2019)  

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Geovisualization of the impact of ship traffic on marine mammals / Nolwenn Le Poulain (2017) 

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Ship Traffic Simulation to assess noise exposure of whale populations in Canada / Axel Orger (2017) 

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Information géographique environnementale et conception d'infrastructure : quel détail pour l'information partagée ? / Charles-Edouard Tolmer in XYZ, n° 147 (juin - août 2016)

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Amélioration de la cartographie des ambiances sonores urbaines / Kamal Serrhini (2016) 

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Observer et prévenir / Françoise de Blomac in DécryptaGéo le mag, n° 163 (janvier 2015)

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Le développement durable à découvert / Agathe Euzen (2013)

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Bruits et odeurs : Une approche positive encore en chantier / Françoise de Blomac in SIG la lettre, n° 140 (octobre 2012)

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Mobilités urbaines et risques des transports / Arnaud Banos (2010)

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