Centre de documentation
scientifique

Solid waste mapping based on very high resolution remote sensing imagery and a novel deep learning approach / Bowen Niu in Geocarto international, vol 38 n° 1 ([01/01/2023])  

Public [article]  inGeocarto international > vol 38 n° 1 [01/01/2023] . - n° 2164361 Titre : Solid waste mapping based on very high resolution remote sensing imagery and a novel deep learning approach Type de document : Article/Communication Auteurs : Bowen Niu, Auteur ; Quanlong Feng, Auteur ; Jianyu Yang, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 2164361 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] cartographie thématique [Termes IGN] Chine [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] contour [Termes IGN] déchet [Termes IGN] fusion de données [Termes IGN] image à très haute résolution [Termes IGN] Inde [Termes IGN] Mexique [Termes IGN] urbanisation Résumé : (auteur) The urbanization worldwide leads to the rapid increase of solid waste, posing a threat to environment and people’s wellbeing. However, it is challenging to detect solid waste sites with high accuracy due to complex landscape, and very few studies considered solid waste mapping across multi-cities and in large areas. To tackle this issue, this study proposes a novel deep learning model for solid waste mapping from very high resolution remote sensing imagery. By integrating a multi-scale dilated convolutional neural network (CNN) and a Swin-Transformer, both local and global features are aggregated. Experiments in China, India and Mexico indicate that the proposed model achieves high performance with an average accuracy of 90.62%. The novelty lies in the fusion of CNN and Transformer for solid waste mapping in multi-cities without the need for pixel-wise labelled data. Future work would consider more sophisticated methods such as semantic segmentation for fine-grained solid waste classification. Numéro de notice : A2023-109 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2022.2164361 Date de publication en ligne : 04/01/2023 En ligne : https://doi.org/10.1080/10106049.2022.2164361 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102407 [article]

A GIS and hybrid simulation aided environmental impact assessment of city-scale demolition waste management / Zhikun Ding in Sustainable Cities and Society, vol 86 (November 2022)  

Public [article]  inSustainable Cities and Society > vol 86 (November 2022) . - n° 104108 Titre : A GIS and hybrid simulation aided environmental impact assessment of city-scale demolition waste management Type de document : Article/Communication Auteurs : Zhikun Ding, Auteur ; Xinping Wen, Auteur ; Xiaoyan Cao, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 104108 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] aide à la décision [Termes IGN] déchet [Termes IGN] impact sur l'environnement [Termes IGN] modèle empirique [Termes IGN] modèle orienté agent [Termes IGN] planification urbaine [Termes IGN] Shenzhen [Termes IGN] simulation dynamique [Termes IGN] système d'information géographique [Termes IGN] ville intelligente Résumé : (auteur) A considerable amount of demolition waste (DW) generated by urbanization and urban renewal has brought significant threats to the environment. However, there is a serious lack of environmental impact assessment towards city-scale demolition waste management (DWM), particularly from the systematical and dynamical perspective. Traditionally the assessment has been conducted from a static perspective. The purpose of this paper is to comprehensively evaluate the environmental impact of city-scale DWM from a complex system perspective. A novel evaluation model was developed by innovatively integrating the geographic information system (GIS) and system hybrid simulation consisting of system dynamics (SD), agent-based modeling (ABM) and discrete event simulation (DES). The proposed model was verified. Based on an empirical analysis of Shenzhen, China, it is found that the environmental impact of city-scale DWM is mainly concentrated in the central and northeastern regions of Shenzhen, demonstrating spatial heterogeneity and regional aggregation. Furthermore, the results reveal that the model is robust and effective to assess environmental impact from four aspects, i.e., land occupation, water pollution, air pollution and energy consumption. The findings contribute to a better understanding of the status quo of city-scale DWM and accompanying environmental impacts, and coordinating various district governments to formulate effective DWM policies. Numéro de notice : A2022-817 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scs.2022.104108 Date de publication en ligne : 06/08/2022 En ligne : https://doi.org/10.1016/j.scs.2022.104108 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101983 [article]

Experimental precipitation reduction slows down litter decomposition but exhibits weak to no effect on soil organic carbon and nitrogen stocks in three Mediterranean forests of Southern France / Mathieu Santonja in Forests, vol 13 n° 9 (september 2022)  

Public [article]  inForests > vol 13 n° 9 (september 2022) . - n° 1485 Titre : Experimental precipitation reduction slows down litter decomposition but exhibits weak to no effect on soil organic carbon and nitrogen stocks in three Mediterranean forests of Southern France Type de document : Article/Communication Auteurs : Mathieu Santonja, Auteur ; Susana Pereira, Auteur ; Thierry Gauquelin, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 1485 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] azote [Termes IGN] changement climatique [Termes IGN] déchet organique [Termes IGN] écosystème forestier [Termes IGN] forêt méditerranéenne [Termes IGN] France (administrative) [Termes IGN] litière [Termes IGN] Pinus halepensis [Termes IGN] précipitation [Termes IGN] puits de carbone [Termes IGN] Quercus ilex [Termes IGN] Quercus pubescens [Vedettes matières IGN] Ecologie forestière Résumé : (auteur) Forest ecosystems are some of the largest carbon (C) reservoirs on earth. Pinus halepensis Mill., Quercus ilex L. and Quercus pubescens Willd. represent the dominant tree cover in the Mediterranean forests of southern France. However, their contributions to the French and global forest C and nitrogen (N) stocks are frequently overlooked and inaccurately quantified and little is known about to what extent the ongoing climate change can alter these stocks. We quantified the soil organic C (SOC) and N (SN) stocks in Mediterranean forests dominated by these tree species and evaluated to what extent an experimental precipitation reduction (about −30% yearly) affects these stocks and the litter decomposition efficiency. Litter mass losses were 55.7, 49.8 and 45.7% after 24 months of decomposition in Q. ilex, Q. pubescens and P. halepensis forests, respectively, and were 19% lower under drier climatic conditions. The SOC stocks were 14.0, 16.7 and 18.5 Mg ha−1 and the SN stocks were 0.70, 0.93 and 0.88 Mg ha−1 in Q. ilex, Q. pubescens and P. halepensis forests, respectively. The shallowness and stoniness of these Mediterranean forests could explain these limited stocks. By distinguishing the organic from the organo–mineral layer, we showed 74% less SOC in the organic layer of the P. halepensis forest under drier conditions, while no difference was detected in the organo–mineral layer or in the two oak forests. This last finding deserves further investigation and points out the necessity to distinguish the organic from the organo–mineral layer to detect the first impacts of climate change on SOC stocks. Numéro de notice : A2022-753 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.3390/f13091485 Date de publication en ligne : 14/09/2022 En ligne : https://doi.org/10.3390/f13091485 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101756 [article]

Identification of urban sectors prone to solid waste accumulation: A machine learning approach based on social indicators / Luis Izquierdo-Horna in Computers, Environment and Urban Systems, vol 96 (September 2022)  

Public [article]  inComputers, Environment and Urban Systems > vol 96 (September 2022) . - n° 101834 Titre : Identification of urban sectors prone to solid waste accumulation: A machine learning approach based on social indicators Type de document : Article/Communication Auteurs : Luis Izquierdo-Horna, Auteur ; Miker Damazo, Auteur ; Deyvis Yanayaco, Auteur Année de publication : 2022 Article en page(s) : n° 101834 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] apprentissage automatique [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] déchet [Termes IGN] densité de population [Termes IGN] données socio-économiques [Termes IGN] Pérou [Termes IGN] régression logistique [Termes IGN] zone urbaine Résumé : (auteur) In the last decades, the accumulation of municipal solid waste in urban areas has become a latent concern in our society due to its implications for the exposed population and the possible health and environmental issues it may cause. In this sense, this research study contributes to the timely identification of these sectors according to the anthropogenic characteristics of their residents as dictated by 10 social indicators (i.e., age, education, income, among others) sorted into three assessment categories (sociodemographic, sociocultural, and socioeconomic). Then, the data collected was processed and analyzed using two machine learning algorithms (random forest (RF) and logistic regression (LR)). The primary information that fed the machine learning model was collected through field visits and local/national reports. For this research, the Puente Piedra and Chaclacayo districts, both located in the province of Lima, Peru, were selected as case studies. Results suggest that the most relevant social indicators that help identifying these sectors are monthly income, consumption patterns, age, and household population density. The experiments showed that the RF algorithm has the best performance, since it efficiently identified 63% of the possible solid waste accumulation zones. In addition, both models were capable of determining different classes (AUC – RF = 0.65, AUC – LR = 0.71). Finally, the proposed approach is applicable and reproducible in different sectors of the national Peruvian territory. Numéro de notice : A2022-512 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101834 Date de publication en ligne : 10/06/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101834 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101052 [article]

Plastic waste cleanup priorities to reduce marine pollution: A spatiotemporal analysis for Accra and Lagos with satellite data / Susmita Dasgupta in Science of the total environment, vol 839 (May 2022)  

Public [article]  inScience of the total environment > vol 839 (May 2022) . - n° 156319 Titre : Plastic waste cleanup priorities to reduce marine pollution: A spatiotemporal analysis for Accra and Lagos with satellite data Type de document : Article/Communication Auteurs : Susmita Dasgupta, Auteur ; Maria Sarraf, Auteur ; David M. Wheeler, Auteur Année de publication : 2022 Article en page(s) : n° 156319 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse spatio-temporelle [Termes IGN] déchet [Termes IGN] distribution spatiale [Termes IGN] enquête [Termes IGN] géoréférencement [Termes IGN] Ghana [Termes IGN] image à haute résolution [Termes IGN] Lagos [Termes IGN] littoral [Termes IGN] matière plastique [Termes IGN] méthode du maximum de vraisemblance (estimation) [Termes IGN] pollution des mers [Termes IGN] variation saisonnière Résumé : (auteur) Plastic waste, with an estimated lifetime of centuries, accounts for the major share of marine litter. Each year, thousands of fish, sea birds, sea turtles, and other marine species are killed by ingesting or becoming entangled with plastic debris. Reducing marine plastic pollution is particularly challenging for developing countries owing to the wide dispersal of plastic waste disposal and scarce public cleanup resources. To costeffectively reduce marine pollution, resources should target “hotspot” areas, where large volumes of plastic litter have a high likelihood of ending up in the ocean. Using new public information, this study develops a hotspot targeting strategy for Accra and Lagos, which are major sources of marine plastic pollution in West Africa. The same global information sources can support hotspot analyses for many other coastal cities that generate marine plastic waste. The methodology combines georeferenced household survey data on plastic use, measures of seasonal variation in marine plastic pollution from satellite imagery, and a model of plastic waste transport to the ocean that uses information on topography, seasonal rainfall, drainage to rivers, and river transport to the ocean. For cleanup, the results for West Africa assign the highest locational priority to areas with heavy plastic-waste disposal along river channels or in steeply sloped locations with high rainfall runoff potential near rivers. They assign the highest temporal priority to just before the onset of the first-semester rainy season, when runoff from the first rains transports large volumes of plastic waste that have accumulated during the dry season. Numéro de notice : A2022-471 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.scitotenv.2022.156319 Date de publication en ligne : 28/05/2022 En ligne : https://doi.org/10.1016/j.scitotenv.2022.156319 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100816 [article]

Détection des micro et macroplastiques à partir de mesures spectrales / Martin Cubaud (2022) 

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Urban construction waste with VHR remote sensing using multi-feature analysis and a hierarchical segmentation method / Qiang Chen in Remote sensing, vol 13 n° 1 (January-1 2021)  

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Influence of forest management activities on soil organic carbon stocks: A knowledge synthesis / Mathias Mayer in Forest ecology and management, Vol 466 (15 June 2020)  

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Impact of deadwood decomposition on soil organic carbon sequestration in Estonian and Polish forests / Ewa Blonska in Annals of Forest Science, Vol 76 n° 4 (December 2019)  

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Disaster debris estimation using high-resolution polarimetric stereo-SAR / Christian N. Koyama in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)  

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Utilisation de SIG pour l'étude de la diffusion spatiale des métaux lourds : cas de la décherge contrôlée de jebel Chakir (Tunisie) / Fethi Bouzayania in Géomatique expert, n° 110 (mai - juin 2016)

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Non-invasive forest litter characterization using full-wave inversion of microwave radar data / Frédéric André in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)  

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Carbon storage in biomass, litter, and soil of different plantations in a semiarid temperate region of northwest China / Yang Gao in Annals of Forest Science, vol 71 n° 4 (June 2014)  

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Le cycle des matières dans l'économie française / CGDD Commissariat Général au Développement Durable (2013) 

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Sélection de zones de stockage de marines par SIG et analyse multicritère / A. Aydi in Géomatique expert, n° 86 (01/05/2012)

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