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An analysis of the spatial and temporal distribution of large‐scale data production events in OpenStreetMap / A. Yair Grinberger in Transactions in GIS, Vol 25 n° 2 (April 2021)  

Public [article]  inTransactions in GIS > Vol 25 n° 2 (April 2021) . - pp 622 - 641 Titre : An analysis of the spatial and temporal distribution of large‐scale data production events in OpenStreetMap Type de document : Article/Communication Auteurs : A. Yair Grinberger, Auteur ; Moritz Schott, Auteur ; Martin Raifer, Auteur ; Alexander Zipf, Auteur Année de publication : 2021 Article en page(s) : pp 622 - 641 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] distribution spatiale [Termes IGN] données localisées des bénévoles [Termes IGN] données localisées libres [Termes IGN] données spatiotemporelles [Termes IGN] extraction de données [Termes IGN] grande échelle [Termes IGN] OpenStreetMap [Termes IGN] qualité des données Résumé : (Auteur) Organized mapping activities within OpenStreetMap frequently lead to the production of massive amounts of data over a short period. In this article we utilize a novel procedure to identify such large‐scale data production events in the history of OpenStreetMap and analyze their patterns. We find that events account for a significant share of OpenStreetMap data and that organizational practices have shifted over time towards local knowledge‐based events and well‐organized data imports. However, regions in the “Global South” remain dependent on remote mapping events, pointing to uneven geographies of representation. We also find that events are frequently followed by periods of increased activity, with the exact nature of effects depending on contextual elements such as previous events. These findings portray organized activities as a significant and unique component which requires consideration when using OpenStreetMap data and analyzing their quality. Numéro de notice : A2021-360 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12746 Date de publication en ligne : 19/03/2021 En ligne : https://doi.org/10.1111/tgis.12746 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97624 [article]

A convolutional neural network approach to predict non‐permissive environments from moderate‐resolution imagery / Seth Goodman in Transactions in GIS, Vol 25 n° 2 (April 2021)  

Public [article]  inTransactions in GIS > Vol 25 n° 2 (April 2021) . - pp 674 - 691 Titre : A convolutional neural network approach to predict non‐permissive environments from moderate‐resolution imagery Type de document : Article/Communication Auteurs : Seth Goodman, Auteur ; Ariel BenYishay, Auteur ; Daniel Runfola, Auteur Année de publication : 2021 Article en page(s) : pp 674 - 691 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] conflit [Termes IGN] image Landsat-8 [Termes IGN] implémentation (informatique) [Termes IGN] Nigéria [Termes IGN] prédiction [Termes IGN] réseau neuronal convolutif Résumé : (Auteur) Convolutional neural networks (CNNs) trained with satellite imagery have been successfully used to generate measures of development indicators, such as poverty, in developing nations. This article explores a CNN‐based approach leveraging Landsat 8 imagery to predict locations of conflict‐related deaths. Using Nigeria as a case study, we use the Armed Conflict Location & Event Data (ACLED) dataset to identify locations of conflict events that did or did not result in a death. Imagery for each location is used as an input to train a CNN to distinguish fatal from non‐fatal events. Using 2014 imagery, we are able to predict the result of conflict events in the following year (2015) with 80% accuracy. While our approach does not replace the need for causal studies into the drivers of conflict death, it provides a low‐cost solution to prediction that requires only publicly available imagery to implement. Findings suggest that the information contained in moderate‐resolution imagery can be used to predict the likelihood of a death due to conflict at a given location in Nigeria the following year, and that CNN‐based methods of estimating development‐related indicators may be effective in applications beyond those explored in the literature. Numéro de notice : A2021-361 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12661 Date de publication en ligne : 13/07/2020 En ligne : https://doi.org/10.1111/tgis.12661 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97625 [article]