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Accuracy of vacant housing detection models: An empirical evaluation using municipal and national census datasets / Kanta Sayuda in Transactions in GIS, vol 26 n° 7 (November 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 7 (November 2022) . - pp 3003 - 3027 Titre : Accuracy of vacant housing detection models: An empirical evaluation using municipal and national census datasets Type de document : Article/Communication Auteurs : Kanta Sayuda, Auteur ; Euijung Hong, Auteur ; Yuki Akiyama, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 3003 - 3027 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] apprentissage automatique [Termes IGN] distribution spatiale [Termes IGN] Extreme Gradient Machine [Termes IGN] géocodage [Termes IGN] immobilier (secteur) [Termes IGN] Japon [Termes IGN] logement Résumé : (auteur) In Japan, the rise in vacant housing has created the need to develop quick, effective, and inexpensive methods to detect the spatial distribution of vacant housing at the municipal level. However, due to incomplete and inaccessible data, the change in the accuracy of the vacant housing detection model must be evaluated while accounting for the limited data. Therefore, this study compares the performance of vacant housing detection models for different data combinations (Basic Resident Register; building registration, water usage, and national census) by considering Wakayama City, Japan, as the case study setting. Three main findings emerged: (1) the contribution of the data to the accuracy varies with the combination of datasets and metrics; (2) even if specific municipal data are unavailable, it is possible to acquire a similar accuracy by combining other data; and (3) the missing value contributes to the vacant housing detection rather than the feature value itself. Numéro de notice : A2022-887 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12992 Date de publication en ligne : 31/10/2022 En ligne : https://doi.org/10.1111/tgis.12992 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102217 [article]

Topographic descriptors on the early Dutch charts of the antipodes / Jan Tent in International journal of cartography, vol 8 n° 3 (November 2022)  

Public [article]  inInternational journal of cartography > vol 8 n° 3 (November 2022) . - pp 272 - 290 Titre : Topographic descriptors on the early Dutch charts of the antipodes Type de document : Article/Communication Auteurs : Jan Tent, Auteur Année de publication : 2022 Article en page(s) : pp 272 - 290 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Toponymie [Termes IGN] Australie [Termes IGN] carte ancienne [Termes IGN] descripteur [Termes IGN] explorateur [Termes IGN] littoral [Termes IGN] néerlandais (langue) [Termes IGN] nomenclature [Termes IGN] Nouvelle-Zélande [Termes IGN] Papouasie-Nouvelle-Guinée [Termes IGN] toponyme Résumé : (auteur) The early Dutch charts of coastal Australia, New Zealand and New Guinea are peppered not only with toponyms but also with topographic descriptors. The latter were intended as navigational aids and warnings for future navigators. Naming or describing a geographic feature is a method of distinguishing it from the surrounding topography. At times some topographic descriptors have been considered or interpreted as toponyms. This article explores whether there are any means of determining the difference between the two, and what may have been initially intended by the explorers who entered them on their manuscript charts. Reasons for the relevance of making such a distinction are also considered. Numéro de notice : A2022-746 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/TOPONYMIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/23729333.2020.1859937 Date de publication en ligne : 11/02/2021 En ligne : https://doi.org/10.1080/23729333.2020.1859937 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101731 [article]

Developing a GIS-based rough fuzzy set granulation model to handle spatial uncertainty for hydrocarbon structure classification, case study: Fars domain, Iran / Sahand Seraj in Geo-spatial Information Science, vol 25 n° 3 (October 2022)  

Public [article]  inGeo-spatial Information Science > vol 25 n° 3 (October 2022) . - pp 399 - 41 Titre : Developing a GIS-based rough fuzzy set granulation model to handle spatial uncertainty for hydrocarbon structure classification, case study: Fars domain, Iran Type de document : Article/Communication Auteurs : Sahand Seraj, Auteur ; Mahmoud Reza Delavar, Auteur Année de publication : 2022 Article en page(s) : pp 399 - 41 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] cartographie géologique [Termes IGN] classification floue [Termes IGN] entropie de Shannon [Termes IGN] forage [Termes IGN] granulométrie (pétrologie) [Termes IGN] hydrocarbure [Termes IGN] incertitude géométrique [Termes IGN] Iran [Termes IGN] prospection minérale [Termes IGN] sous ensemble flou [Termes IGN] système d'information géographique Résumé : (auteur) It is well agreed that geologic risk occurs during hydrocarbon exploration because diverse uncertainties accompany the entire hydrocarbon system parameters such as the source rock, reservoir rock, trap and seal rock. In order to overcome such attributes with uncertainties, a number of soft computing methods are used. Information granules could be provided by the Rough Fuzzy Set Granulation (RFSG) with a thorough quality evaluation. This is capable of attribute reduction that has been claimed to be essential in investigating the hydrocarbon systems. This paper is an endeavor to recommend a Geospatial Information System (GIS)-based model with the aim of categorizing the hydrocarbon structures map consistent with the uncertainty range concepts of geologic risk in the rough fuzzy sets and granular computing. The model used the RFSG for the attribute reduction by a Decision Logic language (DL-language). The RFSG was employed in order to classify hydrocarbon structures according to geological risk and extract the fuzzy rules with a predefined range of uncertainty. In order to assess the precisions of the fuzzy decisions on the hydrocarbon structure classification, the fuzzy entropy and fuzzy cross-entropy are applied. The proposed RFSG model applied for 62 structures as the training data, average fuzzy entropy has been calculated as 0.85, whereas the average fuzzy cross-entropy has been calculated 0.18. As it can be discerned, just seven structures had cross-entropies greater than 0.1, while three structures were larger than 0.3. It is implied that the precision of the proposed model is about 89%. The results yielded two reductions for the condition attributes and 11 fuzzy rules being filtered by the granular computing values. Numéro de notice : A2022-724 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/10095020.2021.2020600 Date de publication en ligne : 03/02/2022 En ligne : https://doi.org/10.1080/10095020.2021.2020600 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101667 [article]

The fractional vegetation cover (FVC) and associated driving factors of modeling in mining areas / Jun Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 10 (October 2022)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 10 (October 2022) . - pp 665 - 671 Titre : The fractional vegetation cover (FVC) and associated driving factors of modeling in mining areas Type de document : Article/Communication Auteurs : Jun Li, Auteur ; Tianyu Guo, Auteur ; Chengye Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 665 - 671 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] Chine [Termes IGN] couvert végétal [Termes IGN] Google Earth Engine [Termes IGN] hétérogénéité spatiale [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-OLI [Termes IGN] industrie minière [Termes IGN] mine [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] régression géographiquement pondérée [Termes IGN] régression multiple [Termes IGN] réseau neuronal artificiel Résumé : (auteur) To determine the fractional vegetation cover (FVC) and associated driving factors of modeling in mining areas, six types of data were used as driving factors and three methods —multi-linear regression (MLR), geographically weighted regression (GWR), and geographically weighted artificial neural network (GWANN)— were adopted in the modeling. The experiments, conducted in Shengli mining areas located in Xilinhot city, China, show that the MLR model without consideration of spatial heterogeneity and spatial non-stationarity performs the worst and that the GWR model presents obvious location differences, since it predefines a linear relationship which is unable to describe FVC for some locations. The GWANN model, improving on these defects, is the most suitable model for the FVC driving process in mining areas; it outperforms the other two models, with root-mean-square error (RMSE) and mean absolute percentage error (MAPE) reaching 0.16 and 0.20. It has improvements of approximately 24% in RMSE and 33% in MAPE compared to the MLR model, and those values grow to 59% and 71% when compared with the GWR model. Numéro de notice : A2022-813 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00070R3 Date de publication en ligne : 01/10/2022 En ligne : https://doi.org/10.14358/PERS.21-00070R3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101973 [article]

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Point-of-interest detection from Weibo data for map updating / Xue Yang in Transactions in GIS, vol 26 n° 6 (September 2022)  

Public [article]  inTransactions in GIS > vol 26 n° 6 (September 2022) . - pp 2716 - 2738 Titre : Point-of-interest detection from Weibo data for map updating Type de document : Article/Communication Auteurs : Xue Yang, Auteur ; Jie Gao, Auteur ; Xiaoyun Zheng, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 2716 - 2738 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] commerce de détail [Termes IGN] détection automatique [Termes IGN] détection de changement [Termes IGN] données issues des réseaux sociaux [Termes IGN] géocodage [Termes IGN] inférence [Termes IGN] information sémantique [Termes IGN] mise à jour cartographique [Termes IGN] point d'intérêt [Termes IGN] Wuhan (Chine) Résumé : (auteur) Points-of-interest (POIs) geographic information system data are increasingly important for supporting map generation and navigation services, although updating their semantic and location information still largely depends on manual labor. In this study, we propose a novel method to automatically detect the changes in POIs from Chinese text and check-in position data provided by the Chinese social media platform, Weibo. The proposed method includes three steps: (1) POI name recognition; (2) location confirmation; (3) and change detection. First, we propose recognizing a POI's name from Weibo text using the improved conditional random field algorithm. Then, we detect the location of each named POI by integrating the text address with the check-in position. The changes in the detected POIs are recognized by extracting the status words from Weibo text and a three-level status word database. To verify the effectiveness of the proposed method, we examine Wuhan as a case and detect the changes in the commercial POI using real-world Weibo data collected from January to September 2020. Based on the validation of three common map platforms, the data provided and the manual field investigation of 55 random samples, the identification accuracies for newly added POIs, the unchanged POIs, and expired POIs are approximately 100, 95.8, and 91.7%, respectively. Numéro de notice : A2022-734 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12982 Date de publication en ligne : 04/09/2022 En ligne : https://doi.org/10.1111/tgis.12982 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101701 [article]

"Process toponymy": A GIS-based community-engaged approach to indigenous dynamic place naming systems and vernacular cartography / Nadezhda Mamontova in Cartographica, vol 57 n° 3 (September)  

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Detection of potential gold mineralization areas using MF-fuzzy approach on multispectral data / Tohid Nouri in Geocarto international, Vol 37 n° 17 ([20/08/2022])  

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Advancements in underground mine surveys by using SLAM-enabled handheld laser scanners / Artu Ellmann in Survey review, vol 54 n° 385 (July 2022)  

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Polyline simplification based on the artificial neural network with constraints of generalization knowledge / Jiawei Du in Cartography and Geographic Information Science, Vol 49 n° 4 (July 2022)  

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Risk assessment and prediction of forest health for effective geo-environmental planning and monitoring of mining affected forest area in hilltop region / Narayan Kayet in Geocarto international, vol 37 n° 11 ([15/06/2022])  

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An informal road detection neural network for societal impact in developing countries / Inger Fabris-Rotelli in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-4-2022 (2022 edition)  

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Dater les documents cartographiques / Jean-Luc Arnaud in e-Perimetron, vol 17 n° 1 (avril 2022)  

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Detecting individuals' spatial familiarity with urban environments using eye movement data / Hua Liao in Computers, Environment and Urban Systems, vol 93 (April 2022)  

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Enriching the metadata of map images: a deep learning approach with GIS-based data augmentation / Yingjie Hu in International journal of geographical information science IJGIS, vol 36 n° 4 (April 2022)  

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Graph neural network based model for multi-behavior session-based recommendation / Bo Yu in Geoinformatica [en ligne], vol 26 n° 2 (April 2022)  

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