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Suitability assessment of urban land use in Dalian, China using PNN and GIS / Ziqian Kang in Natural Hazards, vol 106 n° 1 (March 2021)
[article]
Titre : Suitability assessment of urban land use in Dalian, China using PNN and GIS Type de document : Article/Communication Auteurs : Ziqian Kang, Auteur ; Shuo Wang, Auteur ; Ling Xu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 913 - 936 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] aire naturelle (écologie)
[Termes IGN] analyse multicritère
[Termes IGN] bâtiment industriel
[Termes IGN] Chine
[Termes IGN] classificateur paramétrique
[Termes IGN] distribution spatiale
[Termes IGN] habitat urbain
[Termes IGN] processus de hiérarchisation analytique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] système d'information géographique
[Termes IGN] utilisation du sol
[Termes IGN] zone urbaineRésumé : (auteur) The suitability assessment of land use is crucial to avoid wasting land resources. However, the traditional methods with subjective weights are prone to reduce the reasonability and reliability of assessment. For filling this knowledge gap, the probability neural network (PNN) coupled with GIS was adopted to evaluate the land use suitability in this paper. According to the applications of the urban land resource, the land use was divided into three types (resident, industry and ecological reserve). Thus, the three different assessment criteria systems were built for the three land use types. The result of residential land use indicated that the most suitable, suitable and normal suitable residential land were 401, 272 and 12,406 km2 and mainly located in Changhai, Lvshun and Pulandian accordingly. The most suitable land for industry was in Ganjingzi, Jinzhou and Wafangdian and accounted for 22% of the total area. While the most suitable land for ecological reserve was in Pulandian and Zhuanghe with the area of 1967 km2. The results indicated that the south of Dalian was suitable for the residential land use, north of Dalian was suitable for the ecological land use and the central was suitable for industrial land use. The results were coincided to the actual spatial distribution of land use. The proposed PNN coupled with GIS assessment method in suitability of land use is conducted to provide a more reasonable assessment result that can be used by managers and regulators. Numéro de notice : A2021-419 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-020-04500-z Date de publication en ligne : 04/01/2021 En ligne : https://doi.org/10.1007/s11069-020-04500-z Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97769
in Natural Hazards > vol 106 n° 1 (March 2021) . - pp 913 - 936[article]Toward a yearly country-scale CORINE land-cover map without using images: A map translation approach / Luc Baudoux in Remote sensing, Vol 13 n° 6 (March 2021)
[article]
Titre : Toward a yearly country-scale CORINE land-cover map without using images: A map translation approach Type de document : Article/Communication Auteurs : Luc Baudoux , Auteur ; Jordi Inglada, Auteur ; Clément Mallet , Auteur Année de publication : 2021 Projets : AI4GEO / , MAESTRIA / Mallet, Clément Article en page(s) : n° 1060 - 32 p. Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] appariement sémantique
[Termes IGN] apprentissage dirigé
[Termes IGN] carte d'occupation du sol
[Termes IGN] changement d'occupation du sol
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] Corine Land Cover
[Termes IGN] détection de changement
[Termes IGN] image à haute résolution
[Termes IGN] inférence
[Termes IGN] mise à jour automatique
[Termes IGN] mise à jour de base de donnéesRésumé : (Auteur) CORINE Land-Cover (CLC) and its by-products are considered as a reference baseline for land-cover mapping over Europe and subsequent applications. CLC is currently tediously produced each six years from both the visual interpretation and the automatic analysis of a large amount of remote sensing images. Observing that various European countries regularly produce in parallel their own land-cover country-scaled maps with their own specifications, we propose to directly infer CORINE Land-Cover from an existing map, therefore steadily decreasing the updating time-frame. No additional remote sensing image is required. In this paper, we focus more specifically on translating a country-scale remote sensed map, OSO (France), into CORINE Land Cover, in a supervised way. OSO and CLC not only differ in nomenclature but also in spatial resolution. We jointly harmonize both dimensions using a contextual and asymmetrical Convolution Neural Network with positional encoding. We show for various use cases that our method achieves a superior performance than the traditional semantic-based translation approach, achieving an 81% accuracy over all of France, close to the targeted 85% accuracy of CLC. Numéro de notice : A2021-244 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Autre URL associée : vers HAL Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13061060 Date de publication en ligne : 11/03/2021 En ligne : https://dx.doi.org/10.3390/rs13061060 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97311
in Remote sensing > Vol 13 n° 6 (March 2021) . - n° 1060 - 32 p.[article]Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB / Mahya Norallahi in Natural Hazards, vol 106 n° 1 (March 2021)
[article]
Titre : Urban flood hazard mapping using machine learning models: GARP, RF, MaxEnt and NB Type de document : Article/Communication Auteurs : Mahya Norallahi, Auteur ; Hesam Seyed Kaboli, Auteur Année de publication : 2021 Article en page(s) : pp119 - 137 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] algorithme génétique
[Termes IGN] apprentissage automatique
[Termes IGN] cartographie des risques
[Termes IGN] classification bayesienne
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] entropie maximale
[Termes IGN] inondation
[Termes IGN] Iran
[Termes IGN] zone urbaineRésumé : (auteur) Rapid urban development, increasing impermeable surfaces, poor drainage system and changes in extreme precipitations are the most important factors that nowadays lead to increased urban flooding and it has become an urban problem. Urban flood mapping and its use in making an urban development plan can reduce flood damages and losses. Constantly producing urban flood hazard maps using models that rely on the availability of detailed hydraulic-hydrological data is a major challenge especially in developing countries. In this study, urban flood hazard map was produced with limited data using three machine learning models: Genetic Algorithm Rule-Set Production, Maximum Entropy (MaxEnt), Random Forest (RF) and Naïve Bayes for Kermanshah city, Iran. The flood hazard predicting factors used in modeling were: slope, land use, precipitation, distance to river, distance to channel, curve number (CN) and elevation. Flood inventory map was produced based on available reports and field surveys, that 117 flooded points and 163 non-flooded points were identified. Models performance was evaluated based on area under the receiver-operator characteristic curve (AUC-ROC), Kappa statistic and hits and miss analysis. The results show that RF model (AUC-ROC = 99.5%, Kappa = 98%, Accuracy = 90%, Success ratio = 99%, Threat score = 90% and Heidke skill score = 98%) performed better than other models. The results also showed that distance to canal, land use and CN have shown more contribution among others for modeling the flood and precipitation had the least effect among other factors. The findings show that machine learning methods can be a good alternative to distributed models to predict urban flood-prone areas where there are lack of detailed hydraulic and hydrological data. Numéro de notice : A2021-418 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1007/s11069-020-04453-3 Date de publication en ligne : 04/01/2021 En ligne : https://doi.org/10.1007/s11069-020-04453-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97768
in Natural Hazards > vol 106 n° 1 (March 2021) . - pp119 - 137[article]Using geometric constraints to improve performance of image classifiers for automatic segmentation of traffic signs / Roholah Yazdan in Geomatica, vol 75 n° 1 (Mars 2021)
[article]
Titre : Using geometric constraints to improve performance of image classifiers for automatic segmentation of traffic signs Type de document : Article/Communication Auteurs : Roholah Yazdan, Auteur ; Masood Varshosaz, Auteur ; Saied Pirasteh, Auteur ; Fabio Remondino, Auteur Année de publication : 2021 Article en page(s) : pp 28 - 50 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] contrainte géométrique
[Termes IGN] espace colorimétrique
[Termes IGN] programmation par contraintes
[Termes IGN] signalisation routièreRésumé : (auteur) Automatic detection and recognition of traffic signs from images is an important topic in many applications. At first, we segmented the images using a classification algorithm to delineate the areas where the signs are more likely to be found. In this regard, shadows, objects having similar colours, and extreme illumination changes can significantly affect the segmentation results. We propose a new shape-based algorithm to improve the accuracy of the segmentation. The algorithm works by incorporating the sign geometry to filter out the wrong pixels from the classification results. We performed several tests to compare the performance of our algorithm against those obtained by popular techniques such as Support Vector Machine (SVM), K-Means, and K-Nearest Neighbours. In these tests, to overcome the unwanted illumination effects, the images are transformed into colour spaces Hue, Saturation, and Intensity, YUV, normalized red green blue, and Gaussian. Among the traditional techniques used in this study, the best results were obtained with SVM applied to the images transformed into the Gaussian colour space. The comparison results also suggested that by adding the geometric constraints proposed in this study, the quality of sign image segmentation is improved by 10%–25%. We also comparted the SVM classifier enhanced by incorporating the geometry of signs with a U-Shaped deep learning algorithm. Results suggested the performance of both techniques is very close. Perhaps the deep learning results could be improved if a more comprehensive data set is provided. Numéro de notice : A2021-608 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1139/geomat-2020-0010 En ligne : https://doi.org/10.1139/geomat-2020-0010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98322
in Geomatica > vol 75 n° 1 (Mars 2021) . - pp 28 - 50[article]Assessing spatial-temporal evolution processes and driving forces of karst rocky desertification / Fei Chen in Geocarto international, vol 36 n° 3 ([15/02/2021])
[article]
Titre : Assessing spatial-temporal evolution processes and driving forces of karst rocky desertification Type de document : Article/Communication Auteurs : Fei Chen, Auteur ; Shijie Wang, Auteur ; Xiaoyong Bai, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 262 - 280 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse spatio-temporelle
[Termes IGN] carte d'utilisation du sol
[Termes IGN] Chine
[Termes IGN] classification et arbre de régression
[Termes IGN] désertification
[Termes IGN] données spatiotemporelles
[Termes IGN] image Landsat-OLI
[Termes IGN] image Landsat-TM
[Termes IGN] karst
[Termes IGN] lithologieRésumé : (auteur) Karst Rocky Desertification (KRD) has become the most serious ecological disaster in Southwest China. We used the data of Thematic Mapper (TM) images from 1990, 1995, 2000, 2004, and 2011 and the 2016 Operational Land Imager (OLI) image. These sensing images were pre-processed by removing non-karst areas based on lithology and cutting away the land types impossibly generating KRD from land use maps. Then, we used a Classification And Regression Tree (CART) to classify the KRD. We want to improve the interpretation accuracy of KRD through the above steps. The results were as follows: (1) The KRD experiences the evolution process of ‘first deterioration and then improvement’. The rate is −4.94 km2.a−1 over a period of 1990 to 2004, and the rate is 36.48 km2.a−1 from 2004 to 2016; (2) The most influential factors causing KRD formation are the lithology and the resident population density, with contribution rates of 30.17% and 25.86%, respectively. Numéro de notice : A2021-140 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1595175 Date de publication en ligne : 18/07/2019 En ligne : https://doi.org/10.1080/10106049.2019.1595175 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97036
in Geocarto international > vol 36 n° 3 [15/02/2021] . - pp 262 - 280[article]An ecological approach to climate change-informed tree species selection for reforestation / William H. 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