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Comparative analysis of different CNN models for building segmentation from satellite and UAV images / Batuhan Sariturk in Photogrammetric Engineering & Remote Sensing, PERS, vol 89 n° 2 (February 2023)
[article]
Titre : Comparative analysis of different CNN models for building segmentation from satellite and UAV images Type de document : Article/Communication Auteurs : Batuhan Sariturk, Auteur ; Damla Kumbasar, Auteur ; Dursun Zafer Seker, Auteur Année de publication : 2023 Article en page(s) : pp 97 - 105 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] bati
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] image captée par drone
[Termes IGN] image satellite
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Building segmentation has numerous application areas such as urban planning and disaster management. In this study, 12 CNN models (U-Net, FPN, and LinkNet using EfficientNet-B5 backbone, U-Net, SegNet, FCN, and six Residual U-Net models) were generated and used for building segmentation. Inria Aerial Image Labeling Data Set was used to train models, and three data sets (Inria Aerial Image Labeling Data Set, Massachusetts Buildings Data Set, and Syedra Archaeological Site Data Set) were used to evaluate trained models. On the Inria test set, Residual-2 U-Net has the highest F1 and Intersection over Union (IoU) scores with 0.824 and 0.722, respectively. On the Syedra test set, LinkNet-EfficientNet-B5 has F1 and IoU scores of 0.336 and 0.246. On the Massachusetts test set, Residual-4 U-Net has F1 and IoU scores of 0.394 and 0.259. It has been observed that, for all sets, at least two of the top three models used residual connections. Therefore, for this study, residual connections are more successful than conventional convolutional layers. Numéro de notice : A2023-143 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.22-00084R2 Date de publication en ligne : 01/02/2023 En ligne : https://doi.org/10.14358/PERS.22-00084R2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102718
in Photogrammetric Engineering & Remote Sensing, PERS > vol 89 n° 2 (February 2023) . - pp 97 - 105[article]Identification of enclaves and exclaves by computation based on point-set topology / Xiaonan Wang in International journal of geographical information science IJGIS, vol 37 n° 2 (February 2023)
[article]
Titre : Identification of enclaves and exclaves by computation based on point-set topology Type de document : Article/Communication Auteurs : Xiaonan Wang, Auteur Année de publication : 2023 Article en page(s) : pp 307 - 338 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] frontière
[Termes IGN] géopolitique
[Termes IGN] intersection spatiale
[Termes IGN] partition de surface
[Termes IGN] polygone
[Termes IGN] relation topologique
[Termes IGN] territoire
[Termes IGN] topologieRésumé : (auteur) Enclaves and exclaves have special roles in geography and are of particular importance to fields such as (geo)politics and economy. However, enclaves and exclaves have not been defined with sufficient formality for automatic identification yet. To identify enclaves and exclaves more generally by computational means than current definitions existing in the literature, this article proposes expressive and generalized mathematical definitions of enclaves and exclaves based on point-set topology. A novel Boundary Extended 16-Intersection Model is developed in this article to identify enclaves, and 74 possible spatial configurations of enclaves are distinguished according to conditions of intersections for polygons in partitions and enclaves. The classic Dimensionally Extended 9-Intersection Model is employed to identify exclaves, and two possible spatial configurations of exclaves are distinguished according to conditions of intersections for polygons in partitions and exclaves. Applications of the proposed definitions are exemplified by the identification of enclaves and exclaves in prototypes and in the real world. Numéro de notice : A2023-102 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2120995 Date de publication en ligne : 21/09/2022 En ligne : https://doi.org/10.1080/13658816.2022.2120995 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102428
in International journal of geographical information science IJGIS > vol 37 n° 2 (February 2023) . - pp 307 - 338[article]Is the radial distance really a distance? An analysis of its properties and interest for the matching of polygon features / Yann Méneroux in International journal of geographical information science IJGIS, vol 37 n° 2 (February 2023)
[article]
Titre : Is the radial distance really a distance? An analysis of its properties and interest for the matching of polygon features Type de document : Article/Communication Auteurs : Yann Méneroux , Auteur ; Ibrahim Maidaneh Abdi , Auteur ; Arnaud Le Guilcher , Auteur ; Ana-Maria Olteanu-Raimond , Auteur Année de publication : 2023 Projets : 3-projet - voir note / Article en page(s) : pp 438 - 475 Note générale : bibliographie
This work was supported by the French National Mapping Agency: Institut National de l’Information Géographique et Forestière (IGN) and by the University of DjiboutiLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] abaque
[Termes IGN] algorithme de Douglas-Peucker
[Termes IGN] appariement de formes
[Termes IGN] bâtiment
[Termes IGN] BD Topo
[Termes IGN] distance
[Termes IGN] généralisation
[Termes IGN] géométrie analytique
[Termes IGN] modèle analytique
[Termes IGN] polygone
[Termes IGN] propagation d'erreur
[Termes IGN] transformation rapide de FourierRésumé : (auteur) In this paper, we examine the properties of the radial distance which has been used as a tool to compare the shape of simple surfacic objects. We give a rigorous definition of the radial distance and derive its theoretical properties, and in particular under which conditions it satisfies the distance properties. We show how the computation of the radial distance can be implemented in practice and made faster by the use of an analytical formula and a Fast Fourier Transform. Finally, we conduct experiments to measure how the radial distance is impacted by perturbation and generalization and we give abacuses and thresholds to deduce when buildings are likely to be homologous or non-homologous given their radial distance. Numéro de notice : A2023-074 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2123487 Date de publication en ligne : 23/09/2022 En ligne : https://doi.org/10.1080/13658816.2022.2123487 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101671
in International journal of geographical information science IJGIS > vol 37 n° 2 (February 2023) . - pp 438 - 475[article]Large-scale burn severity mapping in multispectral imagery using deep semantic segmentation models / Xikun Hu in ISPRS Journal of photogrammetry and remote sensing, vol 196 (February 2023)
[article]
Titre : Large-scale burn severity mapping in multispectral imagery using deep semantic segmentation models Type de document : Article/Communication Auteurs : Xikun Hu, Auteur ; Puzhao Zhang, Auteur ; Yifang Ban, Auteur Année de publication : 2023 Article en page(s) : pp 228 - 240 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] carte thématique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] dommage
[Termes IGN] image Landsat-ETM+
[Termes IGN] image Landsat-OLI
[Termes IGN] image Landsat-TM
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] incendie de forêt
[Termes IGN] jeu de données localisées
[Termes IGN] segmentation sémantique
[Termes IGN] surveillance forestière
[Termes IGN] zone sinistréeRésumé : (auteur) Nowadays Earth observation satellites provide forest fire authorities and resource managers with spatial and comprehensive information for fire stabilization and recovery. Burn severity mapping is typically performed by classifying bi-temporal indices (e.g., dNBR, and RdNBR) using thresholds derived from parametric models incorporating field-based measurements. Analysts are currently expending considerable manual effort using prior knowledge and visual inspection to determine burn severity thresholds. In this study, we aim to employ highly automated approaches to provide spatially explicit damage level estimates. We first reorganize a large-scale Landsat-based bi-temporal burn severity assessment dataset (Landsat-BSA) by visual data cleaning based on annotated MTBS data (approximately 1000 major fire events in the United States). Then we apply state-of-the-art deep learning (DL) based methods to map burn severity based on the Landsat-BSA dataset. Experimental results emphasize that multi-class semantic segmentation algorithms can approximate the threshold-based techniques used extensively for burn severity classification. UNet-like models outperform other region-based CNN and Transformer-based models and achieve accurate pixel-wise classification results. Combined with the online hard example mining algorithm to reduce class imbalance issue, Attention UNet achieves the highest mIoU (0.78) and the highest Kappa coefficient close to 0.90. The bi-temporal inputs with ancillary spectral indices work much better than the uni-temporal multispectral inputs. The restructured dataset will be publicly available and create opportunities for further advances in remote sensing and wildfire communities. Numéro de notice : A2023-122 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.12.026 Date de publication en ligne : 11/01/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.12.026 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102498
in ISPRS Journal of photogrammetry and remote sensing > vol 196 (February 2023) . - pp 228 - 240[article]Multi-agent reinforcement learning to unify order-matching and vehicle-repositioning in ride-hailing services / Mingyue Xu in International journal of geographical information science IJGIS, vol 37 n° 2 (February 2023)
[article]
Titre : Multi-agent reinforcement learning to unify order-matching and vehicle-repositioning in ride-hailing services Type de document : Article/Communication Auteurs : Mingyue Xu, Auteur ; Peng Yue, Auteur ; Fan Yu, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 380 - 402 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] appariement de données localisées
[Termes IGN] apprentissage profond
[Termes IGN] autopartage
[Termes IGN] Chine
[Termes IGN] distribution spatiale
[Termes IGN] interaction humain-espace
[Termes IGN] modèle de Markov
[Termes IGN] système d'information urbain
[Termes IGN] système multi-agents
[Termes IGN] taxi
[Termes IGN] transmission de données
[Termes IGN] zone d'activité économiqueRésumé : (auteur) The popularity of ride-hailing platforms has significantly improved travel efficiency by providing convenient and personalized transportation services. Designing an effective ride-hailing service generally needs to address two tasks: order matching that assigns orders to available vehicles and proactive vehicle repositioning that deploys idle vehicles to potentially high-demand regions. Recent studies have intensively utilized deep reinforcement learning to solve the two tasks by learning an optimal dispatching strategy. However, most of them generate actions for the two tasks independently, neglecting the interactions between the two tasks and the communications among multiple drivers. To this end, this paper provides an approach based on multi-agent deep reinforcement learning where the two tasks are modeled as a unified Markov decision process, and the colossal state space and competition among drivers are addressed. Additionally, a modifiable agent-specific state representation is proposed to facilitate knowledge transferring and improve computing efficiency. We evaluate our approach on a public taxi order dataset collected in Chengdu, China, where a variable number of simulated vehicles are tested. Experimental results show that our approach outperforms seven existing baselines, reducing passenger rejection rate, driver idle time and improving total driver income. Numéro de notice : A2023-058 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2022.2119477 Date de publication en ligne : 07/09/2022 En ligne : https://doi.org/10.1080/13658816.2022.2119477 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102396
in International journal of geographical information science IJGIS > vol 37 n° 2 (February 2023) . - pp 380 - 402[article]Where am I now? modelling disorientation in pan-scalar maps / Guillaume Touya in ISPRS International journal of geo-information, vol 12 n° 2 (February 2023)PermalinkAnalysis of cycling network evolution in OpenStreetMap through a data quality prism / Raphaël Bres (2023)PermalinkPermalinkCorrelation of road network structure and urban mobility intensity: An exploratory study using geo-tagged tweets / Li Geng in ISPRS International journal of geo-information, vol 12 n° 1 (January 2023)PermalinkPermalinkGeographically masking addresses to study COVID-19 clusters / Walid Houfaf-Khoufaf in Cartography and Geographic Information Science, vol inconnu (2023)PermalinkGeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates / Valerio Marsocci (2023)PermalinkGeospatial-based machine learning techniques for land use and land cover mapping using a high-resolution unmanned aerial vehicle image / Taposh Mollick in Remote Sensing Applications: Society and Environment, RSASE, vol 29 (January 2023)PermalinkA hexagon-based method for polygon generalization using morphological operators / Lu Wang in International journal of geographical information science IJGIS, vol 37 n° 1 (January 2023)PermalinkHGAT-VCA: Integrating high-order graph attention network with vector cellular automata for urban growth simulation / Xuefeng Guan in Computers, Environment and Urban Systems, vol 99 (January 2023)Permalink