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Urban land-use analysis using proximate sensing imagery: a survey / Zhinan Qiao in International journal of geographical information science IJGIS, vol 35 n° 11 (November 2021)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 35 n° 11 (November 2021) . - pp 2129 - 2148 Titre : Urban land-use analysis using proximate sensing imagery: a survey Type de document : Article/Communication Auteurs : Zhinan Qiao, Auteur ; Xiaohui Yuan, Auteur Année de publication : 2021 Article en page(s) : pp 2129 - 2148 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] données issues des réseaux sociaux [Termes IGN] données localisées des bénévoles [Termes IGN] image Streetview [Termes IGN] utilisation du sol [Termes IGN] zone urbaine Résumé : (auteur) Urban regions are complicated functional systems that are closely associated with and reshaped by human activities. The propagation of online geographic information-sharing platforms and mobile devices equipped with the Global Positioning System (GPS) greatly proliferates proximate sensing images taken near or on the ground at a close distance to urban targets. Studies leveraging proximate sensing images have demonstrated great potential to address the need for local data in the urban land-use analysis. This paper reviews and summarizes the state-of-the-art methods and publicly available data sets from proximate sensing to support land-use analysis. We identify several research problems in the perspective of examples to support the training of models and means of integrating diverse data sets. Our discussions highlight the challenges, strategies, and opportunities faced by the existing methods using proximate sensing images in urban land-use studies. Numéro de notice : A2021-759 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2021.1919682 date de publication en ligne : 03/05/2021 En ligne : https://doi.org/10.1080/13658816.2021.1919682 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98788 [article]

Unsupervised self-adaptive deep learning classification network based on the optic nerve microsaccade mechanism for unmanned aerial vehicle remote sensing image classification / Ming Cong in Geocarto international, vol 36 n° 18 (November 2021)  

Public [article]  inGeocarto international > vol 36 n° 18 (November 2021) . - pp 2065 - 2084 Titre : Unsupervised self-adaptive deep learning classification network based on the optic nerve microsaccade mechanism for unmanned aerial vehicle remote sensing image classification Type de document : Article/Communication Auteurs : Ming Cong, Auteur ; Zhiye Wang, Auteur ; Yiting Tao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2065 - 2084 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de groupement [Termes IGN] chromatopsie [Termes IGN] classification non dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] compréhension de l'image [Termes IGN] échantillonnage d'image [Termes IGN] filtrage numérique d'image [Termes IGN] image captée par drone [Termes IGN] vision [Termes IGN] vision par ordinateur Résumé : (auteur) Unmanned aerial vehicle remote sensing images need to be precisely and efficiently classified. However, complex ground scenes produced by ultra-high ground resolution, data uniqueness caused by multi-perspective observations, and need for manual labelling make it difficult for current popular deep learning networks to obtain reliable references from heterogeneous samples. To address these problems, this paper proposes an optic nerve microsaccade (ONMS) classification network, developed based on multiple dilated convolution. ONMS first applies a Laplacian of Gaussian filter to find typical features of ground objects and establishes class labels using adaptive clustering. Then, using an image pyramid, multi-scale image data are mapped to the class labels adaptively to generate homologous reliable samples. Finally, an end-to-end multi-scale neural network is applied for classification. Experimental results show that ONMS significantly reduces sample labelling costs while retaining high cognitive performance, classification accuracy, and noise resistance—indicating that it has significant application advantages. Numéro de notice : A2021-707 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2019.1687593 date de publication en ligne : 07/11/2019 En ligne : https://doi.org/10.1080/10106049.2019.1687593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98602 [article]

Landslide susceptibility prediction based on image semantic segmentation / Bowen Du in Computers & geosciences, vol 155 (October 2021)  

Public [article]  inComputers & geosciences > vol 155 (October 2021) . - n° 104860 Titre : Landslide susceptibility prediction based on image semantic segmentation Type de document : Article/Communication Auteurs : Bowen Du, Auteur ; Zirong Zhao, Auteur ; Xiao Hu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 104860 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] aléa [Termes IGN] apprentissage automatique [Termes IGN] Chine [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] effondrement de terrain [Termes IGN] image captée par drone [Termes IGN] modèle de simulation [Termes IGN] prévention des risques [Termes IGN] segmentation sémantique Résumé : (auteur) Numéro de notice : A2021-683 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2021.104860 date de publication en ligne : 16/06/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104860 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98408 [article]

Early detection of pine wilt disease using deep learning algorithms and UAV-based multispectral imagery / Run Yu in Forest ecology and management, vol 497 (1 October 2021)  

Public [article]  inForest ecology and management > vol 497 (1 October 2021) . - n° 119493 Titre : Early detection of pine wilt disease using deep learning algorithms and UAV-based multispectral imagery Type de document : Article/Communication Auteurs : Run Yu, Auteur ; Youqing Luo, Auteur ; Quan Zhou, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119493 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] Chine [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] dépérissement [Termes IGN] image captée par drone [Termes IGN] image multibande [Termes IGN] maladie phytosanitaire [Termes IGN] milieu tropical [Termes IGN] peuplement mélangé [Termes IGN] Pinus (genre) [Termes IGN] Pinus massoniana [Termes IGN] réflectance spectrale [Termes IGN] Ulmus (genre) Résumé : (auteur) Pine wilt disease (PWD) is a global devastating threat to forest ecosystems. Therefore, a feasible and effective approach to precisely monitor PWD infection is indispensable, especially at the early stages. However, a precise definition of “early stage” and a rapid and high-efficiency method to detect PWD infection have not been well established. In this study, we systematically divided the PWD infection into green, early, middle, and late stages based on the needle color, the resin secretion, and whether the pine wood nematode (PWN) was carried. Simultaneously, an unmanned aerial vehicle (UAV) equipped with multispectral cameras was used to obtain images. Two target detection algorithms (Faster R-CNN and YOLOv4) and two traditional machine learning algorithms based on feature extraction (random forest and support vector machine) were employed to realize the recognition of infected pine trees. Moreover, we took into consideration of the influence of green broad-leaved trees on the identification of pine trees at the early stage of PWD infection. We obtained the following results: (1) the accuracy of Faster R-CNN (60.98–66.7%) was higher than that of YOLOv4 (57.07–63.55%), but YOLOv4 outperformed in terms of model size, processing speed, training time, and testing time; (2) although the traditional machine learning models had higher accuracy (73.28–79.64%), they were not able to directly identify the object from the images; (3) the accuracy of early detection of PWD infection showed an increase of 3.72–4.29%, from 42.36–44.59% to 46.08–48.88%, when broad-leaved trees were considered. In this study, the combination of UAV-based multispectral images and target detection algorithms allowed us to monitor the occurrence of PWD and obtain the distribution of infected trees at an early stage, which can provide technical support for the prevention and control of PWD. Numéro de notice : A2021-658 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.foreco.2021.119493 En ligne : https://doi.org/10.1016/j.foreco.2021.119493 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98395 [article]

Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data / Laura Elena Cué La Rosa in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 179 (September 2021) . - pp 35 - 49 Titre : Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data Type de document : Article/Communication Auteurs : Laura Elena Cué La Rosa, Auteur ; Camile Sothe, Auteur ; Raul Queiroz Feitosa, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 35 - 49 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Brésil [Termes IGN] carte de la végétation [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] densité de la végétation [Termes IGN] données d'apprentissage [Termes IGN] espèce végétale [Termes IGN] étiquetage sémantique [Termes IGN] forêt tropicale [Termes IGN] image captée par drone [Termes IGN] image hyperspectrale Résumé : (Auteur) This work proposes a multi-task fully convolutional architecture for tree species mapping in dense forests from sparse and scarce polygon-level annotations using hyperspectral UAV-borne data. Our model implements a partial loss function that enables dense tree semantic labeling outcomes from non-dense training samples, and a distance regression complementary task that enforces tree crown boundary constraints and substantially improves the model performance. Our multi-task architecture uses a shared backbone network that learns common representations for both tasks and two task-specific decoders, one for the semantic segmentation output and one for the distance map regression. We report that introducing the complementary task boosts the semantic segmentation performance compared to the single-task counterpart in up to 11% reaching an average user’s accuracy of 88.63% and an average producer’s accuracy of 88.59%, achieving state-of-art performance for tree species classification in tropical forests. Numéro de notice : A2021-575 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.07.001 date de publication en ligne : 28/07/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.07.001 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98175 [article]

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