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Flood depth mapping in street photos with image processing and deep neural networks / Bahareh Alizadeh Kharazi in Computers, Environment and Urban Systems, vol 88 (July 2021)  

Public [article]  inComputers, Environment and Urban Systems > vol 88 (July 2021) . - n° 101628 Titre : Flood depth mapping in street photos with image processing and deep neural networks Type de document : Article/Communication Auteurs : Bahareh Alizadeh Kharazi, Auteur ; Amir H. Behzadan, Auteur Année de publication : 2021 Article en page(s) : n° 101628 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Canada [Termes descripteurs IGN] centre urbain [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] crue [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] Etats-Unis [Termes descripteurs IGN] image Streetview [Termes descripteurs IGN] inondation [Termes descripteurs IGN] profondeur [Termes descripteurs IGN] signalisation routière [Termes descripteurs IGN] système d'aide à la décision [Termes descripteurs IGN] traitement d'image [Termes descripteurs IGN] transformation de Hough [Termes descripteurs IGN] zone urbaine Résumé : (auteur) Many parts of the world experience severe episodes of flooding every year. In addition to the high cost of mitigation and damage to property, floods make roads impassable and hamper community evacuation, movement of goods and services, and rescue missions. Knowing the depth of floodwater is critical to the success of response and recovery operations that follow. However, flood mapping especially in urban areas using traditional methods such as remote sensing and digital elevation models (DEMs) yields large errors due to reshaped surface topography and microtopographic variations combined with vegetation bias. This paper presents a deep neural network approach to detect submerged stop signs in photos taken from flooded roads and intersections, coupled with Canny edge detection and probabilistic Hough transform to calculate pole length and estimate floodwater depth. Additionally, a tilt correction technique is implemented to address the problem of sideways tilt in visual analysis of submerged stop signs. An in-house dataset, named BluPix 2020.1 consisting of paired web-mined photos of submerged stop signs across 10 FEMA regions (for U.S. locations) and Canada is used to evaluate the models. Overall, pole length is estimated with an RMSE of 17.43 and 8.61 in. in pre- and post-flood photos, respectively, leading to a mean absolute error of 12.63 in. in floodwater depth estimation. Findings of this research are sought to equip jurisdictions, local governments, and citizens in flood-prone regions with a simple, reliable, and scalable solution that can provide (near-) real time estimation of floodwater depth in their surroundings. Numéro de notice : A2021-358 Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101628 date de publication en ligne : 01/04/2021 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101628 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97620 [article]

Deep learning in denoising of micro-computed tomography images of rock samples / Mikhail Sidorenko in Computers & geosciences, vol 151 (June 2021)  

Public [article]  inComputers & geosciences > vol 151 (June 2021) . - n° 104716 Titre : Deep learning in denoising of micro-computed tomography images of rock samples Type de document : Article/Communication Auteurs : Mikhail Sidorenko, Auteur ; Denis Orlov, Auteur ; Mohammad Ebadi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 104716 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] accentuation d'image [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] filtrage du bruit [Termes descripteurs IGN] filtre passe-bande [Termes descripteurs IGN] roche [Termes descripteurs IGN] tomographie Résumé : (auteur) Nowadays, the advantages of Digital Rock Physics (DRP) are well known and widely applied in comprehensive core analysis. It is also known that the quality of the 3D pore scale model drastically influences the results of rock properties simulation, which makes the preprocessing stage of DRP very important. In this work, we consider the application of Deep Convolutional Neural Networks (CNNs) for the preprocessing of CT images, specifically for denoising, in two setups - conventional fully-supervised learning and the self-supervised learning, when the only available data is the noisy images. To train CNNs in a supervised setup, we use images processed by a combination of bilateral and bandpass filters. We trained CNNs of the same architecture with different loss functions to find out how the choice of a loss function influences the model's performance. Some of the obtained CNNs yielded the highest quality in terms of full-reference and no-reference metrics and significant histogram effect (bimodal intensity distribution). Images denoised with these models were qualitatively and quantitatively better than the reference “ground truth” images used for training. We use the Deep Image Prior algorithm to train denoising models in a self-supervised setup. The obtained models are much better than ones obtained in fully-supervised setup, but are too slow, as they are optimization-based rather than feed-forward. Such an algorithm can be used in the dataset generation for feed-forward meta-models. These results could help to develop an AI-based instrument to build high-quality 3D segmented models of rocks for DRP applications. Numéro de notice : A2021-389 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.cageo.2021.104716 date de publication en ligne : 02/03/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104716 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97672 [article]

Automatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning / Malarvizhi Arulraj in Remote sensing of environment, vol 257 (May 2021)  

Public [article]  inRemote sensing of environment > vol 257 (May 2021) . - n° 112355 Titre : Automatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning Type de document : Article/Communication Auteurs : Malarvizhi Arulraj, Auteur ; Ana P. Baros, Auteur Année de publication : 2021 Article en page(s) : n° 112355 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] Appalaches [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] bande S [Termes descripteurs IGN] classification automatique [Termes descripteurs IGN] classification barycentrique [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] image GPM [Termes descripteurs IGN] orographie [Termes descripteurs IGN] précipitation Résumé : (auteur) Ground-clutter is a significant cause of missed-detection and underestimation of precipitation in complex terrain from space-based radars such as the Global Precipitation Measurement Mission (GPM) Dual-frequency Precipitation Radar (DPR). This research proposes an Artificial Intelligence (AI) framework consisting of a precipitation detection model (PDM) and a precipitation regime classification model (PCM) to improve orographic precipitation retrievals from GPM-DPR using machine learning. The PDM is a Random Forest Classifier using GPM Microwave Imager (GMI) calibrated brightness temperatures (Tbs) and low-level precipitation mixing ratios from the High-Resolution Rapid Refresh (HRRR) analysis as inputs. The PCM is a Convolutional Neural Network that predicts the precipitation regime class, defined independently based on quantitative features of ground-based radar reflectivity profiles, using GPM DPR Ku-band (Ku-PR) reflectivity profiles and GMI Tbs. The AI framework is demonstrated for warm-season precipitation in the Southern Appalachian Mountains over. Numéro de notice : A2021-279 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.rse.2021.112355 date de publication en ligne : 19/02/2021 En ligne : https://doi.org/10.1016/j.rse.2021.112355 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97372 [article]

Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation / Yansheng Li in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 175 (May 2021) . - pp 20 - 33 Titre : Learning deep semantic segmentation network under multiple weakly-supervised constraints for cross-domain remote sensing image semantic segmentation Type de document : Article/Communication Auteurs : Yansheng Li, Auteur ; Te Shi, Auteur ; Yongjun Zhang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 20 - 33 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] classification semi-dirigée [Termes descripteurs IGN] données d'apprentissage [Termes descripteurs IGN] programmation par contraintes [Termes descripteurs IGN] segmentation sémantique Résumé : (auteur) Due to its wide applications, remote sensing (RS) image semantic segmentation has attracted increasing research interest in recent years. Benefiting from its hierarchical abstract ability, the deep semantic segmentation network (DSSN) has achieved tremendous success on RS image semantic segmentation and has gradually become the mainstream technology. However, the superior performance of DSSN highly depends on two conditions: (I) massive quantities of labeled training data exist; (II) the testing data seriously resemble the training data. In actual RS applications, it is difficult to fully meet these conditions due to the RS sensor variation and the distinct landscape variation in different geographic locations. To make DSSN fit the actual RS scenario, this paper exploits the cross-domain RS image semantic segmentation task, which means that DSSN is trained on one labeled dataset (i.e., the source domain) but is tested on another varied dataset (i.e., the target domain). In this setting, the performance of DSSN is inevitably very limited due to the data shift between the source and target domains. To reduce the disadvantageous influence of data shift, this paper proposes a novel objective function with multiple weakly-supervised constraints to learn DSSN for cross-domain RS image semantic segmentation. Through carefully examining the characteristics of cross-domain RS image semantic segmentation, multiple weakly-supervised constraints include the weakly-supervised transfer invariant constraint (WTIC), weakly-supervised pseudo-label constraint (WPLC) and weakly-supervised rotation consistency constraint (WRCC). Specifically, DualGAN is recommended to conduct unsupervised style transfer between the source and target domains to carry out WTIC. To make full use of the merits of multiple constraints, this paper presents a dynamic optimization strategy that dynamically adjusts the constraint weights of the objective function during the training process. With full consideration of the characteristics of the cross-domain RS image semantic segmentation task, this paper gives two cross-domain RS image semantic segmentation settings: (I) variation in geographic location and (II) variation in both geographic location and imaging mode. Extensive experiments demonstrate that our proposed method remarkably outperforms the state-of-the-art methods under both of these settings. The collected datasets and evaluation benchmarks have been made publicly available online (https://github.com/te-shi/MUCSS). Numéro de notice : A2021-261 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.02.009 date de publication en ligne : 06/03/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.02.009 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97302 [article]

Learning from multimodal and multitemporal earth observation data for building damage mapping / Bruno Adriano in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 175 (May 2021) . - pp 132 - 143 Titre : Learning from multimodal and multitemporal earth observation data for building damage mapping Type de document : Article/Communication Auteurs : Bruno Adriano, Auteur ; Naoto Yokoya, Auteur ; Junshi Xia, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 132 - 143 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] cartographie des risques [Termes descripteurs IGN] catastrophe naturelle [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] cyclone [Termes descripteurs IGN] dommage [Termes descripteurs IGN] données multitemporelles [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image optique [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] observation de la Terre [Termes descripteurs IGN] segmentation sémantique [Termes descripteurs IGN] séisme [Termes descripteurs IGN] surveillance d'ouvrage [Termes descripteurs IGN] tsunami Résumé : (auteur) Earth observation (EO) technologies, such as optical imaging and synthetic aperture radar (SAR), provide excellent means to continuously monitor ever-growing urban environments. Notably, in the case of large-scale disasters (e.g., tsunamis and earthquakes), in which a response is highly time-critical, images from both data modalities can complement each other to accurately convey the full damage condition in the disaster aftermath. However, due to several factors, such as weather and satellite coverage, which data modality will be the first available for rapid disaster response efforts is often uncertain. Hence, novel methodologies that can utilize all accessible EO datasets are essential for disaster management. In this study, we developed a global multimodal and multitemporal dataset for building damage mapping. We included building damage characteristics from three disaster types, namely, earthquakes, tsunamis, and typhoons, and considered three building damage categories. The global dataset contains high-resolution (HR) optical imagery and high-to-moderate-resolution SAR data acquired before and after each disaster. Using this comprehensive dataset, we analyzed five data modality scenarios for damage mapping: single-mode (optical and SAR datasets), cross-modal (pre-disaster optical and post-disaster SAR datasets), and mode fusion scenarios. We defined a damage mapping framework for semantic segmentation of damaged buildings based on a deep convolutional neural network (CNN) algorithm. We also compared our approach to another state-of-the-art model for damage mapping. The results indicated that our dataset, together with a deep learning network, enabled acceptable predictions for all the data modality scenarios. We also found that the results from cross-modal mapping were comparable to the results obtained from a fusion sensor and optical mode analysis. Numéro de notice : A2021-272 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.02.016 date de publication en ligne : 17/03/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.02.016 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97343 [article]

Structure-aware completion of photogrammetric meshes in urban road environment / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)  

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Detecting archaeological features with airborne laser scanning in the alpine tundra of Sápmi, Northern Finland / Oula Seitsonen in Remote sensing, vol 13 n° 8 (15 April 2021)  

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Automatic atmospheric correction for shortwave hyperspectral remote sensing data using a time-dependent deep neural network / Jian Sun in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)  

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A BiLSTM-CNN model for predicting users’ next locations based on geotagged social media / Yi Bao in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)  

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A CNN approach to simultaneously count plants and detect plantation-rows from UAV imagery / Lucas Prado Osco in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)  

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Detecting ground deformation in the built environment using sparse satellite InSAR data with a convolutional neural network / Nantheera Anantrasirichai in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

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A geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection / Xi Wu in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)  

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Graph convolutional networks by architecture search for PolSAR image classification / Hongying Liu in Remote sensing, vol 13 n° 7 (April 2021)  

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Rotation-invariant feature learning in VHR optical remote sensing images via nested siamese structure with double center loss / Ruoqiao Jiang in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

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Scene classification of remotely sensed images via densely connected convolutional neural networks and an ensemble classifier / Qimin Cheng in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 4 (April 2021)  

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