Centre dedocumentation
scientifique

Automatic building footprint extraction from UAV images using neural networks / Zoran Kokeza in Geodetski vestnik, vol 64 n° 4 (December 2020 - February 2021)  

Public [article]  inGeodetski vestnik > vol 64 n° 4 (December 2020 - February 2021) . - pp 545 - 561 Titre : Automatic building footprint extraction from UAV images using neural networks Type de document : Article/Communication Auteurs : Zoran Kokeza, Auteur ; Miroslav Vujasinović, Auteur ; Miro Govedarica, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 545 - 561 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] Bosnie [Termes IGN] cartographie cadastrale [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] détection du bâti [Termes IGN] empreinte [Termes IGN] image à haute résolution [Termes IGN] image captée par drone [Termes IGN] image RVB [Termes IGN] modèle numérique de surface [Termes IGN] orthoimage [Termes IGN] zone d'intérêt Résumé : (Auteur) Up-to-date cadastral maps are crucial for urban planning. Creating those maps with the classical geodetic methods is expensive and time-consuming. Emerge of Unmanned Aerial Vehicles (UAV) made a possibility for quick acquisition of data with much more details than it was possible before. The topic of the research refers to the challenges of automatic extraction of building footprints on high-resolution orthophotos. The objectives of this study were as follows: (1) to test the possibility of using different publicly available datasets (Tanzania, AIRS and Inria) for neural network training and then test the generalisation capability of the model on the Area Of Interest (AOI); (2) to evaluate the effect of the normalised digital surface model (nDSM) on the results of neural network training and implementation. Evaluation of the results shown that the models trained on the Tanzania (IoU 36.4%), AIRS (IoU 64.4%) and Inria (IoU 7.4%) datasets doesn't satisfy the requested accuracy to update cadastral maps in study area. Much better results are achieved in the second part of the study, where the training of the neural network was done on tiles (256x256) of the orthophoto of AOI created from data acquired using UAV. A combination of RGB orthophoto with nDSM resulted in a 2% increase of IoU, achieving the final IoU of over 90%. Numéro de notice : A2020-777 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.15292/geodetski-vestnik.2020.04.545-561 date de publication en ligne : 26/10/2020 En ligne : http://www.geodetski-vestnik.com/en/2020-4 Format de la ressource électronique : URL bulletin Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96706 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
139-2020041	SL	Revue	Centre de documentation	Revues en salle	Disponible

Crater detection and registration of planetary images through marked point processes, multiscale decomposition, and region-based analysis / David Solarna in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6039 - 6058 Titre : Crater detection and registration of planetary images through marked point processes, multiscale decomposition, and region-based analysis Type de document : Article/Communication Auteurs : David Solarna, Auteur ; Alberto Gotelli, Auteur ; Jacqueline Le Moigne, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 6039 - 6058 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] cratère [Termes IGN] détection de contours [Termes IGN] distance de Hausdorff [Termes IGN] extraction de traits caractéristiques [Termes IGN] image multitemporelle [Termes IGN] image thermique [Termes IGN] Mars (planète) [Termes IGN] ondelette [Termes IGN] processus ponctuel marqué [Termes IGN] séparateur à vaste marge [Termes IGN] transformation de Hough [Termes IGN] zone d'intérêt Résumé : (auteur) Because of the large variety of planetary sensors and spacecraft already collecting data and with many new and improved sensors being planned for future missions, planetary science needs to integrate numerous multimodal image sources, and, as a consequence, accurate and robust registration algorithms are required. In this article, we develop a new framework for crater detection based on marked point processes (MPPs) that can be used for planetary image registration. MPPs were found to be effective for various object detection tasks in Earth observation, and a new MPP model is proposed here for detecting craters in planetary data. The resulting spatial features are exploited for registration, together with fitness functions based on the MPP energy, on the mean directed Hausdorff distance, and on the mutual information. Two different methods—one based on birth–death processes and region-of-interest analysis and the other based on graph cuts and decimated wavelets—are developed within the proposed framework. Experiments with a large set of images, including 13 thermal infrared and visible images of the Mars surface, 20 semisimulated multitemporal pairs of images of the Mars surface, and a real multitemporal image pair of the Lunar surface, demonstrate the effectiveness of the proposed framework in terms of crater detection performance as well as for subpixel registration accuracy. Numéro de notice : A2020-526 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2970908 date de publication en ligne : 18/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2970908 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95704 [article]

A novel deep learning instance segmentation model for automated marine oil spill detection / Shamsudeen Temitope Yekeen in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 167 (September 2020) . - pp 190 - 200 Titre : A novel deep learning instance segmentation model for automated marine oil spill detection Type de document : Article/Communication Auteurs : Shamsudeen Temitope Yekeen, Auteur ; Abdul‐Lateef Balogun, Auteur ; Khamaruzaman B. Wan Yusof, Auteur Année de publication : 2020 Article en page(s) : pp 190 - 200 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] hydrocarbure [Termes IGN] image radar moirée [Termes IGN] marée noire [Termes IGN] segmentation sémantique [Termes IGN] vision par ordinateur [Termes IGN] zone d'intérêt Résumé : (auteur) The visual similarity of oil slick and other elements, known as look-alike, affects the reliability of synthetic aperture radar (SAR) images for marine oil spill detection. So far, detection and discrimination of oil spill and look-alike are still limited to the use of traditional machine learning algorithms and semantic segmentation deep learning models with limited accuracy. Thus, this study developed a novel deep learning oil spill detection model using computer vision instance segmentation Mask-Region-based Convolutional Neural Network (Mask R-CNN) model. The model training was conducted using transfer learning on the ResNet 101 on COCO as backbone in combination with Feature Pyramid Network (FPN) architecture for feature extraction at 30 epochs with 0.001 learning rate. Testing of the model was conducted using the least training and validation loss value on the withheld testing images. The model’s performance was evaluated using precision, recall, specificity, IoU, F1-measure and overall accuracy values. Ship detection and segmentation had the highest performance with overall accuracy of 98.3%. The model equally showed a higher accuracy for oil spill and look-alike detection and segmentation although oil spill detection outperformed look-alike with overall accuracy values of 96.6% and 91.0% respectively. The study concluded that the deep learning instance segmentation model performs better than conventional machine learning models and deep learning semantic segmentation models in detection and segmentation. Numéro de notice : A2020-548 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.07.011 date de publication en ligne : 28/07/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.07.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95774 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2020091	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2020093	DEP-RECP	Revue	LaSTIG	Dépôt en unité	Exclu du prêt
081-2020092	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Breaking the eyes: how do users get started with a coordinated and multiple view geovisualization tool? / Izabela Golebiowska in Cartographic journal (the), Vol 57 n° 3 (August 2020)  

Public [article]  inCartographic journal (the) > Vol 57 n° 3 (August 2020) . - pp 235 - 248 Titre : Breaking the eyes: how do users get started with a coordinated and multiple view geovisualization tool? Type de document : Article/Communication Auteurs : Izabela Golebiowska, Auteur ; Tomasz Opach, Auteur ; Jan Ketil Rød, Auteur Année de publication : 2020 Article en page(s) : pp 235 - 248 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse géovisuelle [Termes IGN] comportement [Termes IGN] exploration de données géographiques [Termes IGN] interactivité [Termes IGN] oculométrie [Termes IGN] utilisateur [Termes IGN] zone d'intérêt [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Maps are frequently combined with data displays in the form of coordinated and multiple views (CMV). Although CMV are valuable geovisualization tools, novice users may find them complex and thus require explanation. However, no tutorial guidelines have been developed that indicate what is helpful in understanding CMV geovisualization tools. We therefore conducted a study on the learnability of a CMV tool, informed with eye-tracking data, talk-aloud and interaction logs. We have investigated how untrained users work with a CMV geovisualization tool. The study revealed that: (1) despite their initial confusion, users found the tested tool pleasant to play with while getting to grips with how dynamic brushing works, (2) when examining the tool’s interface, participants mainly looked freely at explanatory elements, such as labels and the legend, but they explored interactive techniques only to a limited degree. We conclude with tips about tutorial design and layout design for CMV tools. Numéro de notice : A2020-805 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00087041.2019.1660513 date de publication en ligne : 26/02/2020 En ligne : https://doi.org/10.1080/00087041.2019.1660513 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96764 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
030-2020031	SL	Revue	Centre de documentation	Revues en salle	Disponible

GIS-based multi criteria decision making method to identify potential runoff storage zones within watershed / Vikas Kumar Rana in Annals of GIS, vol 26 n° 2 (April 2020)  

Public [article]  inAnnals of GIS > vol 26 n° 2 (April 2020) . - pp 149 - 168 Titre : GIS-based multi criteria decision making method to identify potential runoff storage zones within watershed Type de document : Article/Communication Auteurs : Vikas Kumar Rana, Auteur ; Tallavajhala Maruthi Venkata Suryanarayana, Auteur Année de publication : 2020 Article en page(s) : pp 149 - 168 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG [Termes IGN] aide à la décision [Termes IGN] analyse multicritère [Termes IGN] bassin hydrographique [Termes IGN] eau de surface [Termes IGN] hydrologie [Termes IGN] Inde [Termes IGN] processus d'analyse hiérarchique [Termes IGN] ruissellement [Termes IGN] stockage [Termes IGN] système d'information géographique [Termes IGN] zone d'intérêt Résumé : (auteur) The objective of this study was to identify potential runoff storage zones based on the various physical characteristics of the Vishwamitri watershed using a GIS-based conceptual framework that combines through analytic hierarchy process using multi criteria decision-making method. The conceptual framework will help to identify potential runoff storage zones for water storage sites based on the various physical characteristics (rainfall, slope, land use/land cover, height above the nearest drainage, stream order, curve number, topographic wetness index) of the watershed. It was found out that 17% of the area is optimally suitable, 33.2% of the area is moderately suitable, 33.1% of the area is marginally suitable and 18.7% of the area is not suitable for water storage zones/structures. Results will help concerned authorities in the proficient arrangement and execution of water-related plans and schemes, improve water shortage, reduce dependability on ground water and ensure sustainable water availability for local and agricultural purposes in the study area. Numéro de notice : A2020-321 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2020.1733083 date de publication en ligne : 29/02/2020 En ligne : https://doi.org/10.1080/19475683.2020.1733083 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95188 [article]

Context-aware convolutional neural network for object detection in VHR remote sensing imagery / Yiping Gong in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)  

Permalink

Imaging and diagnostic of sub-wavelength micro-structures, from closed-form algorithms to deep learning / Peipei Ran (2020)  

Permalink

Very high resolution land cover mapping of urban areas at global scale with convolutional neural network / Thomas Tilak (2020)  

Permalink

Sig-NMS-based faster R-CNN combining transfer learning for small target detection in VHR optical remote sensing imagery / Ruchan Dong in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)  

Permalink

A generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm / Ana Claudia Dos Santos Luciano in International journal of applied Earth observation and geoinformation, vol 80 (August 2019)  

Permalink

Segmentation d'image par intégration itérative de connaissances / Mahaman Sani Chaibou Salaou (2019)  

Permalink

An efficient technique for creating a continuum of equal-area map projections / Daniel "daan" Strebe in Cartography and Geographic Information Science, Vol 45 n° 6 (November 2018)  

Permalink

Caractérisation et qualification de Modèles Numériques de Surfaces (MNS) - Analyse de la cohérence avec des masques d’eau / Guillaume Sutter (2018)

Permalink

Machine learning and pose estimation for autonomous robot grasping with collaborative robots / Victor Talbot (2018) 

Permalink

The geometry of space-time prisms with uncertain anchors / Bart Kuijpers in International journal of geographical information science IJGIS, vol 31 n° 9-10 (September - October 2017)  

Permalink