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Automated terrain feature identification from remote sensing imagery: a deep learning approach / Wenwen Li in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)
[article]
Titre : Automated terrain feature identification from remote sensing imagery: a deep learning approach Type de document : Article/Communication Auteurs : Wenwen Li, Auteur ; Chia-Yu Hsu, Auteur Année de publication : 2020 Article en page(s) : pp 637 - 660 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse du paysage
[Termes IGN] apprentissage profond
[Termes IGN] base de données d'images
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] compréhension de l'image
[Termes IGN] détection automatique
[Termes IGN] détection d'objet
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] intelligence artificielleRésumé : (auteur) Terrain feature detection is a fundamental task in terrain analysis and landscape scene interpretation. Discovering where a specific feature (i.e. sand dune, crater, etc.) is located and how it evolves over time is essential for understanding landform processes and their impacts on the environment, ecosystem, and human population. Traditional induction-based approaches are challenged by their inefficiency for generalizing diverse and complex terrain features as well as their performance for scalable processing of the massive geospatial data available. This paper presents a new deep learning (DL) approach to support automatic detection of terrain features from remotely sensed images. The novelty of this work lies in: (1) a terrain feature database containing 12,000 remotely sensed images (1,000 original images and 11,000 derived images from data augmentation) that supports data-driven model training and new discovery; (2) a DL-based object detection network empowered by ensemble learning and deep and deeper convolutional neural networks to achieve high-accuracy object detection; and (3) fine-tuning the model’s characteristics and behaviors to identify the best combination of hyperparameters and other network factors. The introduction of DL into geospatial applications is expected to contribute significantly to intelligent terrain analysis, landscape scene interpretation, and the maturation of spatial data science. Numéro de notice : A2020-108 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1542697 Date de publication en ligne : 07/11/2018 En ligne : https://doi.org/10.1080/13658816.2018.1542697 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94708
in International journal of geographical information science IJGIS > vol 34 n° 4 (April 2020) . - pp 637 - 660[article]Geocoding of trees from street addresses and street-level images / Daniel Laumer in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)
[article]
Titre : Geocoding of trees from street addresses and street-level images Type de document : Article/Communication Auteurs : Daniel Laumer, Auteur ; Nico Lang, Auteur ; Natalie Van Doorn, Auteur Année de publication : 2020 Article en page(s) : pp 125 - 136 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse des correspondances
[Termes IGN] apprentissage profond
[Termes IGN] arbre urbain
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'arbres
[Termes IGN] détection d'objet
[Termes IGN] géocodage par adresse postale
[Termes IGN] image panoramique
[Termes IGN] image Streetview
[Termes IGN] inventaire
[Termes IGN] service écosystémique
[Termes IGN] zone urbaineRésumé : (auteur) We introduce an approach for updating older tree inventories with geographic coordinates using street-level panorama images and a global optimization framework for tree instance matching. Geolocations of trees in inventories until the early 2000s where recorded using street addresses whereas newer inventories use GPS. Our method retrofits older inventories with geographic coordinates to allow connecting them with newer inventories to facilitate long-term studies on tree mortality etc. What makes this problem challenging is the different number of trees per street address, the heterogeneous appearance of different tree instances in the images, ambiguous tree positions if viewed from multiple images and occlusions. To solve this assignment problem, we (i) detect trees in Google street-view panoramas using deep learning, (ii) combine multi-view detections per tree into a single representation, (iii) and match detected trees with given trees per street address with a global optimization approach. Experiments for trees in 5 cities in California, USA, show that we are able to assign geographic coordinates to 38% of the street trees, which is a good starting point for long-term studies on the ecosystem services value of street trees at large scale. Numéro de notice : A2020-124 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.001 Date de publication en ligne : 21/02/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.001 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94749
in ISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 125 - 136[article]Assessment of salt marsh change on Assateague Island National Seashore between 1962 and 2016 / Anthony Campbell in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)
[article]
Titre : Assessment of salt marsh change on Assateague Island National Seashore between 1962 and 2016 Type de document : Article/Communication Auteurs : Anthony Campbell, Auteur ; Yeqiao Wang, Auteur Année de publication : 2020 Article en page(s) : pp 187 - 194 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] analyse diachronique
[Termes IGN] approche hiérarchique
[Termes IGN] Atlantique (océan)
[Termes IGN] biodiversité
[Termes IGN] cartographie thématique
[Termes IGN] détection de changement
[Termes IGN] Etats-Unis
[Termes IGN] image à très haute résolution
[Termes IGN] image satellite
[Termes IGN] lidar bathymétrique
[Termes IGN] littoral
[Termes IGN] marais salant
[Termes IGN] montée du niveau de la mer
[Termes IGN] service écosystémique
[Termes IGN] surveillance du littoralRésumé : (auteur) Salt marshes provide extensive ecosystem services, including high biodiversity, denitrification, and wave attenuation. In the mid-Atlantic, sea level rise is predicted to affect salt marsh ecosystems severely. This study mapped the entirety of Assateague Island with Very High Resolution satellite imagery and object-based methods to determine an accurate salt marsh baseline for change analysis. Topobathy-metric light detection and ranging was used to map the salt marsh and model expected tidal effects. The satellite imagery, collected in 2016 and classified at two hierarchical thematic schemes, were compared to determine appropriate thematic richness. Change analysis between this 2016 map and both a manually delineated 1962 salt marsh extent and image classification of the island from 1994 determined rates off change. The study found that from 1962 to 1994, salt marsh expanded by 4.01 ha/year, and from 1994 to 2016 salt marsh was lost at a rate of -3.4 ha/ year. The study found that salt marsh composition, (percent vegetated salt marsh) was significantly influenced by elevation, the length of mosquito ditches, and starting salt marsh composition. The study illustrates the importance of remote sensing monitoring for understanding site-specific changes to salt marsh environments and the barrier island system. Numéro de notice : A2020-148 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.3.187 Date de publication en ligne : 01/03/2020 En ligne : https://doi.org/10.14358/PERS.86.3.187 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94777
in Photogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 3 (March 2020) . - pp 187 - 194[article]Classification and segmentation of mining area objects in large-scale spares Lidar point cloud using a novel rotated density network / Yueguan Yan in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)
[article]
Titre : Classification and segmentation of mining area objects in large-scale spares Lidar point cloud using a novel rotated density network Type de document : Article/Communication Auteurs : Yueguan Yan, Auteur ; Haixu Yan, Auteur ; Junting Guo, Auteur ; Huayang Dai, Auteur Année de publication : 2020 Article en page(s) : 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] classification barycentrique
[Termes IGN] classification orientée objet
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] corrélation automatique de points homologues
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] objet 3D
[Termes IGN] reconnaissance d'objets
[Termes IGN] semis de points clairsemésRésumé : (auteur) The classification and segmentation of large-scale, sparse, LiDAR point cloud with deep learning are widely used in engineering survey and geoscience. The loose structure and the non-uniform point density are the two major constraints to utilize the sparse point cloud. This paper proposes a lightweight auxiliary network, called the rotated density-based network (RD-Net), and a novel point cloud preprocessing method, Grid Trajectory Box (GT-Box), to solve these problems. The combination of RD-Net and PointNet was used to achieve high-precision 3D classification and segmentation of the sparse point cloud. It emphasizes the importance of the density feature of LiDAR points for 3D object recognition of sparse point cloud. Furthermore, RD-Net plus PointCNN, PointNet, PointCNN, and RD-Net were introduced as comparisons. Public datasets were used to evaluate the performance of the proposed method. The results showed that the RD-Net could significantly improve the performance of sparse point cloud recognition for the coordinate-based network and could improve the classification accuracy to 94% and the segmentation per-accuracy to 70%. Additionally, the results concluded that point-density information has an independent spatial–local correlation and plays an essential role in the process of sparse point cloud recognition. Numéro de notice : A2020-256 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 0.3390/ijgi9030182 Date de publication en ligne : 24/03/2020 En ligne : https://doi.org/10.3390/ijgi9030182 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95012
in ISPRS International journal of geo-information > vol 9 n° 3 (March 2020) . - 19 p.[article]Heuristic sample learning for complex urban scenes: Application to urban functional-zone mapping with VHR images and POI data / Xiuyuan Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)
[article]
Titre : Heuristic sample learning for complex urban scenes: Application to urban functional-zone mapping with VHR images and POI data Type de document : Article/Communication Auteurs : Xiuyuan Zhang, Auteur ; Shihong Du, Auteur ; Zhijia Zheng, Auteur Année de publication : 2020 Article en page(s) : pp 1 - 12 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage dirigé
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] cartographie urbaine
[Termes IGN] Chine
[Termes IGN] échantillonnage d'image
[Termes IGN] image à très haute résolution
[Termes IGN] méthode heuristique
[Termes IGN] point d'intérêt
[Termes IGN] scène urbaineRésumé : (Auteur) Urban functional zones are basic units of urban planning and resource allocation, and contribute to a wide range of urban studies and investigations. Existing studies on functional-zone mapping with very-high-resolution (VHR) satellite images focused much on feature representations and classification techniques, but ignored zone sampling which however was fundamental to automatic zone classifications. Functional-zone sampling is much complicated and can hardly be resolved by classical sampling methods, as functional zones are complex urban scenes which consist of heterogeneous land covers and have highly abstract categories. To resolve the issue, this study presents a novel sampling paradigm, i.e., heuristic sample learning (HSL). It first proposes a sparse topic model to select representative functional zones, then uses deep forest to select confusing zones, and finally embraces Chinese restaurant process to label these selected zones. The presented method collects both representative and confusing zone samples and identifies their categories accurately, which makes the functional-zone classification process robust and the classification results accurate. Experiments conducted in Beijing indicate that HSL is effective and efficient for functional-zone sampling and classifications. Compared to traditional manual sampling, HSL reduces the time cost by 55% and improves the classification accuracy by 11.3% on average; furthermore, HSL can reduce the variation in sampling and classification results caused by different proficiency of operators. Accordingly, HSL significantly contributes to functional-zone mapping and plays an important role in urban studies. Numéro de notice : A2020-061 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.01.005 Date de publication en ligne : 13/01/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.01.005 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94577
in ISPRS Journal of photogrammetry and remote sensing > vol 161 (March 2020) . - pp 1 - 12[article]Réservation
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