Descripteur
Documents disponibles dans cette catégorie (45)
Ajouter le résultat dans votre panier Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Point cloud data processing optimization in spectral and spatial dimensions based on multispectral Lidar for urban single-wood extraction / Shuo Shi in ISPRS International journal of geo-information, vol 12 n° 3 (March 2023)
[article]
Titre : Point cloud data processing optimization in spectral and spatial dimensions based on multispectral Lidar for urban single-wood extraction Type de document : Article/Communication Auteurs : Shuo Shi, Auteur ; Xingtao Tang, Auteur ; Bowen Chen, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : n° 90 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse spectrale
[Termes IGN] arbre urbain
[Termes IGN] détection d'objet
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Houston (Texas)
[Termes IGN] interpolation
[Termes IGN] réflectance spectrale
[Termes IGN] segmentation
[Termes IGN] semis de pointsRésumé : (auteur) Lidar can effectively obtain three-dimensional information on ground objects. In recent years, lidar has developed rapidly from single-wavelength to multispectral hyperspectral imaging. The multispectral airborne lidar Optech Titan is the first commercial system that can collect point cloud data on 1550, 1064, and 532 nm channels. This study proposes a method of point cloud segmentation in the preprocessed intensity interpolation process to solve the problem of inaccurate intensity at the boundary during point cloud interpolation. The entire experiment consists of three steps. First, a multispectral lidar point cloud is obtained using point cloud segmentation and intensity interpolation; the spatial dimension advantage of the multispectral point cloud is used to improve the accuracy of spectral information interpolation. Second, point clouds are divided into eight categories by constructing geometric information, spectral reflectance information, and spectral characteristics. Accuracy evaluation and contribution analysis are also conducted through point cloud truth value and classification results. Lastly, the spatial dimension information is enhanced by point cloud drop sampling, the method is used to solve the error caused by airborne scanning and single-tree extraction of urban trees. Classification results showed that point cloud segmentation before intensity interpolation can effectively improve the interpolation and classification accuracies. The total classification accuracy of the data is improved by 3.7%. Compared with the extraction result (377) of single wood without subsampling treatment, the result of the urban tree extraction proved the effectiveness of the proposed method with a subsampling algorithm in improving the accuracy. Accordingly, the problem of over-segmentation is solved, and the final single-wood extraction result (329) is markedly consistent with the real situation of the region. Numéro de notice : A2023-159 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi12030090 Date de publication en ligne : 23/02/2023 En ligne : https://doi.org/10.3390/ijgi12030090 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102852
in ISPRS International journal of geo-information > vol 12 n° 3 (March 2023) . - n° 90[article]Spatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding / Faxi Yuan in Computers, Environment and Urban Systems, vol 97 (October 2022)
[article]
Titre : Spatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding Type de document : Article/Communication Auteurs : Faxi Yuan, Auteur ; Yuanchang Xu, Auteur ; Qingchun Li, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 101870 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] catastrophe naturelle
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] graphe
[Termes IGN] inondation
[Termes IGN] modèle de simulation
[Termes IGN] polynôme de Chebysheff
[Termes IGN] prévention des risques
[Termes IGN] réseau neuronal de graphes
[Termes IGN] réseau routier
[Termes IGN] Texas (Etats-Unis)
[Termes IGN] zone urbaineRésumé : (auteur) The objective of this study is to predict the near-future flooding status of road segments based on their own and adjacent road segments' current status through the use of deep learning framework on fine-grained traffic data. Predictive flood monitoring for situational awareness of road network status plays a critical role to support crisis response activities such as evaluation of the loss of access to hospitals and shelters. Existing studies related to near-future prediction of road network flooding status at road segment level are missing. Using fine-grained traffic speed data related to road sections, this study designed and implemented three spatio-temporal graph convolutional network (STGCN) models to predict road network status during flood events at the road segment level in the context of the 2017 hurricane Harvey in Harris County (Texas, USA). Model 1 consists of two spatio-temporal blocks considering the adjacency and distance between road segments, while model 2 contains an additional elevation block to account for elevation difference between road segments. Model 3 includes three blocks for considering the adjacency and the product of distance and elevation difference between road segments. The analysis tested the STGCN models and evaluated their prediction performance. Our results indicated that model 1 and model 2 have reliable and accurate performance for predicting road network flooding status in near future (e.g., 2–4 h) with model precision and recall values larger than 98% and 96%, respectively. With reliable road network status predictions in floods, the proposed model can benefit affected communities to avoid flooded roads and the emergency management agencies to implement evacuation and relief resource delivery plans. Numéro de notice : A2022-656 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1016/j.compenvurbsys.2022.101870 Date de publication en ligne : 22/08/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2022.101870 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101506
in Computers, Environment and Urban Systems > vol 97 (October 2022) . - n° 101870[article]Assessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data / Cheng-Chun Lee in Computers, Environment and Urban Systems, vol 93 (April 2022)
[article]
Titre : Assessing surface drainage conditions at the street and neighborhood scale: A computer vision and flow direction method applied to lidar data Type de document : Article/Communication Auteurs : Cheng-Chun Lee, Auteur ; Nasir G. Gharaibeh, Auteur Année de publication : 2022 Article en page(s) : n° 101755 Note générale : bibliogrphie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] drainage
[Termes IGN] écoulement des eaux
[Termes IGN] Houston (Texas)
[Termes IGN] inondation
[Termes IGN] lidar mobile
[Termes IGN] modèle numérique de surface
[Termes IGN] ruissellement
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Surface drainage at the neighborhood and street scales plays an important role in conveying stormwater and mitigating urban flooding. Surface drainage at the local scale is often ignored due to the lack of up-to-date fine-scale topographical information. This paper addresses this issue by providing a novel method for evaluating surface drainage at the neighborhood and street scales based on mobile lidar (light detection and ranging) measurements. The developed method derives topographical properties and runoff accumulation by applying a semantic segmentation (SS) model (a computer vision technique) and a flow direction model (a hydrology technique) to lidar data. Fifty lidar images representing 50 street blocks were used to train, validate, and test the SS model. Based on the test dataset, the SS model has 80.3% IoU and 88.5% accuracy. The results suggest that the proposed method can effectively evaluate surface drainage conditions at both the neighborhood and street scales and identify problematic low points that could be susceptible to water ponding. Municipalities and property owners can use this information to take targeted corrective maintenance actions. Numéro de notice : A2022-120 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article DOI : 10.1016/j.compenvurbsys.2021.101755 Date de publication en ligne : 13/01/2022 En ligne : https://doi.org/10.1016/j.compenvurbsys.2021.101755 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99661
in Computers, Environment and Urban Systems > vol 93 (April 2022) . - n° 101755[article]Comprehensive study on the tropospheric wet delay and horizontal gradients during a severe weather event / Victoria Graffigna in Remote sensing, vol 14 n° 4 (February-2 2022)
[article]
Titre : Comprehensive study on the tropospheric wet delay and horizontal gradients during a severe weather event Type de document : Article/Communication Auteurs : Victoria Graffigna, Auteur ; Manuel Hernández-Pajares, Auteur ; Francisco Azpilicueta, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 888 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale
[Termes IGN] données météorologiques
[Termes IGN] gradient de troposphère
[Termes IGN] phénomène climatique extrême
[Termes IGN] positionnement ponctuel précis
[Termes IGN] retard troposphérique zénithal
[Termes IGN] station GNSS
[Termes IGN] surveillance météorologique
[Termes IGN] tempête
[Termes IGN] Texas (Etats-Unis)
[Termes IGN] vapeur d'eauRésumé : (auteur) GNSS meteorology is today one of the most growing technologies to monitor severe weather events. In this paper, we present the usage of 160 GPS reference stations over the period of 14 days to monitor and track Hurricane Harvey, which struck Texas in August 2017. We estimate the Zenith Wet Delay (ZWD) and the tropospheric gradients with 30 s interval using TOMION v2 software and carry out the processing in Precise Point Positioning (PPP) mode. We study the relationship of these parameters with atmospheric variables extracted from Tropical Rainfall Measuring Mission (TRMM) satellite mission and climate reanalysis model ERA5. This research finds that the ZWD shows patterns related to the rainfall rate and to the location of the hurricane. We also find that the tropospheric gradients are correlated with water vapor gradients before and after the hurricane, and with the wind and the pressure gradients only after the hurricane. This study also shows a new finding regarding the spectral distribution of the gradients, with a clear diurnal period present, which is also found on the ZWD itself. This kind of study approaches the GNSS meteorology to the increasing requirements of meteorologist in terms of monitoring severe weather events. Numéro de notice : A2022-166 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.3390/rs14040888 Date de publication en ligne : 12/02/2022 En ligne : https://doi.org/10.3390/rs14040888 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99791
in Remote sensing > vol 14 n° 4 (February-2 2022) . - n° 888[article]Modeling land use change and forest carbon stock changes in temperate forests in the United States / Lucia Fitts in Carbon Balance and Management, vol 16 ([01/02/2021])
[article]
Titre : Modeling land use change and forest carbon stock changes in temperate forests in the United States Type de document : Article/Communication Auteurs : Lucia Fitts, Auteur ; Matthew B. Russell, Auteur ; Grant M. Domke, Auteur ; Joseph F. Knight, Auteur Année de publication : 2021 Article en page(s) : n° 20 (2021) Langues : Anglais (eng) Descripteur : [Termes IGN] changement d'occupation du sol
[Termes IGN] Colorado (Etats-Unis)
[Termes IGN] forêt tempérée
[Termes IGN] Géorgie (Etats-Unis)
[Termes IGN] impact sur l'environnement
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] New York (Etats-Unis ; état)
[Termes IGN] puits de carbone
[Termes IGN] Texas (Etats-Unis)
[Termes IGN] Wisconsin (Etats-Unis)
[Vedettes matières IGN] Végétation et changement climatiqueRésumé : (auteur) Background : Forests provide the largest terrestrial sink of carbon (C). However, these C stocks are threatened by forest land conversion. Land use change has global impacts and is a critical component when studying C fluxes, but it is not always fully considered in C accounting despite being a major contributor to emissions. An urgent need exists among decision-makers to identify the likelihood of forest conversion to other land uses and factors affecting C loss. To help address this issue, we conducted our research in California, Colorado, Georgia, New York, Texas, and Wisconsin. The objectives were to (1) model the probability of forest conversion and C stocks dynamics using USDA Forest Service Forest Inventory and Analysis (FIA) data and (2) create wall-to-wall maps showing estimates of the risk of areas to convert from forest to non-forest. We used two modeling approaches: a machine learning algorithm (random forest) and generalized mixed-effects models. Explanatory variables for the models included ecological attributes, topography, census data, forest disturbances, and forest conditions. Model predictions and Landsat spectral information were used to produce wall-to-wall probability maps of forest change using Google Earth Engine.
Results : During the study period (2000–2017), 3.4% of the analyzed FIA plots transitioned from forest to mixed or non-forested conditions. Results indicate that the change in land use from forests is more likely with increasing human population and housing growth rates. Furthermore, non-public forests showed a higher probability of forest change compared to public forests. Areas closer to cities and coastal areas showed a higher risk of transition to non-forests. Out of the six states analyzed, Colorado had the highest risk of conversion and the largest amount of aboveground C lost. Natural forest disturbances were not a major predictor of land use change.
Conclusions : Land use change is accelerating globally, causing a large increase in C emissions. Our results will help policy-makers prioritize forest management activities and land use planning by providing a quantitative framework that can enhance forest health and productivity. This work will also inform climate change mitigation strategies by understanding the role that land use change plays in C emissions.Numéro de notice : A2021-501 Affiliation des auteurs : non IGN Thématique : FORET/INFORMATIQUE Nature : Article DOI : 10.1186/s13021-021-00183-6 Date de publication en ligne : 03/07/2021 En ligne : https://doi.org/10.1186/s13021-021-00183-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98099
in Carbon Balance and Management > vol 16 [01/02/2021] . - n° 20 (2021)[article]Classification of hyperspectral and LiDAR data using coupled CNNs / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)PermalinkPlacial analysis of events: a case study on criminological places / Sunghwan Cho in Cartography and Geographic Information Science, Vol 46 n° 6 (November 2019)PermalinkInterpreting effects of multiple, large-scale disturbances using national forest inventory data: A case study of standing dead trees in east Texas, USA / Christopher B. Edgar in Forest ecology and management, vol 437 (1 April 2019)PermalinkIntegrating urban and national forest inventory data in support of rural–urban assessments / James A. Westfall in Forestry, an international journal of forest research, vol 91 n° 5 (December 2018)PermalinkSpatial mining of migration patterns from web demographics / T. Edwin Chow in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)PermalinkA spatial analysis of non‐English Twitter activity in Houston, TX / Matthew Haffner in Transactions in GIS, vol 22 n° 4 (August 2018)PermalinkA posteriori bias correction of three models used for environmental reporting / Bogdan M. Strimbu in Forestry, an international journal of forest research, vol 91 n° 1 (January 2018)PermalinkFusion of hyperspectral and LiDAR data using sparse and low-rank component analysis / Behnood Rasti in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)PermalinkOn the spectral combination of satellite gravity model, terrestrial and airborne gravity data for local gravimetric geoid computation / Tao Jian in Journal of geodesy, vol 90 n° 12 (December 2016)PermalinkBumps and bruises in the digital skins of cities: unevenly distributed user-generated content across US urban areas / Colin Robertson in Cartography and Geographic Information Science, Vol 43 n° 4 (September 2016)PermalinkSpatial eigenvector filtering for spatiotemporal crime mapping and spatial crime analysis / Marco Helbich in Cartography and Geographic Information Science, Vol 42 n° 2 (April 2015)PermalinkCharacterization of neighborhood sensitivity of an irregular cellular automata model of urban growth / Khila R. Dahal in International journal of geographical information science IJGIS, vol 29 n° 3 (March 2015)PermalinkAn entropy-based multispectral image classification algorithm / Di Long in IEEE Transactions on geoscience and remote sensing, vol 51 n° 12 (December 2013)PermalinkDeveloping an object-based hyperspatial image classifier with a case study using WorldView-2 data / Harini Sridharan in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)PermalinkThe electronically steerable flash Lidar : A full waveform scanning system for topographic and ecosystem structure applications / H. Duong in IEEE Transactions on geoscience and remote sensing, vol 50 n° 11 Tome 2 (November 2012)PermalinkAn automated system for image-to-vector georeferencing / Y. Li in Cartography and Geographic Information Science, vol 39 n° 4 (October 2012)PermalinkQuantifying urban land cover change between 2001 and 2006 in the Gulf of Mexico region / George Xian in Geocarto international, vol 27 n° 6 (October 2012)PermalinkPermalinkThe development of a web-based demographic data extraction tool for population monitoring / T. Chow in Transactions in GIS, vol 15 n° 4 (August 2011)PermalinkMapping an annual weed with colour-infared aerial photography and image analysis / James H. Everitt in Geocarto international, vol 25 n° 1 (February 2010)PermalinkObservations of urban and suburban environments with global satellite scatterometer data / Son V. Nghiem in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 4 (July - August 2009)PermalinkMorphology-based building detection from airborne Lidar data / X. Meng in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 4 (April 2009)PermalinkRaster-network regionalization for watershed data processing / T.L. Whiteaker in International journal of geographical information science IJGIS, vol 21 n° 3-4 (march - april 2007)PermalinkSpectral analysis of coastal vegetation and land cover using AISA+ hyperspectral data / R. Jensen in Geocarto international, vol 22 n° 1 (March - May 2007)PermalinkA uniform sky illumination model to enhance shading of terrain and urban areas / Patrick Kennelly in Cartography and Geographic Information Science, vol 33 n° 1 (January 2006)PermalinkEvaluating airborne hyperspectral imagery for rangeland assessment in south Texas / James H. Everitt in Geocarto international, vol 19 n° 3 (September - November 2004)PermalinkThe value of cadastral surveying to efficient land administration / G. Jeffress in Surveying and land information science, vol 63 n° 4 (01/12/2003)PermalinkModeling urban population growth from remotely sensed imagery and TIGER GIS road data / F. Qiu in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 9 (September 2003)PermalinkCreating a statewide digital base map: the Texas orthoimagery program / D. Decker in Surveying and land information systems, vol 57 n° 1 (March 1997)PermalinkUtilization of Landsat multispectral data in geobotanical investigations : the location of ironstone gravel in the Sam Houston national forest, Texas / W.G. Cibula in International Journal of Remote Sensing IJRS, vol 11 n° 2 (February 1990)PermalinkOptimum band selection for supervised classification of multispectral data / P.W. Mausel in Photogrammetric Engineering & Remote Sensing, PERS, vol 56 n° 1 (january 1990)PermalinkEstimating grassland phytomass production with near-infrared and mid-infrared spectral variables / James H. Everitt in Remote sensing of environment, vol 30 n° 3 (01/12/1989)PermalinkCanopy leaf display effects on absorbed, transmitted, and reflected solar radiation / A.J. Richardson in Remote sensing of environment, vol 29 n° 1 (July 1989)PermalinkRemote sensor and other data sources for timber use-value assessment / R.D. Baker in Photogrammetric Engineering & Remote Sensing, PERS, vol 55 n° 6 (june 1989)PermalinkDetecting saline soils with video imagery / James H. Everitt in Photogrammetric Engineering & Remote Sensing, PERS, vol 54 n° 9 (september 1988)PermalinkEstimating soil wetness using satellite data / B.J. Choudhury in International Journal of Remote Sensing IJRS, vol 9 n° 7 (July 1988)PermalinkModelling planting configuration and canopy architecture effects on diurnal light absorption changes in cotton / A.J. Richardson in International Journal of Remote Sensing IJRS, vol 9 n° 4 (April 1988)PermalinkSurface anisotropy and hemispheric reflectance for a semiarid ecosystem / E.M. Middleton in Remote sensing of environment, vol 23 n° 2 (01/11/1987)PermalinkDrought-stress detection of buffelgrass with color-infrared aerial photography and computer-aided image processing / James H. Everitt in Photogrammetric Engineering & Remote Sensing, PERS, vol 53 n° 9 (september 1987)PermalinkRacing into tomorrow, 1985 ACSM ASPRS Fall convention / American society for photogrammetry and remote sensing (1985)Permalink