Suchergebnisse - "Geospatial Data Processing"
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Autoren: et al.
Weitere Verfasser: et al.
Quelle: Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems. :725-728
Schlagwörter: • Information systems Large-Scale Optimization, CCS Concepts, EV Infrastructure Planning, Queuing Theory, Applied computing → Transportation, Smart Spatial Grid, Spatial Optimization, • Computing methodologies → Machine learning, [INFO] Computer Science [cs], Electric Vehicle Charging, Geospatial Data Processing, CCS Concepts Applied computing → Transportation • Computing methodologies → Machine learning • Information systems Large-Scale Optimization
Dateibeschreibung: application/pdf
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Autoren:
Schlagwörter: multidimensional scaling, Daten -- Datenbank -- Datenprozessierung -- Geodaten-Verarbeitung -- Multidimensionale Skalierung -- PostgreSQL -- PostGIS -- Apache Spark -- Apache Sedona, Apache Spark, article, Datenbank, data -- database -- data processing -- geospatial data processing -- multidimensional scaling, Apache Sedona, Daten, Geodaten-Verarbeitung, PostgreSQL, PostGIS, Datenprozessierung, data, Multidimensionale Skalierung, ddc:520, geospatial data processing, database, data processing
Dateibeschreibung: 1 Online-Ressource (Seite 187, 51.25 kB) : 1 Textdatei (PDF)
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Schlagwörter: practice report -- fibre optic cable -- fiber optic network expansion -- geospatial data processing -- geospatial data visualization -- planning -- fiber optic cable route -- cost calculation -- material calculation -- as-built documentation, Planung, Geodaten-Visualisierung, Deutsche Telekom AG, Raumdaten, Trassenverlauf, QGIS-Plugin, fiber optic network expansion, as-built documentation, Geodaten-Prozessierung, Kostenberechnung, fibre optic cable, fiber optic cable route, cost calculation, Praxisbericht -- Glasfaserkabel -- Glasfasernetzausbau -- Raumdaten -- Geodaten -- Geodaten-Prozessierung -- Geodaten-Visualisierung -- Open Source -- QGIS -- QGIS-Plugin -- Plan[Goo] -- Planung -- Trassenverlauf -- Kostenberechnung -- Materialberechnung -- Bestandsdokumentation -- Deutsche Telekom AG, Open Source, Glasfaserkabel, Bestandsdokumentation, geospatial data visualization, Praxisbericht, article, Geodaten, material calculation, Materialberechnung, Glasfasernetzausbau, Plan[Goo], ddc:520, geospatial data processing, planning, practice report, QGIS
Dateibeschreibung: 1 Online-Ressource (Seite 221, 36,91 kB) : 1 Textdatei (PDF)
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Autoren: et al.
Weitere Verfasser: et al.
Quelle: Precision Agriculture. 25:2703-2720
Schlagwörter: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, 2. Zero hunger, [SDV.SA] Life Sciences [q-bio]/Agricultural sciences, GNSS, [SDV]Life Sciences [q-bio], Precision, 15. Life on land, viticulture ·, Precision viticulture GNSS Geospatial data processing Nitrogen fertilization, Geospatial data processing, Nitrogen fertilization, Precision viticulture
Dateibeschreibung: application/pdf
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Autoren: et al.
Weitere Verfasser: et al.
Schlagwörter: geodetic measurements, 3D laser scanning, geodesy, Trimble SX10, geospatial data processing, scanning densities, 3D modeling
Dateibeschreibung: application/pdf
Zugangs-URL: https://urn.nsk.hr/urn:nbn:hr:122:732880
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Autoren: et al.
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Quelle: 20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2024)
https://hal.science/hal-04886105
20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2024), Jul 2024, Lisboa, Portugal. pp.377-388, ⟨10.1007/978-3-031-74003-9_30⟩
https://ipmu2024.inesc-id.pt/Schlagwörter: Geospatial data processing, Uncertainty, Imprecise Probability, Interval data, [INFO]Computer Science [cs]
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Quelle: Spatial development; No. 7 (2024); 396-408
Просторовий розвиток; № 7 (2024); 396-408Schlagwörter: ГІС, метод Сімпсона, ellipsoid, rigorous computer methods, geospatial data processing, GIS, референц-еліпсоїд, Simpson's method, morphometry, морфометрія, строгі комп'ютерні методи, опрацювання геопросторових даних
Dateibeschreibung: application/pdf
Zugangs-URL: http://spd.knuba.edu.ua/article/view/302636
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Autoren: et al.
Quelle: EDIS, Vol 2024, Iss 1 (2024)
Schlagwörter: remote sensing, QH301-705.5, Agriculture (General), aerial mapping, Plant culture, UAS, geospatial data processing, Biology (General), digital image processing, image processing, S1-972, SB1-1110
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Autoren: et al.
Weitere Verfasser: et al.
Quelle: Precision agriculture '23 ISBN: 9789086869473
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Autoren: Кінь, Данило
Quelle: Spatial development; No. 7 (2024); 396-408 ; Просторовий розвиток; № 7 (2024); 396-408 ; 2786-7277 ; 2786-7269 ; 10.32347/2786-7269.2024.7
Schlagwörter: ellipsoid, morphometry, rigorous computer methods, GIS, Simpson's method, geospatial data processing, референц-еліпсоїд, морфометрія, строгі комп’ютерні методи, ГІС, метод Сімпсона, опрацювання геопросторових даних
Dateibeschreibung: application/pdf
Relation: http://spd.knuba.edu.ua/article/view/302636/294601; http://spd.knuba.edu.ua/article/view/302636
Verfügbarkeit: http://spd.knuba.edu.ua/article/view/302636
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Autoren: et al.
Weitere Verfasser: et al.
Schlagwörter: emergency and urban planning coherence, geospatial data processing, multirisk management, resilience, resilient urban development, volcanic hazards
Relation: info:eu-repo/semantics/altIdentifier/wos/WOS:001332959300001; volume:16; issue:19; firstpage:1; lastpage:26; numberofpages:26; journal:SUSTAINABILITY; https://hdl.handle.net/11311/1285162
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Schlagwörter: SAR, DEM, flood inundation mapping, water depth modelling, contour matching, geospatial data processing, remote sensing, hydrology, machine learning, floods, fjärranalys, hydrologia, maskininlärning, översvämningar, kaukokartoitus, koneoppiminen, tulvat, 113 Data- och informationsvetenskap
Dateibeschreibung: true
Relation: https://www.doria.fi/handle/10024/193385
Verfügbarkeit: https://www.doria.fi/handle/10024/193385
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Schlagwörter: 3D lasersko skeniranje, gustoće skeniranja, Trimble SX10, geodetska mjerenja, 3D modeliranje, obrada geoprostornih podataka, geodezija, 3D laser scanning, scanning densities, geodetic measurements, 3D modeling, geospatial data processing, geodesy, TEHNIČKE ZNANOSTI. Geodezija, TECHNICAL SCIENCES. Geodesy
Dateibeschreibung: application/pdf
Relation: https://zir.nsk.hr/islandora/object/unin:7880; https://urn.nsk.hr/urn:nbn:hr:122:732880; https://zir.nsk.hr/islandora/object/unin:7880/datastream/PDF
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Autoren: et al.
Weitere Verfasser: et al.
Schlagwörter: Àrees temàtiques de la UPC::Enginyeria civil::Geomàtica, Lasers - Industrial applications, Geospatial data, Vector spaces, Mobile mapping, LiDAR, Basemap updating, Geospatial data processing, Municipalities, Feature extraction, Vectorization, Point cloud classification, Làsers, Dades geoespacials, Espais vectorials
Dateibeschreibung: application/pdf
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Autoren: et al.
Weitere Verfasser: et al.
Schlagwörter: Free and open source software, Geoportal, Geospatial data processing, Geovisualization, Multidimensional, Open standards, Virtual globes, Web
Dateibeschreibung: ELETTRONICO
Relation: info:eu-repo/semantics/altIdentifier/wos/WOS:000554204500001; volume:9; issue:7; firstpage:434; lastpage:459; numberofpages:26; journal:ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION; https://hdl.handle.net/11311/1154789
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Autoren: et al.
Quelle: Sensors, Vol 19, Iss 7, p 1486 (2019)
Schlagwörter: remote sensing, convolutional neural networks, floodplain mapping, fully convolutional network, unmanned aerial vehicles, geospatial data processing, Chemical technology, TP1-1185
Relation: https://www.mdpi.com/1424-8220/19/7/1486; https://doaj.org/toc/1424-8220; https://doaj.org/article/6a0e7afd1b82418294da070acafcb4ba
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Schlagwörter: Routovací algoritmus, historická dopravní data, Waze, OpenStreetMap, algoritmus A, heuristika ALT, zpracování geoprostorových dat, nalezení cesty, Routing algorithm, historical traffic data, A* algorithm, ALT heuristic, geospatial data processing, pathfinding
Dateibeschreibung: application/pdf; text/html
Relation: STRELEC, M. Routovací algoritmus pro plánování dopravy v Brně [online]. Brno: Vysoké učení technické v Brně. Fakulta informačních technologií. 2025.; 164964; https://hdl.handle.net/11012/254393
Verfügbarkeit: https://hdl.handle.net/11012/254393
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Schlagwörter: Design, Reliability, Performance. Keywords Isoscapes, TeraGrid, Geospatial data processing, Dynamic map rendering, Geostatistical model, Stable isotope distribution
Dateibeschreibung: application/pdf
Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.697.8929; http://www.stat.purdue.edu/%7Etlzhang/Lee_et_al_TeraGrid_2011.pdf
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Autoren: et al.
Weitere Verfasser: et al.
Schlagwörter: бездротова мережа, wireless network, виявлення аварій, accident detection, обробка геопросторових даних, geospatial data processing, розгортання давачів, sensor deployment, розумне місто, smart city, міські обчислення, urban computing, 004.62
Geographisches Schlagwort: ТНТУ ім. І.Пулюя, ФІС, м. Тернопіль, Україна, UA
Relation: 1 Duda, O., Kunanets, N., Martsenko, S., Matsiuk, O., Pasichnyk, V., Building secure Urban information systems based on IoT technologies. CEUR Workshop Proceedings 2623, pp. 317-328. 2020.; 2 Musznicki, B., Piechowiak, M., & Zwierzykowski, P. (2022). Modeling real-life urban sensor networks based on open data. Sensors, 22, 9264.; 3 Duda, O., et al, Selection of Effective Methods of Big Data Analytical Processing in Information Systems of Smart Cities. CEUR Workshop Proceedings 2631, pp. 68-78. 2020.; 4 Kozarik, J., Gasparek, K., Zavodnik, T., Cernaj, L., Jagelka, M., & Donoval, M. (2022). ´ Multi-sensor modular IoT platform for high-density monitoring of environmental parameters. In 2022 14th International Conference on Advanced Semiconductor Devices and Microsystems (ASDAM) (pp. 1–4). https://doi.org/10.1109/ ASDAM55965.2022.9966783.; 5 Haggag, M., Ezzeldin, M., El-Dakhakhni, W., & Hassini, E. (2020). Resilient cities critical infrastructure interdependence: A meta-research. Sustainable and Resilient Infrastructure, 7, 291–312.; 6 Bodnarchuk I., Duda O., Kharchenko A., Kunanets N., Matsiuk O., Pasichnyk V. Choice method of analytical information-technology platform for projects associated to the smart city class. ICTERI 2020 ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer Proceedings of the 14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume I: Main Conference р.317-330.; 7 Nunavath, V., & Prinz, A. (2017). Data sources handling for emergency management: Supporting information availability and accessibility for emergency responders. In Human Interface and the Management of Information: Supporting Learning, Decision- Making and Collaboration (pp. 240–259). Springer International Publishing. https:// doi.org/10.1007/978-3-319-58524-6_21.; 8 Li, W., Batty, M., & Goodchild, M. F. (2020). Real-time GIS for smart cities. International Journal of Geographical Information Science, 34, 311–324.; 9 Costa, D. G., Peixoto, J. P. J., Jesus, T. C., Portugal, P., Vasques, F., Rangel, E., & Peixoto, M. (2022). A survey of emergencies management systems in smart cities. IEEE Access, 10, 61843–61872.; 10 Damaˇseviˇcius, R., Bacanin, N., & Misra, S. (2023). From sensors to safety: Internet of Emergency Services (IoES) for emergency response and disaster management. Journal of Sensor and Actuator Networks, 12, 41.; 11 Zaimen, K., Brahmia, M.-E.-A., Moalic, L., Abouaissa, A., & Idoumghar, L. (2022). A survey of artificial intelligence based WSNs deployment techniques and related objectives modeling. IEEE Access, 10, 113294–113329.; 12 Peixoto, João Paulo Just, et al. "Exploiting geospatial data of connectivity and urban infrastructure for efficient positioning of emergency detection units in smart cities." Computers, Environment and Urban Systems 107 (2024): 102054.; 13 Peixoto, J. P. J., Costa, D. G., da Franca Rocha, W. D. J. S., Portugal, P., & Vasques, F. (2023a). On the positioning of emergencies detection units based on geospatial data of urban response centres. Sustainable Cities and Society, 97, Article 104713.; 14 Adeleke, J., Moodley, D., Rens, G., & Adewumi, A. (2017). Integrating statistical machine learning in a semantic sensor web for proactive monitoring and control. Sensors, 17, 807.; 15 Gharaibeh, A., Salahuddin, M. A., Hussini, S. J., Khreishah, A., Khalil, I., Guizani, M., & Al-Fuqaha, A. (2017). Smart cities: A survey on data management, security, and enabling technologies. IEEE Communications Surveys & Tutorials, 19, 2456–2501.; 16 Fedele, R., & Merenda, M. (2020). An IoT system for social distancing and emergency management in smart cities using multi-sensor data. Algorithms, 13.; 17 Huang, H., Yao, X. A., Krisp, J. M., & Jiang, B. (2021). Analytics of location-based big data for smart cities: Opportunities, challenges, and future directions. Computers, Environment and Urban Systems, 90, Article 101712.; 18 Alablani, I., & Alenazi, M. (2020). EDTD-SC: An IoT sensor deployment strategy for smart cities. Sensors, 20, 7191.; 19 Costa, D., Damasceno, A., & Silva, I. (2019). CitySpeed: A crowdsensing-based integrated platform for general-purpose monitoring of vehicular speeds in smart cities, smart. Cities, 2, 46–65.; 20 Madamori, O., Max-Onakpoya, E., Erhardt, G., & Baker, C. (2021). Enabling opportunistic low-cost smart cities by using tactical edge node placement. In , 2021. 16th Conference on Wireless On-Demand Network Systems and Services, WONS (pp. 1–8). https://doi.org/10.23919/WONS51326.2021.9415579; 21 Kamienski, C., Ratusznei, J., Trindade, A., & Cavalcanti, D. (2020). Profiling of a large- scale municipal wireless network. Wireless Networks, 26, 5223.; 22 Yang, T., Lee, S.-H., & Park, S. (2021). AI-aided individual emergency detection system in edge-internet of things environments. Electronics, 10, 2374.; 23 Kyrkou, C., Kolios, P., Theocharides, T., & Polycarpou, M. (2022). Machine learning for emergency management: a survey and future outlook. Proceedings of the IEEE, 1–23.; 24 Zaidi, S. A. R., Hayajneh, A. M., Hafeez, M., & Ahmed, Q. Z. (2022). Unlocking edge intelligence through tiny machine learning (TinyML). IEEE Access, 10, 100867–100877.; 25 Jesus, T. C., Costa, D. G., & Portugal, P. (2018). On the computing of area coverage by visual sensor networks: assessing performance of approximate and precise algorithms. In 16th IEEE International Conference on Industrial Informatics (INDIN) (pp. 193–198). https://doi.org/10.1109/INDIN.2018.8471997; 26 Masatu, E., Sinde, R., & Sam, A. (2022). Development and testing of road signs alert system using a smart mobile phone. Journal of Advanced Transportation, 2022.; 27 Peixoto, J. P. J., Costa, D. G., da Franca Rocha, W. J. S., Portugal, P., & Vasques, F. (2023b). Cityzones: A geospatial multi-tier software tool to compute urban risk zones. SoftwareX, 23, Article 101409.; 28 Kontokosta, C. E., & Malik, A. (2018). The resilience to emergencies and disasters index: Applying big data to benchmark and validate neighborhood resilience capacity. Sustainable Cities and Society, 36, 272–285.; 29 Arvin, M., Beiki, P., Hejazi, S. J., Sharifi, A., & Atashafrooz, N. (2023). Assessment of infrastructure resilience in multi-hazard regions: A case study of Khuzestan province. International Journal of Disaster Risk Reduction, 88, Article 103601.; 30 Khoufi, I., Minet, P., Laouiti, A., & Mahfoudh, S. (2017). Survey of deployment algorithms in wireless sensor networks: Coverage and connectivity issues and challenges. International Journal of Autonomous and Adaptive Communications Systems, 10, 341–390.; 31 Jesus, T. C., Costa, D. G., Portugal, P., Vasques, F., & Ferreira Júnior, W. A. (2023).; 32 Caratù, M., Pigliautile, I., Piselli, C., & Fabiani, C. (2023). A perspective on managing cities and citizens’ well-being through smart sensing data. Environmental Science & Policy, 147, 169–176.; 33 Ang, L.-M., Seng, K. P., Zungeru, A. M., & Ijemaru, G. K. (2017). Big sensor data systems for smart cities. IEEE Internet of Things Journal, 4, 1259–1271.; 34 Wang, X., Wang, C., & Shi, J. (2023). Evaluation of urban resilience based on service- connectivity-environment (sce) model: A case study of jinan city, China. International Journal of Disaster Risk Reduction, 95, Article 103828.; 35 Бедрій, Я. І. Безпека життєдіяльності [Текст] : навч. посіб. : рек. МОН України як навч. посібник для студ. ВНЗ / Я. І. Бедрій. – К. : Кондор, 2009. – 284, [2] с. : іл., табл. – Бібліогр.: с. 285.; 36 Дейнека, Людмила Панасівна. "Безпека життєдіяльності та охорона праці." (2019).; 37 Безпека в надзвичайних ситуаціях. Методичний посібник для здобувачів освітнього ступеня «магістр» всіх спеціальностей денної та заочної (дистанційної) форм навчання / укл.: Стручок В. С. Тернопіль: ФОП Паляниця В. А., 2022. 156 с.; 38 Контроль за станом охорони праці на підприємстві. https://pro-op.com.ua/article/262-qqq-16-m1-11-01-2016-kontrol-za-stanom-ohoroni-prats-na-pdprimstv.; 39 Як контролювати стан охорони праці на підприємстві: основні кроки у поміч. https://nov-rada.gov.ua/2021/06/18/iak-kontroliuvaty-stan-okhorony-pratsi-na-pidpryiemstvi-osnovni-kroky-u-pomich/; http://elartu.tntu.edu.ua/handle/lib/45460
Verfügbarkeit: http://elartu.tntu.edu.ua/handle/lib/45460
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Autoren: et al.
Quelle: International Congress on Environmental Modelling and Software
Schlagwörter: service-based modelling, data management, geospatial data processing, hydrological modelling, decision support, Civil Engineering, Data Storage Systems, Environmental Engineering, Hydraulic Engineering, Other Civil and Environmental Engineering
Dateibeschreibung: application/pdf
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