Zobrazeno 1 - 10
of 58
pro vyhledávání: '"Kristian Torp"'
Autor:
Christian S. Jensen, Kristian Torp
Publikováno v:
Geoforum Perspektiv, Vol 5, Iss 9 (2012)
Externí odkaz:
https://doaj.org/article/552ac55063c449a4b0f93e81e5ccf8f7
Autor:
Christian S. Jensen, Kristian Torp
Publikováno v:
Geoforum Perspektiv, Vol 5, Iss 9 (2006)
Externí odkaz:
https://doaj.org/article/17fe5fe5e9d8432289a02b51225b8017
Autor:
Pavlos Kranas, Javier Pereira, Fernando García Doval, Diego Burgos, Alejandro Ramiro, Raúl De Pablo, Rogelio Rodriguez, Francisco Nvara, Spencer Pablos, Jose Correas Remedios, Petros Petrou, Stavroula Iatropoulou, Theodora Anastasiou, Ioannis Chrysakis, Theofanis Orphanoudakis, Andreas Zalonis, Myroulla Drakos, Christoforos Pirillos, Christos Doulkeridis, Georgios M. Santipantakis, Nikolaos Koutroumanis, George Makridis, Vasilis Koukos, George S. Theodoropoulos, Yannis Theodoridis, Dimosthenis Kyriazis, Mariana Machado Garcez Duarte, Mahmoud Sakr, Esteban Zimanyi, Carlos Sanchez, Francesco D'Andria, Septimiu Nechifo, Cosmin Grigoras, Juergen Neises, Domenico Messina, Rosario Catelli, Petros Brimos, Kostas Nasias, Tiago Teixeira, Bruno Almeida, Pedro Malo, Nikolay Mehandjiev, Nadia Masood Khan, Matteo Falsetta, Anita Graser, Clemens Heistracher, Kristian Torp, Marcelo Corrales Compagnucci, Sophia Karagiorgou
This is the first of the series of deliverables related to the activities of task T2.1 (“Design of Reference Architecture”). This deliverable specifies the system and software requirements of the MobiSpaces integrated solution, the analysis of th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::30dde15ef29ce6b8e9febd07051ff38a
Autor:
Kristian Torp, Magnus N. Hansen
Publikováno v:
Torp, K & Hansen, M N 2022, Efficient network-constrained trajectory queries . in M Renz, M Sarwat, M A Nascimento, S Shekhar & X Xie (eds), 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2022 ., 92, Association for Computing Machinery, GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, pp. 1-4, 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL GIS 2022, Seattle, United States, 01/11/2022 . https://doi.org/10.1145/3557915.3561028
The large search companies have very clearly shown that full-text search on very large datasets can be executed efficiently. In this paper, we show how querying spatio-temporal trajectory data can be converted to a full-text search problem. This allo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c81e67359deb877633fa2e694db2ec74
https://vbn.aau.dk/da/publications/0714a9b0-f4ab-4fb9-8e2c-f540dbbd8f2b
https://vbn.aau.dk/da/publications/0714a9b0-f4ab-4fb9-8e2c-f540dbbd8f2b
Publikováno v:
Andersen, A S, Christensen, A D, Michaelsen, P, Gjela, S & Torp, K 2021, AIS Data as Trajectories and Heat Maps . in X Meng, F Wang, C-T Lu, Y Huang, S Shekhar & X Xie (eds), 29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021 . Association for Computing Machinery, pp. 431-434, 29th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2021, Virtual, Online, China, 02/11/2021 . https://doi.org/10.1145/3474717.3484208
SIGSPATIAL/GIS
SIGSPATIAL/GIS
All large ships are by international law required to provide their position, speed, and course while sailing. This data is called AIS data. Several maritime organizations make this data freely available. In this paper, we present two approaches to qu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c6ed30729b518878294a4038ad5c6f9
https://vbn.aau.dk/da/publications/4488c2b8-6d94-4245-9764-395834bc2265
https://vbn.aau.dk/da/publications/4488c2b8-6d94-4245-9764-395834bc2265
Autor:
Kasper F. Pedersen, Kristian Torp
Publikováno v:
SSTD
Pedersen, K F & Torp, K 2021, Geolocating Traffic Signs using Large Imagery Datasets . in Proceedings of 17th International Symposium on Spatial and Temporal Databases, SSTD 2021 . Association for Computing Machinery, pp. 34-43, 17th International Symposium on Spatial and Temporal Databases, SSTD 2021, Virtual, Online, United States, 23/08/2021 . https://doi.org/10.1145/3469830.3470900
Pedersen, K F & Torp, K 2021, Geolocating Traffic Signs using Large Imagery Datasets . in Proceedings of 17th International Symposium on Spatial and Temporal Databases, SSTD 2021 . Association for Computing Machinery, pp. 34-43, 17th International Symposium on Spatial and Temporal Databases, SSTD 2021, Virtual, Online, United States, 23/08/2021 . https://doi.org/10.1145/3469830.3470900
Maintaining a database with the type, location, and direction of traffic signs is a labor-intensive part of asset management for many road authorities. Today there are high-quality cameras in cell-phones that can add location (EXIF) metadata to the i
Publikováno v:
SSTD '21: 17th international symposium on spatial and temporal databases: virtual, USA, August 23-25, 2021, New York : Association for Computing Machinery, 2021, p. 85-95
SSTD
Petkevicius, L, Saltenis, S, Civilis, A & Torp, K 2021, Probabilistic Deep Learning for Electric-Vehicle Energy-Use Prediction . in Proceedings of 17th International Symposium on Spatial and Temporal Databases, SSTD 2021 . Association for Computing Machinery, pp. 85-95, 17th International Symposium on Spatial and Temporal Databases, SSTD 2021, Virtual, Online, United States, 23/08/2021 . https://doi.org/10.1145/3469830.3470915
SSTD
Petkevicius, L, Saltenis, S, Civilis, A & Torp, K 2021, Probabilistic Deep Learning for Electric-Vehicle Energy-Use Prediction . in Proceedings of 17th International Symposium on Spatial and Temporal Databases, SSTD 2021 . Association for Computing Machinery, pp. 85-95, 17th International Symposium on Spatial and Temporal Databases, SSTD 2021, Virtual, Online, United States, 23/08/2021 . https://doi.org/10.1145/3469830.3470915
The continued spread of electric vehicles raises new challenges for the supporting digital infrastructure. For example, long-distance route planning for such vehicles relies on the prediction of both the expected travel time as well as energy use. We
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f74b430c6766dd6862e4b30a06632190
https://repository.vu.lt/VU:ELABAPDB102949017&prefLang=en_US
https://repository.vu.lt/VU:ELABAPDB102949017&prefLang=en_US
Autor:
Kasper F. Pedersen, Kristian Torp
Publikováno v:
SIGSPATIAL/GIS
Action cameras and smartphones have made it simple and cheap to collect large imagery datasets from the road network while driving. At the same time, several frameworks, e.g., Detectron2 and the TensorFlow Object Detection API, have made it fairly ea
Publikováno v:
Lecture Notes in Business Information Processing ISBN: 9783030616267
eBISS
eBISS
The volume of GPS data collected from moving vehicles has increased significantly over the last years. We have gone from GPS data being collected every few minutes to data being collected every second. With large quantities of GPS data available it i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1869b2e0fabf1062392700a2202857ce
https://doi.org/10.1007/978-3-030-61627-4_4
https://doi.org/10.1007/978-3-030-61627-4_4
Publikováno v:
Jepsen, T S, Jensen, C S, Nielsen, T D & Torp, K 2018, On Network Embedding for Machine Learning on Road Networks: A Case Study on the Danish Road Network . in Proceedings of the 2018 IEEE International Conference on Big Data . IEEE, pp. 3421-3430, 2018 IEEE International Conference on Big Data, Seattle, Washington, United States, 10/12/2018 . https://doi.org/10.1109/BigData.2018.8622416
IEEE BigData
IEEE BigData
Road networks are a type of spatial network, where edges may be associated with qualitative information such as road type and speed limit. Unfortunately, such information is often incomplete; for instance, OpenStreetMap only has speed limits for 13%
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3b46d268397037eaeb27cb6ed5bc2b8c
http://arxiv.org/abs/1911.06217
http://arxiv.org/abs/1911.06217