Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Tempelmeier, Nicolas"'
Publikováno v:
SIGWEB Newsl., Winter, Article 4 (Winter 2022), 8 pages
Geographic data plays an essential role in various Web, Semantic Web and machine learning applications. OpenStreetMap and knowledge graphs are critical complementary sources of geographic data on the Web. However, data veracity, the lack of integrati
Externí odkaz:
http://arxiv.org/abs/2302.08823
The transition from conventional mobility to electromobility largely depends on charging infrastructure availability and optimal placement.This paper examines the optimal placement of charging stations in urban areas. We maximise the charging infrast
Externí odkaz:
http://arxiv.org/abs/2206.06011
Autor:
Tempelmeier, Nicolas, Demidova, Elena
Publikováno v:
SIGSPATIAL 2021
OpenStreetMap is a unique source of openly available worldwide map data, increasingly adopted in real-world applications. Vandalism detection in OpenStreetMap is critical and remarkably challenging due to the large scale of the dataset, the sheer num
Externí odkaz:
http://arxiv.org/abs/2203.11087
Autor:
Tempelmeier, Nicolas, Demidova, Elena
Publikováno v:
Proceedings of The Webconference 2022
OpenStreetMap (OSM), a collaborative, crowdsourced Web map, is a unique source of openly available worldwide map data, increasingly adopted in Web applications. Vandalism detection is a critical task to support trust and maintain OSM transparency. Th
Externí odkaz:
http://arxiv.org/abs/2201.10406
Publikováno v:
30th ACM International Conference on Information and Knowledge Management (CIKM 2021)
OpenStreetMap is a rich source of openly available geographic information. However, the representation of geographic entities, e.g., buildings, mountains, and cities, within OpenStreetMap is highly heterogeneous, diverse, and incomplete. As a result,
Externí odkaz:
http://arxiv.org/abs/2109.10036
Publikováno v:
30th ACM International Conference on Information and Knowledge Management (CIKM 2021)
OpenStreetMap (OSM) is currently the richest publicly available information source on geographic entities (e.g., buildings and roads) worldwide. However, using OSM entities in machine learning models and other applications is challenging due to the l
Externí odkaz:
http://arxiv.org/abs/2108.13092
Publikováno v:
24th IEEE International Conference on Intelligent Transportation Systems - ITSC2021
With an increasing number of electric vehicles, the accurate forecasting of charging station occupation is crucial to enable reliable vehicle charging. This paper introduces a novel Deep Fusion of Dynamic and Static Information model (DFDS) to effect
Externí odkaz:
http://arxiv.org/abs/2108.12352
OpenStreetMap (OSM) is one of the richest openly available sources of volunteered geographic information. Although OSM includes various geographical entities, their descriptions are highly heterogeneous, incomplete, and do not follow any well-defined
Externí odkaz:
http://arxiv.org/abs/2107.13257
Publikováno v:
ISPRS International Journal of Geo-Information 2021, 10(4)
The discovery of spatio-temporal dependencies within urban road networks that cause Recurrent Congestion (RC) patterns is crucial for numerous real-world applications, including urban planning and scheduling of public transportation services. While m
Externí odkaz:
http://arxiv.org/abs/2107.09554
Autor:
Tempelmeier, Nicolas, Demidova, Elena
Publikováno v:
Future Generation Computer Systems 116 (2021) 349-364
Representations of geographic entities captured in popular knowledge graphs such as Wikidata and DBpedia are often incomplete. OpenStreetMap (OSM) is a rich source of openly available, volunteered geographic information that has a high potential to c
Externí odkaz:
http://arxiv.org/abs/2011.05841