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Automated processing for map generalization using web services.

, , and . GeoInformatica, 13 (4): 425-452 (2009)

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Asynchronous Federated Learning for Geospatial Applications., , , , , , and . DMLE/IOTSTREAMING@PKDD/ECML, volume 967 of Communications in Computer and Information Science, page 21-28. Springer, (2018)WebDiP - A tool for information search experiments on the World-Wide-Web, and . Behavior Research Methods, 37 (2): 293-300 (2005)Web based Decsion Process tracing and mining. University of Fribourg, (2004)Traffic4cast at NeurIPS 2020 ? yet more on theunreasonable effectiveness of gridded geo-spatial processes., , , , , , , , , and 9 other author(s). NeurIPS (Competition and Demos), volume 133 of Proceedings of Machine Learning Research, page 325-343. PMLR, (2020)Traffic4cast at NeurIPS 2021 - Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes., , , , , , , , , and 27 other author(s). NeurIPS (Competition and Demos), volume 176 of Proceedings of Machine Learning Research, page 97-112. PMLR, (2021)Traffic4cast at NeurIPS 2022 - Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors., , , , , , , , , and 20 other author(s). CoRR, (2023)Traffic4cast at NeurIPS 2021 - Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes., , , , , , , , , and 27 other author(s). CoRR, (2022)The surprising efficiency of framing geo-spatial time series forecasting as a video prediction task - Insights from the IARAI Traffic4cast Competition at NeurIPS 2019., , , , , , , , and . NeurIPS (Competition and Demos), volume 123 of Proceedings of Machine Learning Research, page 232-241. PMLR, (2019)Traffic4cast at NeurIPS 2022 – Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors., , , , , , , , , and 20 other author(s). NeurIPS (Competition and Demos), volume 220 of Proceedings of Machine Learning Research, page 251-278. PMLR, (2021)Profiling and Improving the PyTorch Dataloader for high-latency Storage: A Technical Report., , , , and . CoRR, (2022)