@article{GrafMorenodelasHerasRuizetal.2018,
  author    = {Graf, Lukas and Moreno-de-las-Heras, Mariano and Ruiz, Maurici and Calsamiglia, Aleix and Garc{\´i}a-Comendador, Juli{\´a}n and Fortesa, Josep and L{\´o}pez-Taraz{\´o}n, Jos{\´e} A. and Estrany, Joan},
  title     = {Accuracy assessment of digital terrain model dataset sources for hydrogeomorphological modelling in small mediterranean catchments},
  series = {Remote sensing},
  volume    = {10},
  journal   = {Remote sensing},
  number    = {12},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2072-4292},
  doi       = {10.3390/rs10122014},
  pages     = {26},
  year      = {2018},
  abstract  = {Digital terrain models (DTMs) are a fundamental source of information in Earth sciences. DTM-based studies, however, can contain remarkable biases if limitations and inaccuracies in these models are disregarded. In this work, four freely available datasets, including Shuttle Radar Topography Mission C-Band Synthetic Aperture Radar (SRTM C-SAR V3 DEM), Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Map (ASTER GDEM V2), and two nationwide airborne light detection and ranging (LiDAR)-derived DTMs (at 5-m and 1-m spatial resolution, respectively) were analysed in three geomorphologically contrasting, small (3-5 km2) catchments located in Mediterranean landscapes under intensive human influence (Mallorca Island, Spain). Vertical accuracy as well as the influence of each dataset's characteristics on hydrological and geomorphological modelling applicability were assessed by using ground-truth data, classic geometric and morphometric parameters, and a recently proposed index of sediment connectivity. Overall vertical accuracy—expressed as the root mean squared error (RMSE) and normalised median deviation (NMAD)—revealed the highest accuracy for the 1-m (RMSE = 1.55 m; NMAD = 0.44 m) and 5-m LiDAR DTMs (RMSE = 1.73 m; NMAD = 0.84 m). Vertical accuracy of the SRTM data was lower (RMSE = 6.98 m; NMAD = 5.27 m), but considerably higher than for the ASTER data (RMSE = 16.10 m; NMAD = 11.23 m). All datasets were affected by systematic distortions. Propagation of these errors and coarse horizontal resolution caused negative impacts on flow routing, stream network, and catchment delineation, and to a lower extent, on the distribution of slope values. These limitations should be carefully considered when applying DTMs for catchment hydrogeomorphological modelling.},
  language  = {en}
}
@article{GrafLaskowskiPapsdorfetal.2022,
  author    = {Graf, Martin and Laskowski, Lukas and Papsdorf, Florian and Sold, Florian and Gremmelspacher, Roland and Naumann, Felix and Panse, Fabian},
  title     = {Frost: a platform for benchmarking and exploring data matching results},
  series = {Proceedings of the VLDB Endowment},
  volume    = {15},
  journal   = {Proceedings of the VLDB Endowment},
  number    = {12},
  publisher = {Association for Computing Machinery},
  address   = {New York},
  issn      = {2150-8097},
  doi       = {10.14778/3554821.3554823},
  pages     = {3292 -- 3305},
  year      = {2022},
  abstract  = {"Bad" data has a direct impact on 88\% of companies, with the average company losing 12\% of its revenue due to it. Duplicates - multiple but different representations of the same real-world entities are among the main reasons for poor data quality, so finding and configuring the right deduplication solution is essential. Existing data matching benchmarks focus on the quality of matching results and neglect other important factors, such as business requirements. Additionally, they often do not support the exploration of data matching results. To address this gap between the mere counting of record pairs vs. a comprehensive means to evaluate data matching solutions, we present the Frost platform. It combines existing benchmarks, established quality metrics, cost and effort metrics, and exploration techniques, making it the first platform to allow systematic exploration to understand matching results. Frost is implemented and published in the open-source application Snowman, which includes the visual exploration of matching results, as shown in Figure 1.},
  language  = {en}
}