Kwaliteitsdocument van de Geomorfologische kaart van Nederland

Meij, Marijn van der; Soil, water Alterra; Maas, G.J.; Rc, Pe

doi:10.18174/538255

Cited by 7 publications

(9 citation statements)

References 1 publication

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“…This shows that conventions about typical relief of geomorphological units are not always valid, and field validation and continuous discussion should be an essential part of the protocol for large mapping campaigns to avoid mistakes due to wrong assumptions and mapping habits (Knight et al, 2011). These points were also included in the mapping protocol for the geomorphological map of The Netherlands (Van der Meij & Maas, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…The mapping process was initiated with an introductory workshop, where the principles of the geomorphological map, its legend and the available data were presented, and the mapping protocol (Van der Meij & Maas, 2020) was discussed. Each team member subsequently received a data package to use during the mapping, consisting of the DEM and hillshade map of the entire Utrechtse Heuvelrug, and the 2019 geomorphological map of the training area, excluding the test area.…”

Section: Methodsmentioning

confidence: 99%

“…The findings from the semi‐automated mapping procedure were not included in this map. The manual maps and their quality, presented in this study, are incorporated into the quality document of the Dutch geomorphological map as well (Van der Meij & Maas, 2020).…”

Section: Methodsmentioning

confidence: 99%

“…The geomorphological map of The Netherlands was mostly developed in the 1960s–1990s through field surveys, soil corings and low‐resolution elevation measurements (Koomen & Maas, 2004). The map is currently being updated using detailed digital elevation data and high‐resolution subsurface information (Van der Meij & Maas, 2020). The map is part of the Key Registry Subsurface in The Netherlands.…”

Section: Dutch Geomorphological Map and Study Areamentioning

confidence: 99%

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Comparing geomorphological maps made manually and by deep learning

Meij

Meijles

Marcos

et al. 2022

Earth Surf Processes Landf

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Geomorphological maps provide information on the relief, genesis and shape of the earth's surface and are widely used in sustainable spatial developments. The quality of geomorphological maps is however rarely assessed or reported, which limits their applicability. Moreover, older geomorphological maps often do not meet current quality requirements and require updating. This updating is time-consuming and because of its qualitative nature difficult to reproduce, but can be supported by novel computational methods. In this paper, we address these issues by (1) quantifying the uncertainty associated with manual geomorphological mapping, (2) exploring the use of convolutional neural networks (CNNs) for semi-automated geomorphological mapping and (3) testing the sensitivity of CNNs to uncertainties in manually created evaluation data.We selected a test area in the Dutch push-moraine district with a pronounced relief and a high variety of landforms. For this test area we developed five manually created geomorphological maps and 27 automatically created landform maps using CNNs. The resulting manual maps are similar on a regional level. We could identify the causes of disagreement between the maps on a local level, which often related to differences in mapping experience, choices in delineation and different interpretations of the legend. Coordination of mapping efforts and field validation are necessary to create accurate and precise maps. CNNs perform well in identifying landforms and geomorphological units, but fail at correct delineation. The human geomorphologist remains necessary to correct the delineation and classification of the computed maps. The uncertainty in the manually created data that are used to train and evaluate CNNs have a large effect on the model performance and evaluation. This also advocates for coordinated mapping efforts to ensure the quality of manually created training and test data. Further model development and data processing are required before CNNs can act as standalone mapping techniques.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Dutch Geomorphological Map and Study Areamentioning

confidence: 99%

See 2 more Smart Citations

Comparing geomorphological maps made manually and by deep learning

Meij

Meijles

Marcos

et al. 2022

Earth Surf Processes Landf

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“…In Tabel 2 staat de verdeling van de oppervlakten over de verschillende bodemtypen met veen in de beekdalen in het studiegebied. De ruimtelijke selectie is gebaseerd op de dalvormen uit de geomorfologische kaart van Nederland (Koomen & Maas, 2004;Van der Meij & Maas 2020;Maas et al, 2021). Het grootste deel van de oppervlakte (50%) wordt ingenomen door veengronden (.V..).…”

Section: Veengronden In Beekdalenunclassified

Maatregelen reductie CO2-uitstoot niet-klassieke veengronden Groningen/Drenthe

Bakema

Heidema

Groot

et al. 2022

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Determinants of informal care time, distress, depression, and quality of life in care partners along the trajectory of Alzheimer's disease

Mank

Maurik

Rijnhart

et al. 2023

Alz & Dem Diag Ass & Dis Mo

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Introduction We evaluated determinants associated with care partner outcomes along the Alzheimer's disease (AD) stages. Methods We included n = 270 care partners of amyloid‐positive patients in the pre‐dementia and dementia stages of AD. Using linear regression analysis, we examined determinants of four care partner outcomes: informal care time, caregiver distress, depression, and quality of life (QoL). Results More behavioral symptoms and functional impairment in patients were associated with more informal care time and depressive symptoms in care partners. More behavioral symptoms were related with more caregiver distress. Spouse care partners spent more time on informal care and QoL was lower in female care partners. Behavioral problems and subtle functional impairment of the patient predisposed for worse care partner outcomes already in the pre‐dementia stages. Discussion Both patient and care partner determinants contribute to the care partner outcomes, already in early disease stages. This study provides red flags for high care partner burden.

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Kwaliteitsdocument van de Geomorfologische kaart van Nederland

Cited by 7 publications

References 1 publication

Comparing geomorphological maps made manually and by deep learning

Comparing geomorphological maps made manually and by deep learning

Maatregelen reductie CO2-uitstoot niet-klassieke veengronden Groningen/Drenthe

Determinants of informal care time, distress, depression, and quality of life in care partners along the trajectory of Alzheimer's disease

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