Convolutional Neural Network-Based Detection of Erosion Rills on Aerial Imagery Combined with Hydrological Model SMODERP Outputs
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21110%2F24%3A00381543" target="_blank" >RIV/68407700:21110/24:00381543 - isvavai.cz</a>
Result on the web
<a href="https://talks.osgeo.org/foss4g-2024/talk/U7DXAQ/" target="_blank" >https://talks.osgeo.org/foss4g-2024/talk/U7DXAQ/</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Convolutional Neural Network-Based Detection of Erosion Rills on Aerial Imagery Combined with Hydrological Model SMODERP Outputs
Original language description
Extreme precipitation events lead to rapid surface runoff, causing sheet erosion and the formation of rills as an impact to increase the risk of flash floods. This combination of processes pose a threat to agricultural land and rural areas, as sediment-laden water can infect urban zones, causing damage to infrastructure. Detecting and predicting the formation of erosive rills on agricultural land is, therefore, crucial for effective land management and disaster prevention in rural areas.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
20101 - Civil engineering
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů