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Titel:

A Hierarchical Deep-Learning Approach for Rapid Windthrow Detection on PlanetScope and High-Resolution Aerial Image Data

Dokumenttyp:
Zeitschriftenaufsatz
Autor(en):
Deigele, Wolfgang; Brandmeier, Melanie; Straub, Christoph
Abstract:
Forest damage due to storms causes economic loss and requires a fast response to prevent further damage such as bark beetle infestations. By using Convolutional Neural Networks (CNNs) in conjunction with a GIS, we aim at completely streamlining the detection and mapping process for forest agencies. We developed and tested different CNNs for rapid windthrow detection based on PlanetScope satellite data and high-resolution aerial image data. Depending on the meteorological situation after the stor...     »
Stichworte:
GISTop_SpatialModelingAndAlgorithms
Zeitschriftentitel:
Remote Sensing
Jahr:
2020
Band / Volume:
12
Quartal:
3. Quartal
Monat:
Jul
Heft / Issue:
13
Nachgewiesen in:
Scopus; Web of Science
Reviewed:
ja
Sprache:
en
Volltext / DOI:
doi:10.3390/rs12132121
Verlag / Institution:
MDPI AG
E-ISSN:
2072-4292
Status:
Verlagsversion / published
Publikationsdatum:
02.07.2020
Semester:
SS 20
TUM Einrichtung:
Lehrstuhl für Geoinformatik
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