Identification of potential landslide in Jianzha county based on InSAR and deep learning
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
1 Online-Ressource.
- Sprache
-
Englisch
- Erschienen in
-
Identification of potential landslide in Jianzha county based on InSAR and deep learning ; volume:14 ; number:1 ; day:12 ; month:9 ; year:2024 ; pages:1-15 ; date:12.2024
Scientific reports ; 14, Heft 1 (12.9.2024), 1-15, 12.2024
- Urheber
-
Yang, Xianwu
Chen, Dannuo
Dong, Yihang
Xue, Yamei
Qin, Kexin
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
-
10.1038/s41598-024-72391-2
- URN
-
urn:nbn:de:101:1-2411292112103.521187084227
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:31 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Yang, Xianwu
- Chen, Dannuo
- Dong, Yihang
- Xue, Yamei
- Qin, Kexin
- SpringerLink (Online service)
Ähnliche Objekte (12)
Identify Landslide Precursors from Time Series InSAR Results
Earthquake-induced landslide monitoring and survey by means of InSAR
The challenge to use multi-temporal InSAR for landslide early warning
Geomorphological assessment as basic complement of InSAR analysis for landslide processes understanding
Landslide observation and volume estimation in central Georgia based on L-band InSAR
Correction to: The challenge to use multi-temporal InSAR for landslide early warning
Combined DEM Extration Method from StereoSAR and InSAR
Landslide displacement prediction using time series InSAR with combined LSTM and TCN: application to the Xiao Andong landslide, Yunnan Province, China
Enhancing infrastructure management with InSAR monitoring
Flood Mapping Using InSAR Coherence Map
A simple method to help determine landslide susceptibility from spaceborne InSAR data: the Montescaglioso case study
Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan
Identify Landslide Precursors from Time Series InSAR Results
Earthquake-induced landslide monitoring and survey by means of InSAR
The challenge to use multi-temporal InSAR for landslide early warning
Geomorphological assessment as basic complement of InSAR analysis for landslide processes understanding
Landslide observation and volume estimation in central Georgia based on L-band InSAR
Correction to: The challenge to use multi-temporal InSAR for landslide early warning
Combined DEM Extration Method from StereoSAR and InSAR
Landslide displacement prediction using time series InSAR with combined LSTM and TCN: application to the Xiao Andong landslide, Yunnan Province, China
Enhancing infrastructure management with InSAR monitoring
Flood Mapping Using InSAR Coherence Map
A simple method to help determine landslide susceptibility from spaceborne InSAR data: the Montescaglioso case study
Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan
Identify Landslide Precursors from Time Series InSAR Results
Earthquake-induced landslide monitoring and survey by means of InSAR
The challenge to use multi-temporal InSAR for landslide early warning
Geomorphological assessment as basic complement of InSAR analysis for landslide processes understanding
Landslide observation and volume estimation in central Georgia based on L-band InSAR
Correction to: The challenge to use multi-temporal InSAR for landslide early warning
Combined DEM Extration Method from StereoSAR and InSAR
Landslide displacement prediction using time series InSAR with combined LSTM and TCN: application to the Xiao Andong landslide, Yunnan Province, China
Enhancing infrastructure management with InSAR monitoring
Flood Mapping Using InSAR Coherence Map
A simple method to help determine landslide susceptibility from spaceborne InSAR data: the Montescaglioso case study