Using GIS, Remote Sensing, and Machine Learning to Highlight the Correlation between the Land-Use/Land-Cover Changes and Flash-Flood Potential.
Romulus CostacheQuoc Bao PhamEma Corodescu-RoscaCatalin I. CîmpianuHaoyuan HongNguyen Thi Thuy LinhMing Fai ChowAli Najah AhmedMatej VojtekSiraj Muhammed PandhianiGabriel MineaNicu CiobotaruMihnea Cristian PopaDaniel Constantin DiaconuBinh Thai PhamPublished in: Remote. Sens. (2020)
Keyphrases
- land cover
- remote sensing
- machine learning
- geographic information systems
- remote sensing images
- change detection
- multispectral
- land cover classification
- remote sensing imagery
- satellite data
- satellite images
- remotely sensed images
- remote sensing data
- satellite imagery
- supervised classification
- hyperspectral
- remotely sensed data
- high resolution
- remotely sensed
- geographical information systems
- image analysis
- image processing
- data mining
- hyperspectral imagery
- landsat tm
- pattern recognition
- high spatial resolution
- spatial analysis
- digital image analysis
- urban growth
- climate change
- multispectral images
- earth observation
- environmental conditions
- spatial data
- infrared
- active learning