Using high-resolution imagery and deep learning to classify land-use following deforestation: a case study in Ethiopia
National-scale assessments of post-deforestation land-use are crucial for decreasing deforestation and forest degradation-related emissions. In this research, we assess the potential of different satellite data modalities (single-date, multi-date, multi-resolution, and an ensemble of multi-sensor im...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Informa UK Limited
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/125031 |
Similar Items: Using high-resolution imagery and deep learning to classify land-use following deforestation: a case study in Ethiopia
- Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series
- Forest cover mapping in post-Soviet Central Asia using multi-resolution remote sensing imagery
- Investigating the use of high resolution multi-spectral satellite imagery for crop mapping in Nigeria crop and landuse classification using WorldView-3 high resolution multispectral imagery and LANDSAT8 data
- Land use mapping of selected sites in the Cambodia, Mekong Mega-Delta using high resolution satellite imagery
- Cropping systems mapping through high resolution imagery
- MusaDeepMosaic: Development of a machine learning genomic mosaic classifier tool.