Implememnation of Deep Learning models in keras.
SegNet architecture (Badrinarayanan et al. 2015) for image segmentation.
DeepVel architecture (Asensio Ramos, Requerey & Vitas 2017) for regression.
2D Semantic Labeling Contest
The semantic labeling contest of ISPRS provides two state-of-the-art airborne image datasets in Vaihingen and Potsdam, respectively for the detection of urban objects.
Six categories/classes have been defined:
1. Impervious surfaces (RGB: 255, 255, 255)
2. Building (RGB: 0, 0, 255)
3. Low vegetation (RGB: 0, 255, 255)
4. Tree (RGB: 0, 255, 0)
5. Car (RGB: 255, 255, 0)
6. Clutter/background (RGB: 255, 0, 0)
Sentinel-2 cropland mapping
Following the paper by [Belgiu & Csillik (2018)] (see also Hao et al. 2018)(https://www.sciencedirect.com/science/article/pii/S0034425717304686) we are going to train SegNet for the segmentation of the croplands. As an input we can use Sentinel-2 MSI multispectral data, and as an output crop types data classified by experts from the European Land Use and Coverage Area Frame Survey (LUCAS) and CropScape – Cropland Data Layer (CDL), respectively.
Datasets in Google Earth Engine:
- Sentinel-2 MSI: MultiSpectral Instrument, Level-1C
- USDA NASS Cropland Data Layers
- Canada AAFC Annual Crop Inventory
Plantations by type and species
Global Annual Burned Area Mapping
- Keras 2.2.2
- TensorFlow 1.10.0
pip install --upgrade keras
pip install --upgrade tensorflow
pip install --upgrade tensorflow-gpu