6.1.3. The Testing Space

For testing, our input data were a 128 × 128 × 9 × 10 tensor; i.e., 10 different scenarios for pixel-wise classification, whose results are shown in Table 3. That is, the framework classifies 128 × 128 × 10 = 163, 840 pixels.

### 6.1.4. Downloading Data

Due to the big size of the data, format npy was used. Data are available in the link Dataset.


Code will be delivered by the corresponding author upon request for research purposes only.

### *6.2. Classes*

The CNNMSI dataset has been semi-manually labeled for supervised semantic segmentation of *C* = 5 classes; vegetation, water, cloud, cloud shadow, and soil. These classes were selected according to their impact in RS research areas such as agriculture, forest monitoring, population growth analysis, and disaster prevention. It is worth mentioning that the detection of clouds and cloud shadows is an important prerequisite for almost all RS applications.
