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Deep learning model to generate cloud masks from Sentinel-2 imagery. A brief summary of the item is not available. Add a brief summary about the item.

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Item created: Jul 25, 2022 Item updated: Dec 31, 2024 Number of downloads: 5,408

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Description

Satellite imagery has several applications, including land use and land cover classification, change detection, object detection, etc. Satellite based remote sensing sensors often encounter cloud coverage due to which clear imagery of earth is not collected. The clouded regions should be excluded, or cloud removal algorithms must be applied, before the imagery can be used for analysis. Most of these preprocessing steps require a cloud mask. In case of single-scene imagery, though tedious, it is relatively easy to manually create a cloud mask. However, for a larger number of images, an automated approach for identifying clouds is necessary. This model can be used to automatically generate a cloud mask from Sentinel-2 imagery.

Using the model
Follow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.

Fine-tuning the model
This model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.

Input
Sentinel-2 L2A imagery in the form of a raster, mosaic dataset or image service.

Output
Classified raster containing three classes:  Low density, Medium density and High density.

Applicable geographies
This model is expected to work well in Europe and the United States. This model works well for land based areas. Large water bodies such as ocean, seas and lakes should be avoided.

Model architecture
This model uses the UNet model architecture implemented in ArcGIS API for Python.

Accuracy metrics
This model has an overall accuracy of 94 percent with L2A imagery. The table below summarizes the precision, recall and F1-score of the model on the validation dataset.  The comparatively low precision, recall and F1 score for Low density clouds might cause false detection of such clouds in certain urban areas. Also, for certain seasonal clouds some extremely bright pixels might be missed out.

ClassPrecisionRecallF1 score
High density0.9600.9750.968
Medium density0.9050.8970.901
Low density0.7740.5710.657

Sample results
Here are a few results from the model.

pred_cloud1

clpred_cloud2_with_snow

pred_cloud3

cloud_pred4

cloud_pred5


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          Size: 146.601 MB

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          ID: 1e1ec9602f4743108708ccdf362e3c48

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          LivingAtlasDLPK, dlpk, cloud removal, cloud mask, sentinel-2, detect cloud

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