Sentinel-2 Cloud Mask Annotations With Variability
A Large, Representative Set of 20m Subscenes
@kaggle.thedevastator_sentinel_2_cloud_mask_annotations_with_variabili
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About this Dataset
Sentinel-2 Cloud Mask Annotations With Variability
Sentinel-2 Cloud Mask Annotations with Variability Tags
A Large, Representative Set of 20m Subscenes
By [source]
About this dataset
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How to use the dataset
In this guide, we will cover how to use this dataset and what information can be derived from it.
First, let’s take a look at the columns in the dataset. We have scene name, difficulty level, annotator name, shadows_marked (yes/no), clear percent, cloud percent, shadow percent, dataset type (WorldView 2 or 3), forest/jungle coverage percentage details etc., snow/ice coverage percentage details etc., agricultural coverage percentage details etc., urban/developed coverage percentage details etc., coastal coverage percentage details etc., hills/mountains coverage percentage details etc., desert/barren coverage percentage details etc., shrublands/plains coverage percentage details(etc.), wetland/bog marsh coverage%, open water%, enclosed water%, thin cloud % , thick clouds % , low clouds % , high clouds % , isolated clouds % along with extended cloud type (altocumulus / stratocumulus) cirrus haze / fog , ice_clouds & contrails . All of these columns provide detailed percentages about different types of landcover along with corresponding cloud types & other useful information like annotator name involved in creating annotation for a particular scene .
The data within each column can then be used to derive further insights about any given Sentinel-2 subscene including landcover as well as various associated meteorological events such as precipitation and wind patterns which could enable specific decision-making applications like crop monitoring or urban development tracking in addition to understanding environmental impacts over large areas easily visible through satellite imagery. Furthermore, by analyzing this data combined with other standard atmospheric parameters such as wind speed & direction it is possible to track storm path direction by looking at cyclonic activity predicted by different conditions pertaining to satellite images gathered previously allowing accurate forecasting opportunity .
Research Ideas
- Using the geographical attributes associated with each scene, this dataset can be used to categorize cultures based on their characteristics and geography.
- This dataset can be used to better understand climate data, by looking at how cloud formations are distributed in a region and in relation to weather patterns.
- This dataset can also help with machine learning projects related to object detection, as the cloud patterns and layout of the scenes can be seen as objects that algorithms should try to recognize or identify correctly while training
Acknowledgements
If you use this dataset in your research, please credit the original authors.
Data Source
License
License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.
Columns
File: classification_tags.csv
| Column name | Description |
|---|---|
| scene | Unique identifier for each subscene. (String) |
| difficulty | Difficulty rating of the subscene. (Integer) |
| annotator | Name of the annotator who classified the subscene. (String) |
| shadows_marked | Whether shadows were marked in the subscene. (Boolean) |
| clear_percent | Percentage of clear sky in the subscene. (Float) |
| cloud_percent | Percentage of clouds in the subscene. (Float) |
| shadow_percent | Percentage of shadows in the subscene. (Float) |
| dataset | Dataset the subscene was taken from. (String) |
| forest/jungle | Percentage of forest/jungle in the subscene. (Float) |
| snow/ice | Percentage of snow/ice in the subscene. (Float) |
| agricultural | Percentage of agricultural land in the subscene. (Float) |
| urban/developed | Percentage of urban/developed land in the subscene. (Float) |
| coastal | Percentage of coastal land in the subscene. (Float) |
| hills/mountains | Percentage of hills/mountains in the subscene. (Float) |
| desert/barren | Percentage of desert/barren land in the subscene. (Float) |
| shrublands/plains | Percentage of shrublands/plains in the subscene. (Float) |
| wetland/bog/marsh | Percentage of wetland/bog/marsh in the subscene. (Float) |
| open_water | Percentage of open water in the subscene. (Float) |
| enclosed_water | Percentage of enclosed water in the subscene. (Float) |
| thin | Percentage of thin clouds in the subscene. (Float) |
| thick | Percentage of thick clouds in the subscene. (Float) |
| low | Percentage of low clouds in the subscene. (Float) |
| high | Percentage of high clouds in the subscene. (Float) |
| isolated | Percentage of isolated clouds in the subscene. (Float) |
| extended | Percentage of extended |
| cumulus | Percentage of cumulus clouds in the subscene. (Float) |
| cumulonimbus | Percentage of cumulonimbus clouds in the subscene. (Float) |
| altocumulus/stratocumulus | Percentage of altocumulus/stratocumulus clouds in the subscene. (Float) |
| cirrus | Percentage of cirrus clouds in the subscene. (Float |
| haze/fog | Percentage of haze/fog in the subscene. (Float) |
| ice_clouds | Percentage of ice clouds in the subscene. (Float) |
| contrails | Percentage of contrails in the subscene. (Float) |
Acknowledgements
If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .
Tables
Classification Tags
@kaggle.thedevastator_sentinel_2_cloud_mask_annotations_with_variabili.classification_tags
- 48.69 kB
- 513 rows
- 33 columns
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CREATE TABLE classification_tags (
"index" BIGINT,
"scene" VARCHAR,
"difficulty" BIGINT,
"annotator" VARCHAR,
"shadows_marked" BIGINT,
"clear_percent" DOUBLE,
"cloud_percent" DOUBLE,
"shadow_percent" DOUBLE,
"dataset" VARCHAR,
"forest_jungle" BIGINT,
"snow_ice" BIGINT,
"agricultural" BIGINT,
"urban_developed" BIGINT,
"coastal" BIGINT,
"hills_mountains" BIGINT,
"desert_barren" BIGINT,
"shrublands_plains" BIGINT,
"wetland_bog_marsh" BIGINT,
"open_water" BIGINT,
"enclosed_water" BIGINT,
"thin" BIGINT,
"thick" BIGINT,
"low" BIGINT,
"high" BIGINT,
"isolated" BIGINT,
"extended" BIGINT,
"cumulus" BIGINT,
"cumulonimbus" BIGINT,
"altocumulus_stratocumulus" BIGINT,
"cirrus" BIGINT,
"haze_fog" BIGINT,
"ice_clouds" BIGINT,
"contrails" BIGINT
);