Update README.md

README.md

@@ -15,7 +15,7 @@ size_categories:
 
 The hereby FLAIR (#2) dataset is sampled countrywide and is composed of over 20 billion annotated pixels of very high resolution aerial imagery at 0.2 m spatial resolution, acquired over three years and different months (spatio-temporal domains). 
 Aerial imagery patches consist of 5 channels (RVB-Near Infrared-Elevation) and have corresponding annotation (with 19 semantic classes or 13 for the baselines). 
-Furthermore, to integrate broader spatial context and temporal information, high resolution Sentinel-2 1-year time series with 10 spectral band are also provided. 
+Furthermore, to integrate broader spatial context and temporal information, high resolution Sentinel-2 satellite 1-year time series with 10 spectral band are also provided. 
 More than 50,000 Sentinel-2 acquisitions with 10 m spatial resolution are available.
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@@ -168,10 +168,13 @@ The dataset covers 50 spatial domains, encompassing 916 areas spanning 817 km².
 ## Dataset Structure
 
 ### Spatio-Temporal Distribution
-The FLAIR dataset consists of 77 762 patches. Each patch includes a high-resolution aerial image of $0.2$\:m (512x512), a yearly satellite image time series with a spatial resolution of 10m (40x40), and pixel-precise elevation and land cover annotations at 0.2m resolution (512x512).
+The FLAIR dataset consists of 77 762 patches. Each patch includes a high-resolution aerial image (512x512) at 0.2 m, a yearly satellite image time series (40x40 by default) with a spatial resolution of 10 m, 
+and pixel-precise elevation and land cover annotations at 0.2 m resolution (512x512).
+
+<p align="center"><img src="flair-pacthes.png" alt="" style="width:50%;max-width:600px;"/></p><br>
 
 ### Annotations
-Each pixel has been manually annotated by photo-interpretation of the 20cm resolution aerial imagery, carried out by a team supervised by geography experts from the IGN. 
+Each pixel has been manually annotated by photo-interpretation of the 20 cm resolution aerial imagery, carried out by a team supervised by geography experts from the IGN. 
 Movable objects like cars or boats are annotated according to their underlying cover.
 
 ### Training Splits
@@ -182,6 +185,10 @@ Consequently, every split accurately reflects the landscape diversity inherent t
 It is important to mention that the patches come with meta-data permitting alternative splitting schemes, for example focused on domain shifts.
 
 
+<p align="center"><img src="flair-splits.png" alt="" style="width:25%;max-width:600px;"/></p><br>
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+
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 ## Reference
 Please include a citation to the following article if you use the FLAIR dataset: