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igroy06
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imcc_hungary_15_18

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geography
landcover
CLMS
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x
float64
4.7M
5.3M
y
float64
2.5M
2.9M
imperviousness
uint8
1
255
source_file
stringclasses
17 values
class_label
stringclasses
7 values
rgb
listlengths
3
3
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Dataset Card for Imperviousness Classified Change (2015–2018) – Hungary (Tabular Form)

Description

This dataset is a tabularized version of the Copernicus Land Monitoring Service (CLMS) Imperviousness Classified Change (IMCC) 2015–2018 layer for Hungary.
The original raster tiles at 20 m resolution have been converted into a single pandas DataFrame (full_df), where each row represents a pixel with its geospatial coordinates, imperviousness class value, descriptive label, and RGB color for visualization.

This format allows easy analysis with Python (pandas, scikit-learn, etc.), making the dataset suitable for machine learning, classification, and geospatial studies without requiring raster-specific libraries.

Date Information

What the IMCC 2015–2018 dataset represents-

It’s a classified change layer, not a yearly time series.Each pixel encodes whether imperviousness changed between 2015 and 2018, and possibly the type of change (increase, decrease, stable). The TIFFs themselves have no time dimension per pixel — the whole dataset is just a spatial snapshot of change across a fixed period.

So, do the dates matter?

Yes, for context/metadata: Adding start_date and end_dateis useful when you later merge this dataset with others (e.g., soil, climate, socio-economic data). It tells you that these imperviousness values summarize that 3-year window.

No, for pixel-level analysis: Since every pixel shares the same start/end dates, it doesn’t add extra information at the row level. It’s essentially metadata that applies to the whole DataFrame.

The important dates - the start & end dates refer to the observation period when this data was collected.

Field Value
dataset IMCC_1518_020m
start_date 2015-07-17
end_date 2018-08-16
publication_date 2020-07-10

Dataset Structure

Columns

Column Description
x X coordinate (ETRS89-LAEA projection, meters)
y Y coordinate (ETRS89-LAEA projection, meters)
imperviousness Pixel value (integer code from CLMS symbology)
source_file Name of the raster tile (.tif) the pixel came from
class_label Human-readable description of the imperviousness change
rgb Tuple (R,G,B) color value for plotting the class

Classes

Value Label RGB (R,G,B)
0 Unchanged areas (IMD=0%) (240,240,240)
1 New cover (255,0,0)
2 Loss of cover (0,100,0)
10 Unchanged areas (IMD>0% both years) (156,156,156)
11 Increased IMD (255,191,0)
12 Decreased IMD (64,178,0)
254 Unclassifiable (255,0,255)
255 Outside area (0,0,0)

Size

  • Resolution: 20 m pixels
  • Geographic extent: Hungary (ETRS89-LAEA projection)
  • DataFrame size: depends on raster coverage (typically tens of millions of rows)

Intended Uses

  • Urban expansion analysis
  • Soil sealing and environmental studies
  • Land cover / land use change detection
  • Training ML models (classification, clustering, spatial prediction) on tabular geospatial data
Downloads last month
7
Size of downloaded dataset files:
39.3 MB
Size of the auto-converted Parquet files:
39.3 MB
Number of rows:
29,616,769