Datasets:
license: apache-2.0
language:
- en
tags:
- geography
- landcover
- CLMS
- rastar-to-tabular
size_categories:
- 10M<n<100M
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