Datasets:

hiyam-d
/

PrediTree

id string	department_name string	chm array 2D	no_data_percentage float32	crs string	transform string	bounds string	resolution float32	chm_mean_year int16	rgbnir_ndvi_1 array 3D	rgbnir_year_1 uint16	rgbnir_ndvi_2 array 3D	rgbnir_year_2 uint16	rgbnir_ndvi_3 array 3D	rgbnir_year_3 uint16
pas_0650-7035_00_00	pas	[[0.0,0.0,0.0,0.0,0.0,0.0,1.1201171875,1.1201171875,0.0,0.0,0.6201171875,0.0,0.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED)	0	EPSG:2154	\| 0.50, 0.00, 650006.00\| \| 0.00,-0.50, 7034995.25\| \| 0.00, 0.00, 1.00\|	(650006.0, 7034867.25, 650134.0, 7034995.25)	0.5	2,024	[[[137,133,126,115,108,108,110,127,120,129,152,171,176,186,194,202,208,198,184,176,180,182,190,194,1(...TRUNCATED)	2,015	[[[144,161,173,171,173,175,181,183,187,191,197,204,204,195,204,207,203,202,196,195,196,195,192,195,2(...TRUNCATED)	2,018	[[[126,125,109,94,95,106,120,130,140,151,126,133,166,175,197,202,202,202,200,192,193,209,212,214,214(...TRUNCATED)	2,021
pas_0650-7035_00_01	pas	[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.(...TRUNCATED)	0	EPSG:2154	\| 0.50, 0.00, 650134.00\| \| 0.00,-0.50, 7034995.25\| \| 0.00, 0.00, 1.00\|	(650134.0, 7034867.25, 650262.0, 7034995.25)	0.5	2,024	[[[137,141,142,136,123,113,111,112,116,117,115,119,120,121,117,115,123,118,120,120,119,119,117,109,1(...TRUNCATED)	2,015	[[[131,127,121,119,123,127,124,117,110,111,111,111,109,115,117,107,108,115,113,105,102,104,102,111,1(...TRUNCATED)	2,018	[[[48,47,50,51,50,47,46,45,43,44,45,48,48,47,48,49,48,48,49,47,45,46,46,43,44,45,47,48,48,50,48,48,4(...TRUNCATED)	2,021
pas_0650-7035_00_02	pas	[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.(...TRUNCATED)	0	EPSG:2154	\| 0.50, 0.00, 650262.00\| \| 0.00,-0.50, 7034995.25\| \| 0.00, 0.00, 1.00\|	(650262.0, 7034867.25, 650390.0, 7034995.25)	0.5	2,024	[[[107,114,115,117,124,125,125,132,127,122,121,116,119,130,126,126,121,120,110,107,110,109,105,108,1(...TRUNCATED)	2,015	[[[102,103,105,107,108,106,107,110,111,114,119,121,126,131,129,123,119,118,112,117,124,127,128,129,1(...TRUNCATED)	2,018	[[[129,114,113,117,117,113,112,111,109,109,109,110,115,119,116,116,122,122,120,119,106,109,106,105,1(...TRUNCATED)	2,021
pas_0650-7035_00_03	pas	[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.(...TRUNCATED)	0	EPSG:2154	\| 0.50, 0.00, 650390.00\| \| 0.00,-0.50, 7034995.25\| \| 0.00, 0.00, 1.00\|	(650390.0, 7034867.25, 650518.0, 7034995.25)	0.5	2,024	[[[140,143,131,114,115,109,117,109,121,137,142,147,142,139,136,133,133,132,127,137,128,135,133,131,1(...TRUNCATED)	2,015	[[[177,181,183,182,182,178,177,173,170,173,172,170,169,165,164,171,177,179,168,159,164,162,157,167,1(...TRUNCATED)	2,018	[[[60,62,58,58,60,59,61,64,66,67,70,66,71,71,74,85,73,75,71,71,68,78,80,76,78,73,69,68,70,60,59,58,6(...TRUNCATED)	2,021
pas_0650-7035_00_04	pas	[[7.26171875,4.75,4.671875,7.3203125,4.9609375,4.5,2.279296875,1.0595703125,4.9609375,5.1015625,4.92(...TRUNCATED)	0	EPSG:2154	\| 0.50, 0.00, 650518.00\| \| 0.00,-0.50, 7034995.25\| \| 0.00, 0.00, 1.00\|	(650518.0, 7034867.25, 650646.0, 7034995.25)	0.5	2,024	[[[29,39,41,41,56,69,69,55,41,40,43,36,43,49,49,40,44,40,40,39,57,68,80,78,80,84,84,84,54,38,31,49,5(...TRUNCATED)	2,015	[[[51,54,54,45,42,43,42,40,43,53,56,56,46,49,42,40,47,53,60,70,76,90,82,78,83,87,85,85,94,91,80,67,7(...TRUNCATED)	2,018	[[[38,42,58,65,62,54,56,44,34,38,43,60,56,56,56,55,62,60,63,67,65,68,68,53,53,61,67,63,69,62,52,56,5(...TRUNCATED)	2,021
pas_0650-7035_00_05	pas	[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.(...TRUNCATED)	0	EPSG:2154	\| 0.50, 0.00, 650646.00\| \| 0.00,-0.50, 7034995.25\| \| 0.00, 0.00, 1.00\|	(650646.0, 7034867.25, 650774.0, 7034995.25)	0.5	2,024	[[[140,137,139,146,135,142,139,139,141,139,137,135,134,131,137,135,133,135,131,136,137,136,137,134,1(...TRUNCATED)	2,015	[[[98,98,99,100,101,101,104,102,101,98,101,97,96,98,96,99,104,102,100,101,99,98,99,96,98,97,97,96,93(...TRUNCATED)	2,018	[[[40,40,39,40,41,43,48,41,39,39,40,41,43,42,42,45,46,43,48,47,43,44,44,40,40,42,40,40,38,38,38,38,3(...TRUNCATED)	2,021
pas_0650-7035_00_06	pas	[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.(...TRUNCATED)	0	EPSG:2154	\| 0.50, 0.00, 650774.00\| \| 0.00,-0.50, 7034995.25\| \| 0.00, 0.00, 1.00\|	(650774.0, 7034867.25, 650902.0, 7034995.25)	0.5	2,024	[[[140,138,138,136,135,138,136,133,128,130,134,100,132,126,123,112,139,140,136,142,140,139,142,142,1(...TRUNCATED)	2,015	[[[96,95,94,101,105,109,102,98,105,103,106,106,106,109,112,107,107,110,107,107,96,104,110,114,100,99(...TRUNCATED)	2,018	[[[46,47,47,47,46,45,45,47,47,48,53,66,105,49,50,100,75,49,46,45,44,42,41,43,45,47,47,48,47,47,50,55(...TRUNCATED)	2,021
pas_0650-7035_00_07	pas	[[0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.(...TRUNCATED)	0.003906	EPSG:2154	\| 0.50, 0.00, 650902.00\| \| 0.00,-0.50, 7034995.25\| \| 0.00, 0.00, 1.00\|	(650902.0, 7034867.25, 651030.0, 7034995.25)	0.5	2,024	[[[118,111,110,107,107,106,99,92,98,101,103,101,105,113,113,104,94,94,95,93,103,104,101,99,93,93,82,(...TRUNCATED)	2,015	[[[157,159,156,153,158,164,162,154,155,161,163,162,163,162,162,161,162,161,161,153,161,167,165,160,1(...TRUNCATED)	2,018	[[[141,137,127,128,136,131,128,132,131,143,149,145,142,134,134,133,134,136,142,146,139,135,132,130,1(...TRUNCATED)	2,021
pas_0650-7035_00_08	pas	[[18.3125,18.953125,11.3203125,11.21875,11.8203125,0.0,17.546875,0.0,0.0,0.0,22.15625,24.109375,22.7(...TRUNCATED)	0	EPSG:2154	\| 0.50, 0.00, 651030.00\| \| 0.00,-0.50, 7034995.25\| \| 0.00, 0.00, 1.00\|	(651030.0, 7034867.25, 651158.0, 7034995.25)	0.5	2,024	[[[50,58,52,33,25,23,22,23,39,42,46,29,28,22,20,22,22,24,26,26,45,48,52,36,27,31,37,48,41,41,57,50,2(...TRUNCATED)	2,015	[[[43,36,33,37,36,38,43,41,36,42,48,49,52,51,54,53,54,50,50,53,59,59,56,50,56,64,69,66,70,68,72,77,7(...TRUNCATED)	2,018	[[[45,30,29,32,31,35,46,44,38,42,47,55,56,53,53,55,60,62,53,51,63,59,58,63,66,59,59,62,63,62,55,60,5(...TRUNCATED)	2,021
pas_0650-7035_00_09	pas	[[21.625,0.0,0.0,18.453125,19.015625,18.4375,0.0,0.0,0.0,20.609375,20.765625,0.0,0.0,0.0,0.0,0.0,0.0(...TRUNCATED)	0	EPSG:2154	\| 0.50, 0.00, 651158.00\| \| 0.00,-0.50, 7034995.25\| \| 0.00, 0.00, 1.00\|	(651158.0, 7034867.25, 651286.0, 7034995.25)	0.5	2,024	[[[70,68,66,45,52,57,65,70,54,60,54,42,49,48,51,52,58,71,62,57,57,50,65,52,53,71,63,52,40,29,27,26,2(...TRUNCATED)	2,015	[[[76,69,60,62,58,54,58,56,60,65,68,70,67,66,65,63,59,62,57,59,66,67,59,61,66,60,63,63,61,64,63,64,6(...TRUNCATED)	2,018	[[[59,60,57,50,47,51,50,39,47,54,58,55,52,56,53,54,41,38,40,43,44,48,47,47,52,50,51,40,48,49,44,45,5(...TRUNCATED)	2,021

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🌳 PrediTree: A Multi-Temporal Multi-Spectral Sub-Meter Canopy Height Maps Dataset

📖 Overview

PrediTree is a large-scale multi-temporal, multi-spectral canopy height dataset designed for 🌍 remote sensing, forestry monitoring, and environmental analysis.
All imagery and canopy height products are spatially aligned at 0.5 m resolution, enabling fine-grained tree growth prediction and ecological studies.

✨ Key Highlights

📊 Multi-Temporal: 3 yearly acquisitions (RGB + NIR + NDVI)
🌈 Multi-Spectral: High-resolution optical imagery including RGB, NIR, and derived NDVI
🌲 Canopy Height Models (CHM): LiDAR-based data
📏 Resolution: 0.5 m
🌍 Coverage: France-wide dataset with departmental splits
📦 Scale: 785k training patches, ~880 GB of data

📂 Dataset Structure

Each sample contains:

Column	Description
`chm`	🌲 Canopy Height Model (m)
`rgbnir_ndvi_[1-3]`	📸 RGB + NIR + NDVI imagery for three years (5 bands, 256×256)
`rgbnir_year_[1-3]`	📅 Acquisition year for imagery
`chm_mean_year`	🏞️ Average canopy height across years
`no_data_percentage`	❌ % missing pixels
`crs`, `transform`, `bounds`, `resolution`	🗺️ Geospatial metadata

📊 Dataset Specs

splits:
  train:
    num_examples: 785,392
    256_256px_subtile_examples: 3,141,568
    size: 880 GB
resolution: 0.5 m
dataset_size: 880 GB
license: apache-2.0

🔬 Scientific Context

PrediTree is the first CHM dataset to offer multi-temporal sub-meter CHM-aligned imagery specifically designed for training and evaluating tree height prediction models.

📜 Citation

If you use this dataset, please cite:

@inproceedings{debary2025preditree,
  title={PrediTree: A Multi-Temporal Sub-meter Dataset of Multi-Spectral Imagery Aligned With Canopy Height Maps},
  author={Debary, Hiyam and Fiaz, Mustansar and Klein, Levente},
  booktitle={GAIA},
  year={2025},
  url={https://huggingface.co/datasets/hiyam-d/PrediTree}
}

🔖 Tags

remote-sensing · multi-temporal · multi-spectral · canopy-height-prediction · infrared · rgb · model

Downloads last month: 471

Size of the auto-converted Parquet files (First 5GB):

3.89 GB

Number of rows (First 5GB):

4,460

Estimated number of rows:

758,313