HexaLCSeg: Hexagon-based Historical Land Cover Benchmark Dataset

This dataset is a research outcome of a European Research Council, Proof of Concept Grant funded (Grant Number 101100837, A GeoAI-based Land Use Land Cover Segmentation Process to Analyse and Predict Rural Depopulation, Agricultural Land Abandonment, and Deforestation in Bulgaria and Turkey, 1940-2040, GeoAI_LULC_Seg) project.

We introduce a new benchmark dataset derived from very high-resolution historical Hexagon (KH-9) reconnaissance satellite images for use in deep learning-based image segmentation tasks. Our dataset comprises high-resolution monochromatic Hexagon images from the 1970s and 1980s covering Turkish and Bulgarian territories, encompassing a large geographic area.

Land cover (LC) classes used in this study:Our dataset is inspired by the European Space Agency (ESA) WorldCover project and includes eight LC classes and related RGB codes were set for each class but we adjusted the 0-pixel value as no data and replaced the 0 values with 1 in the ESA RGB code palette. Additionally, a new sub-class for the trees, named Permanent Cropland is defined and its RGB code was set to 1-207-117. This class is important to differentiate permanent fruit trees from other trees, specifically crucial for past agricultural mapping purposes.

The HexaLCSeg dataset comprises eight panchromatic images accompanied by corresponding 3-channel RGB Ground Truth Masks, all with 8-bit radiometric resolution and a spatial resolution of 1 meter. The dataset is organized into a total of 10,000 patches, each sized at 256x256 pixels. We split our dataset into 70% training (7000 patches), 15% validation (1500 patches), and 15% testing (1500 patches).

Methodology:In our study, we employed the geographic object-based image analysis (GEOBIA) approach to generate accurate land cover (LC) maps, which serve as the ground truth masks for our dataset.

For deep learning-based image segmentation, we employed a total of 9 CNN models, implementing U-Net++ and DeepLabv3+ segmentation architectures with different hyperparameters, paired with SE-ResNeXt50 backbone that pre-trained with weight values from the 2012 ILSVRC ImageNet dataset.

Models, metric results and weights:

Model No
Architecture
Loss Function
Augmentation
Loss
Accuracy
IoU
F-1 Score
Precision
Recall

Model 1
U-Net++
Focal Loss
No Augmentation
0.1252
0.9734
0.8052
0.8804
0.8805
0.8803

Model 2
U-Net++
Focal Loss
Horizontal Flip
0.1253
0.9728
0.8008
0.8776
0.8778
0.8774

Model 3
DeepLabv3+
Focal Loss
No Augmentation
0.1255
0.9720
0.7959
0.8739
0.8744
0.8734

Model 4
U-Net++
Focal Loss
Random BC
0.1256
0.9717
0.7938
0.8725
0.8727
0.8723

Model 5
DeepLabv3+
Dice Loss
Horizontal Flip
0.1292
0.9714
0.7928
0.8714
0.8717
0.8711

Model 6
DeepLabv3+
Dice Loss
No Augmentation
0.1307
0.9711
0.7906
0.8699
0.8702
0.8697

Model 7
DeepLabv3+
Focal Loss
Horizontal Flip
0.1257
0.9711
0.7897
0.8698
0.8704
0.8692

Model 8
DeepLabv3+
Focal Loss
Random BC
0.1259
0.9704
0.7871
0.8667
0.8673
0.8662

Model 9
DeepLabv3+
Dice Loss
Random BC
0.1401
0.9691
0.7793
0.8608
0.8612
0.8604

System-specific notes and configuration:

The code was implemented in Python (3.10) Programming Language.

- torch == 2.1.2- segmentation-models-pytorch == 0.3.3- Albumentations == 1.4.0

Apart from main data science libraries, RS-specific libraries such as GDAL, rasterio, and tifffile are also required.

Citation:Please kindly cite our paper if this code and the dataset used in the study are useful for your research.

Elif Sertel et al., “HexaLCSeg: A Historical Benchmark Dataset from Hexagon Satellite Images for Land Cover Segmentation [Software and Data Sets],” IEEE Geoscience and Remote Sensing Magazine 12, no. 3 (September 2024): 197–206, https://doi.org/10.1109/MGRS.2024.3394248.