HexaLCSeg: Hexagon-based Historical Land Cover Benchmark Dataset
Authors/Creators
- 1. Istanbul Technical University
-
2.
Koç University
Description
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.