HexaLCSeg: Hexagon-based Historical Land Cover Benchmark Dataset

Abstract

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. Sertel, E., Kabadayı, M.E, Sengul, G. S. & Tumer, I. N, (2024). HexaLCSeg: A Historical Benchmark Dataset from Hexagon Satellite Images for Land Cover Segmentation, IEEE Geoscience and Remote Sensing Magazine, Accepted for publication.