Dihkan, Mustafa; Akbulut, Zeynep; Güler, Gülsena; Özdemir, Samed; Karsli, Fevzi

Historical aerial imagery acts as a crucial bridge to the pre-satellite era, becoming increasingly vital for quantifying long-term land cover changes and supporting retrospective environmental studies. This study presents a comprehensive comparison of CNN-based (DeepLabV3+, PSPNet, HRNet, ConvNeXt) and transformer-based (ViT, Swin Transformer, BEiT) architectures for semantic segmentation of historical aerial imagery. In particular, the potential of Transformer-based architectures for semantic segmentation of panchromatic historical aerial imagery in the context of increasing segmentation complexity remains underexplored. We assess how effectively each architecture handles increasing class granularity and captures finer-grained land-cover semantics. Experiments are conducted at three levels of detail: binary, three-class, and five-class segmentation. The dataset comprises panchromatic images from 1960, captured in Turkey. Precision, recall, F1-score, and intersection-over-union (IoU) metrics were employed to evaluate class-wise performance. In the binary ground and non-ground segmentation task, all architectures achieved mean F1-scores of 0.945–0.959 and mean IoU values of 0.905–0.921. In the three-class and five-class tasks, mean F1-scores ranged from 0.861–0.902 and 0.754–0.803, with mean IoU values of 0.777–0.831 and 0.623–0.682, respectively. In the three-class setting, ConvNeXt, Swin Transformer, and BEiT clustered towards the upper end of the mean metrics with only small differences among them. Increasing class granularity from three to five reduced mean performance across architectures, with decreases of up to 0.123 in mean F1-score and up to 0.176 in mean IoU. Across all classification levels, ConvNeXt consistently achieved higher performance, distinguishing itself specifically in the five-class task with the highest F1-scores and IoU values. ConvNeXt outperformed both the CNN-based baselines and Transformer-based models in the challenging building and road categories.

Akbulut, Zeynep; Karsli, Fevzi; Dihkan, Mustafa

Ground filtering is an essential process that separates ground and non-ground points for generating digital elevation models (DEMs) from airborne laser scanning (ALS) point cloud data. Traditionally, ground filtering was carried out using rule-based approaches, but with the advancements in deep learning networks, ground filtering can now be performed using these methods. Deep neural networks (DNNs) require large amounts of training samples to reveal key information within the dataset. OpenGF is a new ultra-large-scale dataset which has nearly half a billion points from nine different scenes ranging from highly dense urban areas to steep vegetation covered mountains for ground filtering task. In this study, PointNet++ architecture was employed for ground filtering on the OpenGF dataset. Firstly, we slightly modified PointNet++ architecture’s sampling strategy to decrease the number of duplicate points in the batching process. We proposed a new adaptive tiling strategy based on input point cloud density to cope with highly varying point sampling of the OpenGF dataset. Modified PointNet++ architecture was trained with both adaptive tiling strategy and original OpenGF tiles to investigate the advantages and drawbacks of the proposed strategy on the ground filtering task. To evaluate the performance of the proposed strategy, the results for different test areas were compared with those obtained using the original PointNet++ and rule-based cloth simulation filtering (CSF) algorithms. Despite using fewer epochs, the proposed data collection strategy achieved up to 13% higher accuracy on test scenes with the modified PointNet++ architecture compared to the network trained with original OpenGF tiles.