This study presents the first systematic investigation into the influence of spectral enhancement techniques on the segmentation accuracy of specific soil and vegetation marks associated with palaeochannels. These marks are often subtle and can be seasonally obscured by vegetation dynamics and soil variability. Spectral enhancement methods, such as spectral indices and statistical aggregations, are routinely applied to improve their visual discriminability and interpretability. Despite recent progress in automated detection workflows, no prior research has rigorously quantified the effects of these enhancement techniques on the performance of deep learning–based segmentation models. This gap at the intersection of remote sensing and AI-driven analysis is critical, as addressing it is essential for improving the accuracy, efficiency, and scalability of subsurface feature detection across large and heterogeneous landscapes. In this study, two state-of-the-art deep learning architectures, U-Net and YOLOv8, were trained and tested to assess the influence of these spectral transformations on model performance, using Sentinel-2 imagery acquired across three seasonal windows. Across all experiments, spectral enhancement techniques led to clear improvements in segmentation accuracy compared with raw multispectral inputs. The multi-temporal Median Visualisation (MV) composite provided the most stable performance overall, achieving mean IoU values of 0.22 ± 0.02 in April, 0.07 ± 0.03 in August, and 0.19 ± 0.03 in November for U-Net, outperforming the full 12-band Sentinel-2 stack, which reached only 0.04, 0.02, and 0.03 in the same periods. FCC and VBB also performed competitively, e.g., FCC reached 0.21 ± 0.02 (April) and VBB 0.18 ± 0.03 (April), showing that compact three-band enhancements consistently exceed the segmentation quality obtained from using all spectral bands. Performance varied with environmental conditions, with April yielding the highest accuracy, while August remained challenging across all methods. These results highlight the importance of seasonally informed spectral preprocessing and establish an empirical benchmark for integrating enhancement techniques into AI-based archaeological and geomorphological prospection workflows.

This paper presents a 1:20,000 scale Geoarchaeological map of the Cornia river coastal plain in southern Tuscany. The Geoarchaeological map integrates a comprehensive dataset of geomorphological and archaeological features. The spatial relationships between landforms and archaeological records show that the settlement pattern from the VII century BC to the V century AD was mostly influenced by the presence of several depositional landforms which formed before the period under consideration. Integrating the outputs of geomorphological and topographical analyses with archaeological databases is also helpful for land planning, especially in areas where present-day anthropic exploitation needs predictive and valid tools to assess the archaeological hazard.

Deep learning methods have significantly advanced the analysis of satellite imagery, greatly enhancing the efficiency of screening extensive datasets. However, progress in the automatic identification of elements under the surface (subsoil features), detectable only through proxies like soil and vegetation alterations, has been limited. This challenge is further compounded by the significant influence of seasonal variability and land use management on their visibility. Automatic identification often requires multitemporal observations to maximise the likelihood of obtaining favourable visibility conditions and to strengthen the reliability of each identification. Additionally, the limited availability of publicly accessible specialised datasets has impeded progress in applying deep learning approaches. This paper presents the first publicly available dataset of multitemporal Sentinel-2 imagery for identifying palaeochannels, a specific category of subsoil features. We established a baseline for semantic segmentation by evaluating three architectures with various parameter configurations, specifically designed to address the unique challenges of segmenting these elongated and branched features under diverse landscape conditions at different times of the year. The results offer preliminary insights into addressing the varying visibility of subsoil features by applying deep learning to analyse temporal series. The dataset is available on Hugging Face and IIT Dataverse repositories.

Deep learning methods have significantly advanced the analysis of satellite imagery, greatly enhancing the efficiency of screening extensive datasets. However, progress in the automatic identification of elements under the surface (subsoil features), detectable only through proxies like soil and vegetation alterations, has been limited. This challenge is further compounded by the significant influence of seasonal variability and land use management on their visibility. Automatic identification often requires multitemporal observations to maximise the likelihood of obtaining favourable visibility conditions and to strengthen the reliability of each identification. Additionally, the limited availability of publicly accessible specialised datasets has impeded progress in applying deep learning approaches. This paper presents the first publicly available dataset of multitemporal Sentinel-2 imagery for identifying palaeochannels, a specific category of subsoil features. We established a baseline for semantic segmentation by evaluating three architectures with various parameter configurations, specifically designed to address the unique challenges of segmenting these elongated and branched features under diverse landscape conditions at different times of the year. The results offer preliminary insights into addressing the varying visibility of subsoil features by applying deep learning to analyse temporal series. The dataset is available on Hugging Face and IIT Dataverse repositories.