The Cave of Altamira (Spain), a UNESCO World Heritage site, contains one of the most fragile and inaccessible Paleolithic rock-art environments in Europe, where geomatics documentation is constrained not only by severe spatial, lighting and safety limitations but also by conservation-driven restrictions on time, access and operational procedures. This study applies a confined-space UAV equipped with LiDAR-based SLAM navigation to document and assess the stability of the vertical rock wall leading to “La Hoya” Hall, a structurally sensitive sector of the cave. Twelve autonomous and assisted flights were conducted, generating dense LiDAR point clouds and video sequences processed through videogrammetry to produce high-resolution 3D meshes. A Mask R-CNN deep learning model was trained on manually segmented images to explore automated crack detection under variable illumination and viewing conditions. The results reveal active fractures, overhanging blocks and sediment accumulations located on inaccessible ledges, demonstrating the capacity of UAV-SLAM workflows to overcome the limitations of traditional surveys in confined subterranean environments. All datasets were integrated into the DiGHER digital twin platform, enabling traceable storage, multitemporal comparison, and collaborative annotation. Overall, the study demonstrates the feasibility of combining UAV-based SLAM mapping, videogrammetry and deep learning segmentation as a reproducible baseline workflow to inform preventive conservation and future multitemporal monitoring in Paleolithic caves and similarly constrained cultural heritage contexts.

A precise estimation of agricultural production provides relevant information for upcoming seasons, and helps in the assessment of crop losses before harvest in case of adverse situations. The objective of this work is to explore the development of a model capable of estimating barley production of a small agricultural production (127 ha) in Belchite, Spain. Variables adapted to the crop calendar of the growing barley are used to achieve that purpose. The variables have been created with weather data and remote sensing images. These images are acquired in two ranges of the electromagnetic spectrum, i.e., microwaves and optical spectral range, obtained from Sentinel-1 and Sentinel-2, respectively. Models are defined with a multiple linear regression method using all combinations of the independent variables correlated with production. The best linear regression model has a prediction error of 57.38 kg/ha (4%). The use of spectral variables, derived from radar vegetation index Cross Ratio (CR) and optical Inverted Red Edge Chlorophyll Index (IRECI), and climatic variables adapted to the crop calendar and climatic conditioning is revealed as an adequate strategy to obtain adjusted models.

The use of different data from satellite platforms for archaeological prospecting and remote sensing has been applied since the end of the 20th century. Although the current use of drones with different visible and multispectral sensors for small areas has partially replaced in some cases the use of this type of satellite information due to its higher spatial resolution. The historical importance of satellite imagery is essential to find out about and compare the transformations of the archaeological landscape of the last 60 years since the CORONA satellite program started in 1960. In this paper we propose the evaluation of a proposal for the automation of processes of two photographic reconnaissance correlative satellite programs CORONA (1960-1972), HEXAGON (1971-1986) declassified since 1995 and 2011 respectively, and the commercial satellite WorldView-3 (WV3) (2014) for use in the detection of buried archaeological structures at the archaeological site of Zar Tepe in the southeast of Uzbekistan. This is a site located in the Surkhan Darya region very little known between the first century BC and the fourth century AD. This methodology is part of the IPAEB project (International Pluridisciplinary Archaeological Expedition to Bactria) led by the University of Barcelona, the University of Salamanca and recently in 2019 the University of Zaragoza. IPAEB was started in 2006 in the South of Uzbekistan and is currently trying to explore the urban planning of the Zar Tepe archaeological site and the elements that make up its natural physical environment: evidence of communication routes, smallholdings, irrigation channels, fences and sources of raw materials.

Wildfires have historically shaped Mediterranean ecosystems, fostering the adaptation of fire-resilient species such as Pinus halepensis Mill. Assessing post-fire resilience is essential to understand landscape recovery and guide forest management. This requires evaluating the speed, intensity, and trajectory of vegetation recovery relative to a defined baseline, although the influence of control point selection and baseline configuration remains unclear, despite its critical role in shaping the interpretation of recovery dynamics. This study proposes a methodological framework to assess the resilience of P. halepensis using 14-year Landsat time series following wildfire events, combined with image segmentation algorithms and Object-Based Image Analysis (GEOBIA). The analysis integrates two complementary vectors: (i) temporal evolution of NDVI and (ii) spectral probability of assignment to P. halepensis. Results indicate that NDVI suggests an average vegetation recovery time of seven years; however, spectral probability remains below 40% during this period, indicating slower tree cover recovery. Field inventories confirm that full recovery requires more than 15 years, with early stages dominated by shrublands, mainly Quercus coccifera. These findings show that NDVI alone overestimates resilience and that control selection and baseline configuration strongly influence assessments. GEOBIA enhances the ecological precision of resilience evaluation.

Remote sensing has become a key non-invasive tool in archaeological prospection, particularly in regions where logistical constraints limit sustained fieldwork. This study presents the results from Zar Tepe (1st–5th centuries AD), in the Surkhandarya province of southern Uzbekistan, within northwestern Bactria. The research aimed to document the site’s urban layout, accurately relocate Soviet-era excavation sectors within the present-day topography, and refine the interpretation of earlier interventions that were only partially documented and lacked precise georeferencing. A multiscale and multitemporal methodology was applied, integrating CORONA and WorldView-3 satellite imagery, UAV and terrestrial photogrammetry, GNSS Precise Point Positioning, magnetic prospection, and targeted archaeological verification. The workflow followed an iterative laboratory–field sequence, combining remote-sensing analysis, field checks, data refinement, and systematic ground-truth validation. Fieldwork was conducted during two contrasting phenological periods, in June 2024 and December 2025, to assess seasonal variability in surface and subsurface visibility. The integrated approach enabled the accurate spatial fitting of legacy excavation sectors and supported the cross-validation of optical and salt-efflorescence-related anomalies with geophysical evidence. These results provide a stronger basis for the cautious interpretation of buried architectural features and for refining hypotheses concerning Zar Tepe’s urban organization and occupational dynamics.