ABSTRACT Archaeological sites are increasingly threatened by human activities and natural processes, causing irreversible heritage loss. Unmanned aerial vehicle (UAV) photogrammetry has advanced documentation and monitoring, yet many applications lack accuracy assessment or rely on a single data source. This study applies high-resolution, multi-temporal UAV-photogrammetry to nine archaeological sites in the Barranca Valleys, Peru—the first case study of this scale in the region. Surveys were scheduled to capture geomorphic responses to the forecasted 2023–2024 mega El Niño, while also documenting pressures such as urbanization, agricultural expansion, and looting. By combining orthomosaics, digital elevation models, image cross-correlation, and horizontal displacement analysis—supported by explicit accuracy evaluation—this research provides a robust framework for assessing site degradation and highlights the added value of multi-source approaches for long-term monitoring.

Employing K-means clustering to reconstruct missing water surface elevations in LiDAR digital elevation models for hydrodynamic simulation.

Evaluation of medieval land transformation for the construction of hilltop castles based on topographic analysis of LiDAR DEMs

Field-scale nitrogen migration mechanisms in small watersheds remain poorly quantified due to insufficient representation of microtopographic heterogeneity. This study investigates nitrogen transport dynamics in a 1.27 km2 agricultural watershed in China’s Jianghuai region using unmanned aerial vehicle (UAV) -derived 0.1 m digital elevation models (DEMs) and coupled hydrological–erosion modeling. The Soil Conservation Service Curve Number (SCS-CN) and Modified Universal Soil Loss Equation (MUSLE) models quantified nitrogen output loads, while the multi-flow direction algorithm simulated migration trajectories for total nitrogen (TN), ammonium, and nitrate. Results revealed strong spatial heterogeneity in nitrogen exports (watershed mean: 29.66 kg TN/km2·a), with bare land and greenhouses exhibiting the highest outputs (448.54 and 363.41 kg/km2·a) and forested areas showing minimal export (<6.1 kg/km2·a). Nitrogen migration was predominantly controlled by topographic gradients, with microtopographic features—field ridges, ditches, and buildings—physically redirecting flows and creating critical export nodes at field boundaries. DEM resolution critically affected simulation accuracy: erosion intensity displayed a non-monotonic response with an inflection point near 1 m resolution, corresponding to the median elevation difference (1.2 m) of field ridges. Structural equation modeling confirmed that high-resolution DEMs (0.1–2 m) maintained topographic control over nitrogen migration (~80% contribution), whereas 30 m DEMs reduced this influence to 30%, inducing spurious meteorological dominance. This study demonstrates that decimeter-scale DEMs are essential for accurately capturing microtopographic regulation of nitrogen transport, providing a methodological basis for precision management of agricultural non-point source pollution.

ABSTRACT Despite the widespread application of Digital Elevation Models (DEMs) in Geographic Information Systems (GIS) and terrain analysis, generating high‐fidelity virtual terrain from sketches remains a significant challenge, particularly regarding feature recognition and realistic pattern generation. This paper proposes a novel method based on Conditional Generative Adversarial Networks (CGANs) for virtual terrain synthesis, effectively addressing key issues in feature extraction, denoising, and preservation. Specifically, the Feature Enhancement Module (FEM) leverages deep learning‐based attention mechanisms to refine terrain features, maintaining the integrity of both global and local patterns. The Elevation Denoising Module (EDM) detects and eliminates noise artifacts introduced during generation, while the Collaborative Loss Module (CLM) optimizes feature preservation. Evaluated on a comprehensive dataset of 15,680 sub‐DEM tiles from six geographically distinct regions, our experiments demonstrate the model's effectiveness. Quantitative results show that our method achieves a Root Mean Square Error (RMSE) of 9.38 m. Furthermore, our approach outperforms existing deep learning models, showing performance improvements of 7.3% over Diffusion, 8.6% over FEN, 10.9% over TFaSR, and 15.3% over IETA. Beyond numerical metrics, the proposed model exhibits superior qualitative advantages, particularly in preserving hydrological connectivity—achieving a mean Intersection over Union (mIoU) of 85.01% for valley lines—and effectively mitigating high‐frequency generative artifacts.

Traditional field-based ecological surveys are inefficient in mountainous regions with steep slopes and deep valleys, highlighting the need for new quantitative remote sensing–based approaches. To account for complex terrain, four representative topographic factors (slope, relief, dissection, curvature) were selected via Digital Elevation Model (DEM) analysis to develop a Terrain Complexity Index (TCI), replacing the dryness component in the Remote Sensing Ecological Index (RSEI). Combined with greenness, wetness, and heat factors from Landsat 8, TCI was integrated using principal component analysis to form a Terrain-Adjusted RSEI (TARSEI), extending ecological assessment from two to three dimensions. In a mountainous case study in Huzhou City, Zhejiang, China, TARSEI showed a marked 34.2-percentage-point improvement over the original RSEI. Its high-value areas captured 82.3% of ecotourism points of interest, versus 48.1% for RSEI, demonstrating its enhanced accuracy for terrain-specific analysis. TARSEI further identified 28 new ecotourism resource clusters totaling 520.1 km2 (8.9% of the city area), with a 98.5% overlap with high TARSEI zones. These results confirmed TARSEI’s effectiveness and provided robust scientific support for sustainable ecotourism development and spatial planning. With its high accuracy, stability, and universality, TARSEI is a promising and transferable tool for ecotourism resource assessment and spatial planning and management in complex terrain regions.

Despite the availability of a highly effective vaccine, yellow fever virus (YFV) is still endemic in 47 countries globally. Although disease due to YFV was first recorded in 1635, factors contributing to its spread remain poorly understood today. Using archival data from the nineteenth century, we digitalised and mapped the 1857 yellow fever (YF) epidemic in Lisbon, Portugal, to understand how transmission dynamics and spatial and environmental characteristics led to disparities in health outcomes between sociodemographic groups. We modelled the basic and effective reproduction number (R0 and Rt) and found that transmission dynamics throughout this pre-vaccination era epidemic is consistent with prevailing estimates (R0 ≃ 5). Transmission peaked at the end of October 1857 when YF was declared an epidemic, then declined until January 1858. YFV killed 4.2% of the population with infection attack rates ranging between 10.3-13.5%. Out of the 34 parishes in urban Lisbon, our hotspot analysis identified 15 statistically significant high-risk parishes near the coastline. Our maps, combined with a digital terrain model, show that the highest number of deaths occurred within connected streets confined in low-elevation built-up areas with homes. We discuss the potential role of wind and temperature in aiding mosquito dispersal across Lisbon, which were believed as the main historical environmental drivers of YF. More people died at home than in hospitals, and although working-aged men accounted for most fatalities, the highest probability of death was found among women working at home. Our study highlights the role of human-environment interactions in shaping a historical YF epidemic in a pre-vaccination urban setting and enhances our understanding of modern-day transmission dynamics.

ABSTRACT Rain‐induced erosion processes can severely damage Earthen archaeological sites. Huaca Chornancap (HCH; eighth–14th century ad ) is a platform located in the Lambayeque region (Peru) exposed to seasonal rain due to El Niño Southern Oscillation (ENSO). We present data from a UAV‐based photogrammetric survey and produce a Digital Surface Model from which we compute selected morphometric parameters. Rills and gullyes affect the HCH steep flanks, whereas pits concentrates on the flat top. Slides induced by rain also affect the western flank. Results from a model evidence the HCH sectors where diffusive erosion processes are predicted to act over the next century. We determine a vertical erosion rates of 0.33 m/century by ENSO‐related rain, 0.13 m/century by splash erosion and < 0.25 m/century by diffusion processes. Our findings emphasize the deteroriation of the HCH structural integrity. The methodological approach we propose may be applied to other earthen archaeological sites worldwide.

Abstract Surveying an area for new fossil sites is a labor-intensive and resource-draining activity that can be alleviated with the aid of machine learning models. In karst landscapes of southern Africa, Plio-Pleistocene fossils that inform the paleoanthropological record are primarily found preserved in caves and sinkholes. The purpose of this study is to assess the utility of Random Forest (RF) models for cave and sinkhole prediction in the Cradle of Humankind, South Africa. Multispectral satellite imagery, digital elevation models (DEMs), and geologic maps were converted into raster (pixelated matrix) images in a GIS environment to denote varying aspects of the local topography, including elevation, slope, aspect, curvature, drainage, spectral reflectance, vegetation cover, fault proximity, and underlying geology. The rasters were stacked and overlaid with 1080 known cave and sinkhole locality points and 1080 random non-cave points in the study area for model training. Variable values associated with these geopoints were input into an RF model in Python for training and evaluation using a spatial ten-fold cross-validation. The model performed with 81.6% accuracy and an area under the curve (AUC) of 0.912. The importance of each variable for prediction was evaluated by measuring the increase in prediction error when variable values were shuffled. Distance to major faults, location within the Chuniespoort geologic group, dolomite presence, chert presence, and elevation exhibited the highest importance for model accuracy, while three out of 48 total predictor variables exhibited less importance than a randomly generated variable. The identification of important/unimportant variables will help build more efficient, robust models in future iterations, as well as help identify variables that could be useful in other karst regions.

This study examines how variations in engineered woody debris dam designs affect bed morphology. Pools and riffles, formed by these dams, create varied flow velocities and depths, dissipating energy and mitigating downstream flood risk. Understanding their hydraulic effects on scour and deposition remains limited. Laboratory flume experiments were conducted at two discharges, using two structure designs, each comprising three wooden cylinders with varying bed-to-base gaps. Digital Elevation Models were generated using a Kinect v1.0 and Matlab. To verify localised velocities and bed shear stress values, a computational flume was constructed using Jacobs Flood Modeller. Findings showed that the 2D model replicated the physical experiments effectively, highlighting increased bed shear stress downstream of the structure and corroborating the location of maximum scour depth. Results indicated that structures close to the bed increased scour depth beneath them, enlarging the wetted area and reducing their effectiveness in slowing the flow.

Abstract. Climate change is driving significant cryospheric degradation in the European Alps, potentially increasing the frequency of glacial hazards such as ice avalanches. This study presents an integrated, reproducible workflow to assess the vulnerability of hydroelectric infrastructure in the Aosta Valley, Italy. The methodology combines a statistical analysis of historical European events with stochastic numerical modelling. A robust relationship between collapse volume and the angle of reach was derived using bootstrap regression. Glacier release points were identified by intersecting flowlines with glacier fronts, applying a densification strategy for complex ice bodies. Potential runout scenarios were simulated using the r.randomwalk model on a normalized 24 m Digital Elevation Model (DEM), comparing datasets from 2000 and 2008 to evaluate sensitivity to glacial retreat. Results highlight localized criticalities, particularly in the Valpelline sector, where collapses from the Luseney and Solatset glaciers could directly impact intake structures and reservoirs. In Valtournenche, simulations identify scenarios that approach the Perrères power plant. Despite these specific risks, the regional energy network demonstrates high overall resilience. This approach provides a valuable tool for prioritizing monitoring and mitigation strategies in high-mountain energy planning.

Abstract. Urban digital twins (UDTs) play a pivotal role in advancing smart city development, enabling virtual representations of complex urban environments and supporting advanced planning, monitoring, and decision-making. Despite significant progress, challenges remain in intuitively visualizing dynamic, heterogeneous data, managing real-time updates, and representing temporal occupancy in a comprehensible way. This research introduces a visual metaphor, “Colorful Smoke”, for temporal space occupancy visualization within voxel-based UDTs. The approach first generates a voxel-based urban digital twin by fusing CityGML, Digital Terrain Model (DTM), and point cloud data. Elements assumed to be static at the time of observation are represented by voxels, whereas dynamic objects, such as vehicles and pedestrians, are represented using the ‘Colorful Smoke’ metaphor, in which smoke density encodes occupied space, transparency indicates the probability of occupancy, and color represents the type of dynamic object. In contrast to existing voxel-based methods, this design enables users to intuitively perceive temporal occupancy, assess data reliability, and identify areas of change in urban spaces without visual overload. Future work will enhance the metaphor’s clarity through additional colors and adjustment of rendering parameters, and evaluate its perceptual effectiveness via user studies and continuous dynamic updates. The proposed “Colorful Smoke” approach offers a scalable and intuitive method for representing temporal occupancy in UDTs, bridging the gap between complex data and human perception, and providing a foundation for future dynamic urban visual analytics.

This project initiative aims to construct a comprehensive 3D model of Sinnar Taluka. This project lies in critical challenges in urban planning, disaster management, infrastructure design, and environmental analysis. The methodology discussed here involved various steps such as obtaining toposheets, georeferencing on them, clipping relevant areas, mosaicking images together, allocating contours, preparing a Digital Elevation Model (DEM), delineating boundaries, deriving river networks and their properties. These parameters comprise of characteristics like drainage density, stream frequency, relief ratio, slope, aspect, and many more. Determination of morphometric parameters like land cover data, building footprints, road networks, and administrative boundaries using GIS software platforms. The outcomes of this project are expected to empower decision-makers with a holistic view of Sinnar Taluka's geographical characteristics. This study also offers valuable insights into water resources within the studied region (i.e., Sinnar Taluka) that can be utilized for effective planning and management with regard to the hydrological aspect.

Sabkhas are unique salt flat formations situated along the coastline and have been the subject of extensive scientific inquiry. This study delves into the formation and significance of sabkhas, along the South Red Sea coast of Egypt. Combining field observations, satellite imagery, and GIS analysis, the research unveils the processes shaping these distinct landscapes and their broader impact on the region. The study utilizes Sentinel-2A imagery and digital elevation models to map salinity and identify optimal methods for salt detection. It further employs advanced data processing techniques to refine land cover classification and identify unique features within four sabkhas along the Red Sea coast: Ras Baghdadi, Marsa Abu Madd, Bir Shalatein-Marsa Himeira, and Diib. Examining these sabkhas reveals intricate details of their topography, hydrology, and sediment composition. The study identifies factors contributing to their individual characteristics, such as structural control, interaction with lagoons, and the influence of wind and aridity. Analysis of satellite data and field observations unveils the presence of salt ponds, dunes, microbial mats, and distinct sediment layers within these formations. Evaporite crystals, halophytic vegetation, and color patterns provide further insights into their formation processes. The study emphasizes that sea level fluctuations, fluvial and aeolian processes, and limited human intervention have shaped the temporal evolution of these sabkhas. However, climate change poses significant future challenges. By highlighting the importance of understanding and preserving these ecologically and economically valuable ecosystems, this research underscores the urgent need for their protection in the face of a changing climate.

Abstract The severity and frequency of tundra fires in Arctic permafrost landscapes is expected to increase with ongoing climate change. By burning the insulating organic layer of soils, tundra fires impact the soil thermal regime for underlying permafrost and can accelerate thaw in the years following the burn. In this paper, we address the scarcity of long-term studies on post-fire permafrost degradation in ice-wedge landscapes by using a space-for-time substitution analysis spanning a chronosequence (pseudo-time series) of up to 67 years of remote sensing data from Alaskan tundra fire scars. We use computer vision and graph analysis on high-resolution digital elevation models derived from airborne lidar of fire-affected areas in Western Alaska to investigate the effects of tundra fires on the post-fire development of microtopography and surface hydrology in polygonal ice-wedge landscapes. Our analysis indicates a modest overall trend toward recovery of polygonal surface structure over timescales of 70+ years, though considerable variability among fire scars highlights that post-fire trajectories are not uniform.

This study evaluated the flood risk along the banks of the Burgay River in the city of Azogues using hydraulic modelling with HEC-RAS. Flood scenarios for 10-, 50-, and 100-year return periods were simulated, accounting for local topography, estimat-ed precipitation, and associated peak discharges. A Digital Elevation Model (DEM) sourced from the SIG Tierras platform was employed for watershed delineation and geomorphological characterization. Model calibration based on comparison with recorded stream gauging data reduced the error in the Manning’s roughness coefficient to 3 %, thereby improving prediction accuracy. The results showed a substantial expansion of vulnerable zones as the return period increased, underscoring the need for mitigation measures such as levee construction, implementation of drainage systems, and deployment of an early warning system. This study confirms HEC-RAS utility as a tool for planning flood-control strategies and protecting urban areas exposed to flood risk.

The Lake Chambon area, located between the Col de la Croix-Morand and Murol (Massif Central, France), consists of a Hercynian crystalline basement partially overlain by Cenozoic formations, largely composed of volcanic products related to the Mont-Dore stratovolcano. The present-day topography, sedimentation patterns, and drainage network are strongly controlled by a complex fault system. A detailed morphostructural analysis identified more than 500 lineaments from a high-resolution digital elevation model (DEM), which were digitized and analyzed in a GIS environment using QGIS. A directional classification combining expert-based interpretation with a semi-supervised machine-learning approach (k-means clustering) revealed seven major fault families, grouped into clusters consistent with a regional dextral shear regime. An interpretive tectonic model is proposed, consistent with the current stress field ( σ 1 trending between N160°E and N170°E). Faults of the F1 family are interpreted as dextral shear zones related to the South Armorican Shear Zone–Cholet–Poitiers Fault–Southern Border Fault of the Limagne graben system, associated with secondary Riedel-type structures. The influence of the sinistral Sillon Houiller Fault is expressed by the F6′′ family (N20°E) and by the F2′ family, whose orientation is comparable to that of the Tauves–Aigueperse fault system (N50°E). The F4 family corresponds to extensional faults, locally reactivated within this broader strike-slip tectonic framework. The proposed neotectonic framework allows for the interpretation of several key geomorphological features. The Lake Chambon Basin may correspond to a transtensional pull-apart structure. In contrast, the slow-moving landslide at Chambon-sur-Lac, located between the transtensional zones of the Rochers de Pousseterre to the west and Lake Chambon to the east, appears to be controlled by the structural inheritance and kinematics of faults F4, F6′′ and F2′, which locally accommodate oblique deformation within a transpressive regime. Finally, the study suggests that deep hydrothermal activity at Chambon-sur-Lac may be linked to regional seismicity associated with the F1 fault system.

This study presents a comprehensive methodology to improve monorail route selection in Kirkuk city, Iraq using a quantitative GIS-based multi-criteria decision analysis. The primary aim of this study is to develop and apply a quantitative, GIS-based multi-criteria decision analysis framework for optimizing and objectively selecting the most suitable monorail route in Kirkuk city, Iraq, by integrating spatial, topographic, accessibility, service coverage, and economic factors in order to support sustainable and cost-effective urban transport planning in a post-conflict context. The research area covers 460.89 km², including an extensive road network of 5,533.27 km and 3,193 points of interest (POI). Four different monorail routes were analysed and evaluated using OpenStreetMap data derived from an analytic hierarchy process, digital elevation models and site suitability analysis. The approach uses weighted graph construction, various shortest path methods, and a wide range of performance metrics, including cost-effectiveness, service coverage, topographic conditions, and accessibility. Route optimization includes terrain suitability (scores from 0.50 to 0.78), elevation profile (288–393m), gradient limitations (maximum score 0.20 to 0.32), and points of interest (POIs) within a service radius of 500 m to 1 km. Based on this, Results shows Route 1 is the ideal choice as it found to be the most economical route (4.15 km, US$214.01 million), had the highest suitability score (0.70), and required the shortest travel time (9.61 minutes). On the contrary, Route 0 had the widest service coverage (31.37 km² service area, 66.1 points of interest within 500 m).

This paper investigates the integration of geographic information systems (GIS)-based visibility analysis—commonly known as viewshed analysis—with real-time 3D rendering in unreal engine, specifically within the context of archaeological and cultural heritage applications. Visibility maps are an essential tool in archaeological research, helping scholars understand the spatial relationships, sightlines, and symbolic visibility between structures, monuments, and landscapes. However, traditional GIS viewshed analysis is often static and limited to 2D environments. This project proposes a method to bring visibility analysis into immersive 3D environments by visualizing GIS-generated data within unreal engine. The methodology involves generating a viewshed from a given digital elevation model (DEM) using established GIS software. The resulting raster is then exported and processed into a texture or material mask compatible with unreal engine. Once imported, the data is mapped onto a 3D landscape model, allowing users to explore visibility dynamically, including first-person or VR-based navigation. This interdisciplinary approach contributes to the field of digital archaeology by enhancing spatial interpretation and audience engagement through immersive geovisualization. It also outlines a flexible pipeline for integrating geospatial datasets into 3D environments, potentially applicable to site management, public education, and digital preservation efforts.

We present the first preliminary adaptation and implementation of the HOTSED framework in a high-altitude watershed of the Eastern Italian Alps chosen as pilot area. HOTSED was applied to assess the spatio-temporal variability of sediment source hotspots driven by rainfall-induced surface runoff across different climatic conditions and rainfall intensities. We analyzed four seasonal scenarios and four daily scenarios, including an ordinary event and three extreme events with different return periods (10-year, 30-year, and 50-year). A pre-existing polygon-based geomorphological map was used to spatially define sediment sources across the study area. The geomorphic potential of each sediment source was estimated through a qualitative scoring of map attributes, supported by semi-quantitative, spatially distributed indices, including slope, permafrost distribution, and a proxy for frost-cracking incidence on the bedrock. Structural sediment connectivity was estimated using a geomorphometric index based on a Digital Terrain Model. For each scenario, a proxy for sediment transport potential was computed using a rainfall-calibrated index, applying a 0°C ground surface temperature threshold to exclude snow-covered areas. All components were then integrated through a raster-based equation, yielding the HOTSED model. Results show that hotspots become more widespread and geomorphologically active during warmer and wetter seasons, particularly in summer and autumn, due to a combination of higher cumulative rainfall, intensified thermo-mechanical weathering, and increased topographic-altitudinal control on water flows. The model successfully identified hotspot toposequences with a high potential to trigger hazardous cascade processes. The analysis shows that even moderate rainfall extremes (e.g., 10-year return period events) can significantly amplify hazard patterns. This highlights the importance of identifying and monitoring geomorphic responses and, hence, managing appropriately cascading systems in Alpine watersheds under changing climatic conditions.