Jul 2026· Journal of Geospatial Science and Analytics· 1 citation
Abstract
High-resolution spatial data is crucial for riverine modeling and flood mitigation. Traditional data often lacks necessary resolution or flexibility, making Unmanned Aerial Vehicles (UAVs) a transformative solution for generating precise Digital Elevation Models (DEMs). This systematic review analyzes 65 peer-reviewed studies published between 2014 and 2025. Following PRISMA guidelines, studies were selected based on specific inclusion and exclusion criteria focusing on riverine hydraulic applications to evaluate data acquisition methods, spatial accuracy, and operational challenges. The synthesis reveals a standard workflow using multirotor platforms, Structure from Motion (SfM) photogrammetry, and Ground Control Points (GCPs) to feed hydrodynamic models like HEC-RAS. While the literature consistently reports centimeter-level vertical accuracy—ideal for mapping flood inundation—critical challenges persist regarding the optical penetration of dense vegetation and submerged bathymetry. Ultimately, while UAV photogrammetry is a robust spatial analysis tool, advancing high-fidelity riverine analytics requires hybrid approaches integrating technologies like UAV-borne LiDAR and sonar. Creating these seamless topobathymetric models is essential for improving reliable flood risk management and informing effective environmental policy.
This study assesses the capabilities of UAV-based Earth observation for analyzing marginalized communities, using Roma settlements in southeastern Slovakia as a case study. Marginalized populations are often underrepresented in official spatial datasets, resulting in a limited understanding of their living conditions, infrastructure needs, and environmental risks. To address this gap, we propose a multi-scalar, UAV-based observational approach that bridges the limitations of coarse satellite imagery and logistically constrained ground surveys. High-resolution RGB and thermal imagery were acquired across three settlements with varying spatial characteristics and processed using photogrammetric workflows to generate detailed orthophotos and spatial products. The results demonstrate that UAV data with centimeter-level spatial resolution enable precise mapping of settlement morphology, infrastructure, waste distribution, and thermal inequalities. Furthermore, UAV observations enable change detection and environmental risk assessment at scales that are not achievable with conventional remote sensing. However, the study also highlights critical operational and ethical challenges, including regulatory constraints, privacy concerns, and the need for community engagement. By integrating technical evaluation with socially sensitive research practices, this work proposes a methodological framework for responsible UAV deployment in marginalized contexts. The findings underscore the potential of UAV-based observation to improve spatial visibility and support evidence-based planning while emphasizing the importance of ethical implementation.
F. Dadrass Javan, L. Ihnacik, P. Blišťan et al.· Remote Sensing· 0 citations
Accurate subaqueous morphological modelling is crucial for sustainable aquatic ecosystem management, yet single-sensor hydrographic surveys remain constrained by environmental limitations. Through a systematic review, this study aims to evaluate the efficacy of multi-level, multi-sensor data fusion approaches (acoustic, optical, LiDAR, and satellite) in bathymetric modelling. A search of five electronic databases (Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink) up to 14 August 2026 was conducted to identify relevant peer-reviewed studies. From 685 initial records, 62 studies were included for systematic synthesis. The eligible studies’ methodological quality was assessed via a customized six-domain risk-of-bias framework. The results indicated significant improvements in spatial coverage and accuracy at complex land–water transition zones compared to single-source baselines, reducing depth retrieval errors by 18% to over 50% and achieving sub-decimeter accuracies (RMSE < 0.10 m). Feature-level Geospatial Artificial Intelligence (GeoAI) further enhanced nonlinear environmental modelling and benthic habitat classification (accuracies > 98%). This review identified that multisensor fusion effectively enhances bathymetric accuracy, providing a critical foundation for sustainable water-resource management. While standardized uncertainty quantification remains an operational bottleneck, future interventions integrating predictive 4D Digital Twins show immense promise for long-term ecological monitoring, flood-risk mitigation, and climate-resilient infrastructure planning.
Hubert Sybilski, Anna Fryśkowska-Skibniewska, Paulina Jaczewska· Sustainability· 0 citations
This study presents a UAV photogrammetry and GIS-based workflow for generating high-accuracy three-dimensional cadastral models in urban environments. The proposed framework integrates RTK-enabled UAV image acquisition, ground control point (GCP) surveying, photogrammetric reconstruction, GIS-based spatial data management, and accuracy assessment within a unified workflow. A total of 454 aerial images were acquired and processed to generate a dense point cloud, digital surface model, orthomosaic, and textured 3D urban model. Positional accuracy was evaluated using 15 independently surveyed RTK GNSS checkpoints distributed throughout the study area. The results demonstrated a relative accuracy of 1.7 cm and an absolute positional accuracy of 2.47 cm based on independent checkpoint validation, confirming the suitability of the proposed workflow for large-scale cadastral mapping applications. The generated 3D cadastral model enabled accurate extraction and visualization of parcel boundaries, building footprints, and urban spatial features. The findings indicate that UAV photogrammetry combined with GIS provides a cost-effective and reliable approach for developing high-precision three-dimensional cadastral datasets that support modern land administration, urban planning, and digital city management.
Salar Mirzapour, Z. Azizi, H. Zavar et al.· Scientific Reports· 0 citations
Remote sensing is widely recognized as a key technology across a wide range of technical and scientific domains, especially in agriculture. Although satellite data have long supported crop monitoring, their limitations in spatial resolution, revisit frequency and cloud coverage have often constrained their applications. High-resolution satellites, available from the beginning of the 2000s, have improved performance, particularly in the field of precision agriculture, but they remain expensive and inflexible. Unmanned Aerial Vehicles perform better in precision agriculture, offering flexibility and high levels of detail; however, their limited operational areas and short endurance flight times constrain their effectiveness. In this evolving landscape, High Altitude Pseudo Satellites (HAPSs), particularly high-altitude balloons, are emerging as a promising new technology that could fill the gaps between satellite and drone remote sensing. These platforms provide large area coverage with high-resolution imagery and long endurance flights at low operational expenses and ease of deployment. This study investigates the operational characteristics, strengths, and geometric limitations of data acquired by the CubeHAPS® platform, a high-altitude pseudo-satellite system, as a prerequisite for its application in precision agriculture. Focusing on experimental campaigns conducted in northern Italy in summer 2024 and 2025, the research characterizes platform stability, image block consistency, and photogrammetric quality through internal metrics. The results demonstrate measurable improvements between the two campaigns, attributed to the introduction of a stabilization system in 2025 and establishing the conditions under which the platform can support reliable photogrammetric reconstruction.
Lorenza Bovio, Victor Miherea, Jannis Fath et al.· Geomatics· 0 citations
This study evaluated a cost-effective UAV-based multi-sensor approach for characterizing river corridor conditions during and after moderate floods in two mountain river corridors in Northern Utah, USA: the Logan River and Blacksmith Fork River. These study reaches included urban, rural, and agricultural areas, hydraulic structures and bridges, and fish passage structures. A DJI Matrice 300 UAV was used with two separate payloads: an AgEagle Altum-PT multispectral camera and an R3 Pro V2 two-return LiDAR system. The workflow included UAV flight planning and data collection, post-processing of the multi-spectral and LiDAR sensor data, spatial resolution and accuracy assessment, and interpretation of the resultant data. The multi-spectral post-processing produced pansharpened orthomosaics with a spatial resolution of 0.0432 m, while the UAV LiDAR produced DSM/DTM products at 0.05 m resolution. LiDAR accuracy assessment showed vertical RMSE values of approximately 0.0602 m for the Blacksmith Fork and 0.0782 m for the Logan River. The results showed that multispectral imagery and 2-band LiDAR provided a cost-effective means for detailed remote sensing with each sensor providing complementary information for flood and river corridor assessment. Multispectral imagery supported interpretation of flood extent, vegetation condition, relative turbidity, and thermal patterns, while LiDAR captured terrain and surface features such as banks, levees, floodplain surfaces, channel modifications, and structures. The integrated datasets supported maximum flood extent mapping and flood-level estimation. These datasets can support reach-scale hydraulic modeling, catchment hydrology, river corridor ecology, floodplain conditions, and real-time monitoring of floods, in addition to quantification of flood hazards or post-flood impacts for municipalities and insurers.
Ishwar Joshi, I. Gowing, B. Crookston· Water· 0 citations
Unmanned Aerial Vehicle (UAV) photogrammetry is increasingly used for local-scale mapping because it enables rapid generation of high-resolution orthophotos and elevation products. However, the positional and vertical quality of these products depends strongly on the georeferencing strategy, particularly the use of Ground Control Points (GCPs). This study evaluates the effect of GCPs on the accuracy of UAV-derived orthophoto, Digital Surface Model (DSM), and Digital Terrain Model (DTM) at the premises of the Land Management Training Center (LMTC), Dhulikhel, Nepal. The same UAV image block of 334 images was processed under two workflows: one using only onboard image geolocation and another using five surveyed GCPs. Eight independent checkpoints were used for accuracy assessment. Orthophoto planimetric accuracy was evaluated using checkpoint coordinates, while vertical agreement of DSM and DTM was assessed using surveyed ground elevations. In addition, pairwise checkpoint-distance analysis and supplementary object-based comparison were used to examine relative geometric differences in the orthophotos. The with-GCP orthophoto achieved a horizontal RMSEH of 0.063 m, whereas the without-GCP orthophoto showed an RMSEH of 3.507 m. Similarly, the with-GCP DSM and DTM achieved RMSEZ values of 0.131 m and 0.143 m, respectively, compared with 15.385 m and 15.347 m in the without-GCP workflow. The without-GCP orthophoto also exhibited systematic westward and northward displacement and minor scale-related geometric differences. The results demonstrate that GCP-based processing remains essential for reliable campus-scale UAV photogrammetry when the outputs are intended for measurement, terrain representation, planning, and other applications requiring dependable absolute accuracy.
B. Bisht, Nabraj Subedi, Ram Kumar Sapkota et al.· Journal of Land Management a...· 0 citations
