High-resolution Aerial Imagery Research Articles

A new technique mapping submerged beachrocks using low-altitude UAV photogrammetry, the Altınova region, northern coast of the Sea of Marmara (NW Türkiye)

High-resolution aerial imagery captured from low altitudes enables the detailed reconstruction of the geomorphological features of submerged beachrocks and the quantification of their topographic complexity. These coastal deposits serve as vital indicators for estimating past sea-level positions and deformation, contributing to our understanding of climate and tectonic processes across various temporal and spatial scales. This study focuses on submerged beachrocks identified in the nearshore coastal area of Tekirdağ city (Altınova), and the existing knowledge regarding such geological formations is limited.Situated in the tectonically active western Marmara region, the coastal zone of The Tekirdağ-Altınova is influenced by the North Anatolian Fault Zone (NAFZ), which has significantly shaped the coastline over time. Utilizing unmanned aerial vehicle (UAV) data, we successfully isolated submerged beachrocks from other coastal deposits, identifying them at depths of 2 m below current sea level and extending approximately 5 km along the coast. This identification was facilitated by integrating high-resolution aerial imagery with morphological analysis techniques, specifically employing structure-from-motion photogrammetry to generate a dense point cloud for bathymetric mapping.The utilization of cost-effective UAV imagery facilitated the efficient monitoring of beachrocks. Geographic information systems (GIS) and the TerraceM application were employed to analyze the high-resolution bathymetry data (with a 5 cm resolution), enabling the precise estimation of shoreline angles and associated errors. These shoreline angles, which correspond to the past sea levels high stand, were mapped using swath profiles oriented perpendicular to the isobaths. Our findings reveal that the majority of submerged beachrocks exhibit shoreline angles between ∼ −0.7 m and −1.1 m. This study presents innovative methodologies for mapping the height and spatial distribution of beachrocks, as well as for reliably estimating reliable uplift rates in the nearshore area of Tekirdağ-Altınova.

Individual Tree Crown Delineation Using Airborne LiDAR Data and Aerial Imagery in the Taiga–Tundra Ecotone

The circumpolar Taiga–Tundra Ecotone significantly influences the feedback mechanism of global climate change. Achieving large-scale individual tree crown (ITC) extraction in the transition zone is crucial for estimating vegetation biomass in the transition zone and studying plants’ response to climate change. This study employed aerial images and airborne LiDAR data covering several typical transitional zone regions in northern Finland to explore the ITC delineation method based on deep learning. First, this study developed an improved multi-scale ITC delineation method to enable the semi-automatic assembly of the ITC sample collection. This approach led to the creation of an individual tree dataset containing over 20,000 trees in the transitional zone. Then, this study explored the ITC delineation method using the Mask R-CNN model. The accuracies of the Mask R-CNN model were compared with two traditional ITC delineation methods: the improved multi-scale ITC delineation method and the local maxima clustering method based on point cloud distribution. For trees with a height greater than 1.3 m, the Mask R-CNN model achieved an overall recall rate (Ar) of 96.60%. Compared to the two conventional ITC delineation methods, the Ar of Mask R-CNN showed an increase of 1.99 and 5.52 points in percentage, respectively, indicating that the Mask R-CNN model can significantly improve the accuracy of ITC delineation. These results highlight the potential of Mask R-CNN in extracting low trees with relatively small crowns in transitional zones using high-resolution aerial imagery and low-density airborne point cloud data for the first time.

Deep Learning (CNN) for Detecting Road Infrastructure in Old Mosul City Using High-Resolution Aerial Imagery

يعد رسم خرائط دقيقة للبنية التحتية للطرق من الصور الجوية أمرًا بالغ الأهمية لمختلف التطبيقات ولكنه يطرح تحديات عديدة بسبب تعقيد أنماط الطرق في الواقع الحقيقي. يبحث هذا البحث في تقنيات التعلم العميق لاستخراج الطرق بشكل الي من البيانات العامة. يتم تقييم العديد من بنى الشبكات العصبية التلافيفية (CNN) بما في ذلك الشبكة الطيفية المكانية الهجينة (HybridSN) التي تجمع بين للصور البصرية وبيانات الليدار. يتم تقييم النماذج على مجموعة بيانات من الصور الجوية الحضرية باستخدام علامات حقيقة أرضية مشتقة من تقنية الليدار. ويحقق HybridSN الذي يدمج كلاً من المعالجة الطيفية والمكانية أعلى أداء بدقة إجمالية تبلغ 96.9% و80.6% من التقاطع بعد المعالجة اللاحقة. تتيح النمذجة المشتركة للإشارات متعددة الوسائط إمكانية تحديد أجزاء الطريق وتحديدها بدقة عالية. وبالمقارنة، فإن شبكات CNN التي تستفيد من السياق المكاني وحده تؤدي أداءً أسوأ مع أفضل دقة إجمالية تبلغ 95.4% بعد المعالجة اللاحقة. تظهر جميع النماذج أوجه قصور في استخراج شبكات الطرق المتماسكة . وهذا يدل على أهمية دمج البيانات الطيفية والمكانية ضمن أطر التعلم العميق لاستخراج الطرق. تسلط النتائج الضوء على الفرص المتاحة لتطوير أحدث الخرائط من خلال أجهزة الاستشعار الهجينة وتصميم بنى عصبية ذات وعي طوبولوجي أقوى. يقوم بالتحليل الآلي للبيانات الجوية متعددة الوسائط مع التعلم العميق والحفاظ على كفاءة قوائم جرد حديثة للبنية التحتية الحيوية للنقل على نطاق المدن.

A Remote-Sensing-Based Method Using Rockfall Inventories for Hazard Mapping at the Community Scale in the Arequipa Region of Peru

Small communities in the Arequipa region of Peru are susceptible to rockfall hazards, which impact their lives and livelihoods. To mitigate rockfall hazards, it is first necessary to understand their locations and characteristics, which can be compiled into an inventory used in the creation of rockfall hazard rating maps. However, the only rockfall inventory available for Arequipa contains limited data of large, discrete events, which is insufficient for characterizing rockfall hazards at the community scale. A more comprehensive inventory would result in a more accurate rockfall hazard rating map—a significant resource for hazard mitigation and development planning. This study addresses this need through a remote method for rockfall hazard characterization at a community scale. Three communities located in geographically diverse areas of Arequipa were chosen for hazard inventory and characterization, with a fourth being used for validation of the method. Rockfall inventories of source zones and rockfall locations were developed using high-resolution aerial imagery, followed by field confirmation, and then predictions of runout distances using empirical models. These models closely matched the actual runout distance distribution, with all three sites having an R2 value of 0.98 or above. A semi-automated method using a GIS-based model was developed that characterizes the generation and transport of rockfall. The generation component criteria consisted of source zone height, slope angle, and rockmass structural condition. Transport was characterized by rockfall runout distance, estimated rockfall trajectory paths, and hazard ratings of corresponding source zones. The representative runout distance inventory model of the validation site matched that of a nearby site with an R2 of 0.98, despite inventorying less than a third of the number of rockfalls. This methodology improves upon current approaches and could be tested in other regions with similar climatic and geomorphic settings. These maps and methodology could be used by local and regional government agencies to warn residents of rockfall hazards, inform zoning regulations, and prioritize mitigation efforts.

Comparison of Shoreline Change from High Resolution Aerial and Satellite Imagery: A South Carolina Case Study

Coastal shorelines are complex and valuable environments that are ever changing. Remote sensing technologies such as satellites and aerial imaging sensors provide an aerial perspective for mapping and analyzing shoreline change. This study aims to compare two accessible and widely used remote sensing data sources- MAXAR WorldView 2 satellite (WV2) and National Agriculture Imagery Program (NAIP) aerial imagery, while also identifying shoreline change trends adjacent to North Inlet, South Carolina. We found that the NAIP and WV2 imagery were very similar in their ability to provide a reliable backdrop for digitizing shoreline data, albeit with differing costs and temporal considerations. In the case study application of using the imagery to detect shoreline change at North inlet over a 12-year period, we found that the three northern regions of the study area experienced overall average erosional impacts of −2.1 m, −4.5 m, and −11.3 m per year. The southernmost region experienced an overall average accretional rate of + 10.6 m. These changes were like a few nearby inlet-adjacent shoreline change studies and are considered to be caused by storm and tidal events, natural sediment drift, and other the dynamic influences of the inlet.

Visual Interpretation of High-Resolution Aerial Imagery: A Tool for Land Managers

Abstract Remotely sensed imagery from various collection platforms (e.g., satellites, crewed and uncrewed aircraft) are used by biologists and other conservation personnel to support management activities ranging from monitoring invasive species to assessing land cover and vegetation characteristics. Although remote sensing–based vegetation indices and models have been developed and used for some management applications, straightforward visual interpretation of imagery by on-the-ground personnel may be a pragmatic approach for obtaining time-sensitive and spatially relevant information to support and guide local management activities. Our primary objective was to qualitatively assess our ability to identify patches of target invasive plant species based on simple visual interpretation of high-resolution aerial imagery. We also sought to compare the high-resolution imagery to widely available imagery (e.g., National Agriculture Imagery Program) to determine the efficacy of each for assessing vegetation communities and land-cover features in support of management activities. To accomplish these objectives, we obtained high-resolution imagery and visually scanned and assessed the imagery by using standard geographic information system software. We were able to differentiate patches of crownvetch Securigera varia (L.) Lassen and wild parsnip Pastinaca sativa L., but not spotted knapweed Centaurea stoebe L. or leafy spurge Euphorbia esula L. The relative success in identifying these species had a relationship to plant characteristics (e.g., flower color and morphology, height), time of year (phenology), patch size and density, and potentially site characteristics such density of the underlying vegetation (e.g., grasses), substrate color characteristics (i.e., color contrast with flowers), and physical disturbance. Our straightforward, qualitative assessment suggests that visual interpretation of high-resolution imagery, but not some lower-resolution imagery, may be an efficient and effective tool for supporting local invasive species management through activities such as monitoring known patches, identifying undetected infestations, assessing management actions, guiding field work, or prioritizing on-the-ground monitoring activities.

Comparison between aerial imagery and conventional cadastral mapping methods in Ekiti State Nigeria; towards a fit-for-purpose approach

Towards achieving Fit-for-Purpose (FFP) cadastral mapping, this study compares conventional cadastral mapping methods of theodolite traverse, total station and Real Time Kinematic Global Navigation Satellite System (RTK GNSS) with high-resolution aerial imagery in Ekiti State, Nigeria. Evaluating time, cost, accuracy, and coverage, it finds that high-resolution aerial imagery can achieve results comparable to conventional instruments with wider coverage, thereby expediting land registration and fostering sustainable development. However, challenges in obtaining high-resolution imagery necessitate regulatory reform for UAV use. The study recommends adoption of innovative solutions to improve the spatial, legal, and institutional frameworks in the state and enhance land governance.

EnhancedNet, an End-to-End Network for Dense Disparity Estimation and its Application to Aerial Images

Recent developments in deep learning technology have boosted the performance of dense stereo reconstruction. However, the state-of-the-art deep learning-based stereo matching methods are mainly trained using close-range synthetic images. Consequently, the application of these methods in aerial photogrammetry and remote sensing is currently far from straightforward. In this paper, we propose a new disparity estimation network for stereo matching and investigate its generalization abilities in regard to aerial images. First, we propose an end-to-end deep learning network for stereo matching, regularized by disparity gradients, which includes a residual cost volume and a reconstruction error volume in a refinement module, and multiple losses. In order to investigate the influence of the multiple losses, a comprehensive analysis is presented. Second, based on this network trained with synthetic close-range data, we propose a new pipeline for matching high-resolution aerial imagery. The experimental results show that the proposed network improves the disparity accuracy by up to 40% in terms of errors larger than 1 px compared to results when not including the refinement network, especially in areas containing detailed small objects. In addition, in qualitative and quantitative experiments, we are able to show that our model, pre-trained on a synthetic stereo dataset, achieves very competitive sub-pixel geometric accuracy on aerial images. These results confirm that the domain gap between synthetic close-range and real aerial images can be satisfactorily bridged using the proposed new deep learning method for dense image matching.

AM-UNet: Road Network Extraction from high-resolution Aerial Imagery Using Attention-Based Convolutional Neural Network

AM-UNet: Road Network Extraction from high-resolution Aerial Imagery Using Attention-Based Convolutional Neural Network

A Deep Learning Approach to Segment Coastal Marsh Tidal Creek Networks from High-Resolution Aerial Imagery

Tidal creeks play a vital role in influencing geospatial evolution and marsh ecological communities in coastal landscapes. However, evaluating the geospatial characteristics of numerous creeks across a site and understanding their ecological relationships pose significant challenges due to the labor-intensive nature of manual delineation from imagery. Traditional methods rely on manual annotation in GIS interfaces, which is slow and tedious. This study explores the application of Attention-based Dense U-Net (ADU-Net), a deep learning image segmentation model, for automatically classifying creek pixels in high-resolution (0.5 m) orthorectified aerial imagery in coastal Georgia, USA. We observed that ADU-Net achieved an outstanding F1 score of 0.98 in identifying creek pixels, demonstrating its ability in tidal creek mapping. The study highlights the potential of deep learning models for automated tidal creek mapping, opening avenues for future investigations into the role of creeks in marshes’ response to environmental changes.

Quantifying the Variability of “Fixed-Width” Buffers on Harvested Lands in Western Oregon and Washington

Abstract In contemporary forest management, buffers of unharvested trees are left along streams to protect riparian and aquatic ecosystems. Buffer regulations often focus on specific minimum width requirements, which aid in straightforward regulation and application, but minimum widths also suggest buffered edges are uniform and contain little variability. Conceptual papers suggest that alternative buffer configurations may offer greater flexibility in landscape-level protection, increase forest complexity, and enhance aquatic and riparian biodiversity. However, before considering alternatives to fixed-width buffers, it is necessary to quantify the inherent variability in current buffer practices present on the landscape. In this study, we used aerial imagery to quantify variability of buffer widths in two hundred randomly selected recently harvested units on managed land in Oregon and Washington with both fish-bearing and non-fish-bearing sections of stream. Wider buffers on larger streams had a greater magnitude of variability, but when normalized by stream size, variation was greater in smaller streams, and overall, variation ranged from 25% to 50% of the mean width. Despite local variability, buffer widths rarely fell below 9.14 m (30 ft). The variation quantified here provides an initial measure of variability to inform future management, given emerging interest in variable retention buffers. Study Implications: In contemporary forest management, riparian and aquatic habitat protection usually relies on minimum buffer width regulations. Minimum requirements create the perception that buffered edges are uniform distances from streams containing little variability, so conceptual papers have suggested alternative buffer configurations with variable retention edges to enhance ecological benefits. However, there are several reasons why, in practice, buffer widths may vary substantially from minimum sizes within and across harvest units under current forest management, but this potential variation has not been quantified. Before considering alternative riparian management options, it is necessary to quantify and understand the inherent variability in current practices. By utilizing high-resolution aerial imagery and digital elevation models, we quantified buffer width variation. Using a set of two hundred recently harvested units, we demonstrate notable buffer width variation across managed Pacific Northwest forests. We attribute the variation in buffer widths to the presence of road crossings, tributary junctions, underlying valley slope and slope variation, and flexibility in regulations that may be overlooked in broad evaluations of strict minimum widths. Understanding fundamental information about buffer width variability provides information about current practices and provides a standard against which proposed increases in variability may be compared.

Dual level attention based lightweight vision transformer for streambed land use change classification using remote sensing

Due to rapid urbanization, rising food demand, and changed precipitation patterns, the waterbodies are contracting their former beds. The continuous shrinking of waterbodies is deteriorating the vital cultural, supporting, provisioning, and regulating services. Thus, understanding and mitigating the impacts of streambed land cover change is crucial for maintaining healthy aquatic ecosystems and improving flood resilience of surrounding population. The existing works use high-resolution aerial imagery focusing on large waterbodies, while ignoring the most vulnerable floodplains of innumerous small water bodies due to high inter-class similarity. This limits the ability to perform a temporal analysis of land cover change along small water bodies. The present work aims to resolve this issue using open-source satellite imagery and taking patched samples along the boundary of small water bodies to identify long-term changes in land cover patterns. Sentinel-2 and Landsat 50 acquired satellite images were used to identify the land cover of this colonized stream bed. The data of Landsat 50 served as historical reference for identifying the changed land use. To capture spatial hierarchies in satellite images effectively, in this paper, a novel dual attention-based vision transformer has been developed for land-cover classification in four categories namely, water, built-up, siltation, and vegetation. The developed model is trained on the data collected from three potential sites in India. The experimental results are validated against seven state-of-the-art deep learning models. The results reveal that the proposed method outperformed all the considered methods by achieving accuracy and precision of 88.4% and 88.9%, respectively, while consuming the least number of parameters. The results reaffirm the concretization and erosion of nature’s flood buffers for economic advancement.

Fusion of multi-temporal PlanetScope data and very high-resolution aerial imagery for urban tree species mapping

Detailed assessment of the ecosystem services provided by urban green spaces requires data on urban tree species. While many approaches for mapping of urban trees from remotely sensed data have been proposed, the fusion of multi-temporal satellite imagery with very high resolution orthophotos remains relatively underexplored. In this research, we assess the potential of a multimodal deep learning approach with intermediate data fusion for classifying common tree species found in the Brussels Capital Region. Our method combines two image sources: (a) multi-temporal PlanetScope data and (b) high-resolution aerial imagery. To evaluate the contribution of each image source, we separately train and assess each branch of the network. Both image sources demonstrate the ability to predict prevalent tree species with high accuracy. However, the fusion of the two image sources yields the best results, achieving an overall accuracy of 0.88 for the five most common tree species in the region. Our approach is compared to two conventional machine learning methods: a random forest (RF) and a support vector machine classifier (SVM) and outperforms both with a 11 percentage point increase in overall accuracy over RF and a 10 percentage point increase over SVM. Increasing the number of species from five to thirteen, including all species with more than 500 tree samples, results in a marginal decrease in accuracy (from 0.88 to 0.84). Overall, our deep learning approach demonstrates its efficacy in classifying common tree species in urban settings and provides a foundation for a comprehensive quantification of ecosystem services offered by urban trees through remote sensing data.

Extraction of building footprint using MASK-RCNN for high resolution aerial imagery

Extracting individual buildings from satellite images is crucial for various urban applications, including population estimation, urban planning, and other related fields. However, Extracting building footprints from remote sensing data is a challenging task because of scale differences, complex structures and different types of building. Addressing these issues, an approach that can efficiently detect buildings in images by generating a segmentation mask for each instance is proposed in this paper. This approach incorporates the Regional Convolutional Neural Network (MASK-RCNN), which combines Faster R-CNN for object mask prediction and boundary box recognition and was evaluated against other models like YOLOv5, YOLOv7 and YOLOv8 in a comparative study to assess its effectiveness. The findings of this study reveals that our proposed method achieved the highest accuracy in building extraction. Furthermore, we performed experiments on well-established datasets like WHU and INRIA, and our method consistently outperformed other existing methods, producing reliable results.

Limited spatial co-occurrence of wildfire and prescribed burning on moorlands in Scotland

Prescribed burning of vegetation is a land management approach that can be used to prevent or reduce the risk of wildfires yet burns can escape and be an ignition source for wildfires. In Scotland, muirburn is a form of prescribed burning used to encourage new growth of heather for grouse and livestock. Future regulation aims to mitigate potential negative impacts of muirburn on biodiversity, peat carbon stocks and wildfire risk. In this study, we assessed the spatial co-occurrence of muirburn and wildfires across Scotland. Evidence of muirburn was quantified using high-resolution aerial imagery from 2018 and wildfire occurrence was a composite of satellite and aerial detection covering 2015 to 2020. Fires were mapped on to dominant vegetation habitat types (acid grasslands, heather grasslands, heather heathlands, and peatbogs) and peat soil. Overall, 96 % of the total wildfire area occurred outside moorlands managed by muirburn, with wildfires covering 1.1 % of total moorland area. Heather grasslands, peatbogs and habitats on peat soils had a greater proportional area of wildfires than other moorland habitats and habitats on non-peat soils. In contrast, heather heathlands with non-peat soils had a greater proportional area of muirburn. We suggest the limited co-occurrence of muirburn and wildfires, may be due to fuel load reduction following muirburns, but may arise due to wildfire prevention strategies on estates and/or lower occurrence of recreational caused wildfires. In addition to muirburn regulation, our findings emphasise the need for policy, management, awareness and education outside of muirburn areas to reduce the risk of wildfires.

Impact Assessment of Nematode Infestation on Soybean Crop Production Using Aerial Multispectral Imagery and Machine Learning

Accurate and prompt estimation of geospatial soybean yield (SY) is critical for the producers to determine key factors influencing crop growth for improved precision management decisions. This study aims to quantify the impacts of soybean cyst nematode (SCN) infestation on soybean production and the yield of susceptible and resistant seed varieties. Susceptible varieties showed lower yield and crop vigor recovery, and high SCN population (20 to 1080) compared to resistant varieties (SCN populations: 0 to 340). High-resolution (1.3 cm/pixel) aerial multispectral imagery showed the blue band reflectance (r = 0.58) and Green Normalized Difference Vegetation Index (GNDVI, r = −0.6) have the best correlation with the SCN populations. While GDNVI, Green Chlorophyll Index (GCI), and Normalized Difference Red Edge Index (NDRE) were the best differentiators of plant vigor and had the highest correlation with SY (r = 0.59–0.75). Reflectance (REF) and VIs were then used for SY estimation using two statistical and four machine learning (ML) models at 10 different train–test data split ratios (50:50–95:5). The ML models and train–test data split ratio had significant impacts on SY estimation accuracy. Random forest (RF) was the best and consistently performing model (r: 0.84–0.97, rRMSE: 8.72–20%), while a higher train–test split ratio lowered the performances of the ML models. The 95:5 train–test ratio showed the best performance across all the models, which may be a suitable ratio for modeling over smaller or medium-sized datasets. Such insights derived using high spatial resolution data can be utilized to implement precision crop protective operations for enhanced soybean yield and productivity.

Comparison of Deep and Machine Learning Approaches for Quebec Tree Species Classification Using a Combination of Multispectral and LiDAR Data

Accessing tree species information is required for making appropriate decisions in forest management. 3D photo-interpretation using high spatial resolution aerial imagery is used to provide information on tree species in the province of Quebec. However, the shortage of qualified interpreters and the increasing costs of 3D photo-interpretation have affected the production of the forest inventory. In this study, we employed deep and machine learning models to classify nine tree species (i.e., paper birch, yellow birch, red maple, poplar, black spruce, white spruce, tamarack, jack pine, and balsam fir). We used a combination of spectral and vertical structural information extracted from 30-cm aerial imagery and airborne light detection and ranging (LiDAR) data in a 9 , 100 km 2 forested area in Quebec, Canada. The results indicated that Dense Convolution Network (DenseNet) achieved the best overall accuracy of 78%, outperforming machine learning methods by 5%. In addition, the models’ performance was independently assessed using permanent and temporary ground sample plots, acknowledging the superiority of DenseNet in terms of overall accuracy (73%) in predicting the dominant species. Our results suggest that the combination of aerial imagery and airborne LiDAR data, using deep learning approaches, can be applied to accurately map tree species.

Evapotranspiration estimation using high-resolution aerial imagery and pySEBAL for processing tomatoes

AbstractThe use of high-resolution aerial imagery for assessing actual crop evapotranspiration $$ \left({ET}_{a}\right)$$ holds the potential to optimize the use of limited water resources in agriculture. Despite this potential, there is a shortage of information regarding the effectiveness of energy balance algorithms, initially designed for satellite remote sensing in estimating $$ {ET}_{a}$$ using aerial imagery. This study addresses this gap by employing the remote sensing model pySEBAL (Surface Energy Balance Algorithm for Land) in conjunction with high-resolution aerial imagery to estimate $$ {ET}_{a}$$ for processing tomatoes. Throughout the 2021 growing season, an aircraft captured multispectral and thermal imagery over a processing tomato field near Esparto, California, USA. Simultaneously, an eddy covariance flux tower within the field measured high-frequency turbulent fluxes and low-frequency biometeorology variables essential for evaluating the energy balance. The comprehensive assessment of energy balance components, including $$ {ET}_{a}$$, yielded compelling evidence that pySEBAL accurately estimated $$ {ET}_{a}$$ at high spatial resolution. The root mean square error (RMSE) and normalized RMSE for various energy balance components were as follows: 33 W m− 2 (12%) for latent heat flux, 29 W m− 2 (35%) for sensible heat flux, 24 W m− 2 (4%) for net radiation, and 10 W m− 2 (15%) for soil heat flux. Additionally, $$ {ET}_{a}$$ exhibited an RMSE and NRMSE of 0.26 mm d− 1 (6%). Moreover, the spatial mapping of $$ {ET}_{a}$$ across the processing tomato field visually depicted the spatial variability associated with irrigation scheduling, crop development, areas affected by disease, and soil heterogeneity. This research underscores the value of high resolution spatial aerial imagery and pySEBAL algorithm for estimating $$ {ET}_{a}$$ variability in the field, a crucial aspect for guiding precision irrigation management and ensuring the optimal use of limited water resources in agriculture.

Combining dual attention mechanism and efficient feature aggregation for road and vehicle segmentation from UAV imagery

Unmanned aerial vehicles (UAVs) have gained significant popularity in recent years due to their ability to capture high-resolution aerial imagery for various applications, including traffic monitoring, urban planning, and disaster management. Accurate road and vehicle segmentation from UAV imagery plays a crucial role in these applications. In this paper, we propose a novel approach combining dual attention mechanisms and efficient multi-layer feature aggregation to enhance the performance of road and vehicle segmentation from UAV imagery. Our approach integrates a spatial attention mechanism and a channel-wise attention mechanism to enable the model to selectively focus on relevant features for segmentation tasks. In conjunction with these attention mechanisms, we introduce an efficient multi-layer feature aggregation method that synthesizes and integrates multi-scale features at different levels of the network, resulting in a more robust and informative feature representation. Our proposed method is evaluated on the UAVid semantic segmentation dataset, showcasing its exceptional performance in comparison to renowned approaches such as U-Net, DeepLabv3+, and SegNet. The experimental results affirm that our approach surpasses these state-of-the-art methods in terms of segmentation accuracy.

3D Modeling of rural environments from multiscale aerial imagery

Given the increasing attention to environmental preservation and sustainable development, the digitization of rural landscapes stands out as a pivotal strategy for effective environmental management and sustainability, land use planning, and preservation of cultural heritage. This work proposes a novel methodology for generating 3D models of rural landscapes by integrating multiscale data sources. Although Unmanned Aerial Vehicles (UAV) simplify the acquisition of multi-source data, their coverage is typically restricted to small landscapes due to their limited range and flight time. On the other hand, although the use of aerial images provides a broader view of the terrain, it is important to note that the low resolution of these images interferes with the task of accurate 3D modeling. Given these challenges, we propose a methodology that combines UAV data and high-resolution aerial imagery provided by the Spanish National Orthophoto Program (PNOA). This multi-source data integration is crucial to generating detailed and accurate 3D models of rural environments. The proposed methodology involves three steps: (1) semantic segmentation of aerial images identifying features such as vegetation, ground, and human-made structures, (2) estimation of the Digital Elevation Model (DEM), and (3) 3D modeling of rural environments using the point clouds generated from UAV images. The conducted experiments demonstrate the effectiveness of our approach identifying and representing previously mentioned features. Thus, this work presents advances in 3D representation techniques for real scenarios, contributing to the coordination of land utilization and environmental sustainability in rural landscapes.