Aerial Data Research Articles

Laser-Empowered UAVs for Aerial Data Aggregation in Passive IoT Networks

Laser-Empowered UAVs for Aerial Data Aggregation in Passive IoT Networks

A Green Fingerprint of Antarctica: Drones, Hyperspectral Imaging, and Machine Learning for Moss and Lichen Classification

Mapping Antarctic Specially Protected Areas (ASPAs) remains a critical yet challenging task, especially in extreme environments like Antarctica. Traditional methods are often cumbersome, expensive, and risky, with limited satellite data further hindering accuracy. This study addresses these challenges by developing a workflow that enables precise mapping and monitoring of vegetation in ASPAs. The processing pipeline of this workflow integrates small unmanned aerial vehicles (UAVs)—or drones—to collect hyperspectral and multispectral imagery (HSI and MSI), global navigation satellite system (GNSS) enhanced with real-time kinematics (RTK) to collect ground control points (GCPs), and supervised machine learning classifiers. This workflow was validated in the field by acquiring ground and aerial data at ASPA 135, Windmill Islands, East Antarctica. The data preparation phase involves a data fusion technique to integrate HSI and MSI data, achieving the collection of georeferenced HSI scans with a resolution of up to 0.3 cm/pixel. From these high-resolution HSI scans, a series of novel spectral indices were proposed to enhance the classification accuracy of the model. Model training was achieved using extreme gradient boosting (XGBoost), with four different combinations tested to identify the best fit for the data. The research results indicate the successful detection and mapping of moss and lichens, with an average accuracy of 95%. Optimised XGBoost models, particularly Model 3 and Model 4, demonstrate the applicability of the custom spectral indices to achieve high accuracy with reduced computing power requirements. The integration of these technologies results in significantly more accurate mapping compared to conventional methods. This workflow serves as a foundational step towards more extensive remote sensing applications in Antarctic and ASPA vegetation mapping, as well as in monitoring the impact of climate change on the Antarctic ecosystem.

Controlling the Flexibility of Non-Gaussian Processes Through Shrinkage Priors

The normal inverse Gaussian (NIG) and generalized asymmetric Laplace (GAL) distributions can be seen as skewed and semi-heavy-tailed extensions of the Gaussian distribution. Models driven by these more flexible noise distributions are then regarded as flexible extensions of simpler Gaussian models. Inferential procedures tend to overestimate the degree of non-Gaussianity in the data and therefore we propose controlling the flexibility of these non-Gaussian models by adding sensible priors in the inferential framework that contract the model towards Gaussianity. In our venture to derive sensible priors, we also propose a new intuitive parameterization of the non-Gaussian models and discuss how to implement them efficiently in Stan. The methods are derived for a generic class of non-Gaussian models that include spatial Matérn fields, autoregressive models for time series, and simultaneous autoregressive models for aerial data. The results are illustrated with a simulation study and geostatistics application, where priors that penalize model complexity were shown to lead to more robust estimation and give preference to the Gaussian model, while at the same time allowing for non-Gaussianity if there is sufficient evidence in the data.

Integrating Aerial and 3D Data into a Data-Driven Decision-Making Workflow for Nature-Based Stormwater Solutions

Urbanization and climate change have increased the need for stormwater management and nature-based solutions. Decisions made at the project level impact the emergence of the systemic traits of the stormwater network and the functionality of the catchment areas in urban planning. To that end, it is vital to introduce the decision-making tools for analysing both the utilities and amenities of nature-based solutions (NBS) to increase their adoption to reduce the peak loads in the stormwater system and, to that end, mitigate the impacts of climate change. There is a deficiency in employing a software-based approach to analyse the qualitative and quantitative aspects of NBSs to back up design decisions. This paper demonstrates a workflow using drone-based photogrammetry, 3D modelling, and simulation software to generate visual and functional models assisting in informed decision-making in the design of stormwater systems as functional landscape architecture. Using aerial data from drones and modelled design solutions, the proposed workflow simulates rain events, infiltration, evaporation, water flow, and the accumulation of stormwater in a way that allows the visual and quantified analysis of detailed landscape architecture designs. The paper provides an example of a rooftop site simulation demonstrating the infiltration and flow of water to the drainage. The visual decision-making method provided can aid in investment decisions for functional landscape design in support of stormwater management.

Multi-Scale Influence Analysis of Urban Shadow and Spatial Form Features on Urban Thermal Environment

In urban thermal environment research (UTE), urban shadows formed by buildings and trees contribute to significant variations in thermal conditions, particularly during the mid-day period. This study investigated the multi-scale effects of indicators, including urban shadows, on UTE, focusing specifically on the mid-day hours. It integrated field temperature measurements and drone aerial data from multiple city blocks. Considering both urban shadows and direct solar radiation, a linear mixed-effects model was employed to study the multi-scale effects of urban morphological indicators. Results showed that: (1) UTE is a multi-scale, multi-factor phenomenon, with thermal effects manifesting at specific scales. Under shadow conditions, smaller scales (10–20 m) of landscape heterogeneity and larger scales (300–400 m) of landscape consistency better explained temperature variations mid-day. Conversely, under direct sunlight, temperature was primarily influenced by larger scales (150–300 m). (2) Trees significantly reduced temperature; 100% tree canopy cover within a 10-m radius reduced air temperatures by approximately 2 °C mid-day. However, there is no significant correlation between temperature and green spaces. (3) Building area and height were significantly correlated with temperature. Specifically, an increase in building area beyond 150 m, especially within a 300-m radius, leads to higher temperatures. Conversely, building height within a 10–20 m range exhibits significant cooling effects. These findings provide crucial reference data for micro-scale UTE investigations during mid-day hours and offer new strategies for urban planning and design.

PPChain: A Blockchain for Pandemic Prevention and Control Assisted by Federated Learning.

Taking COVID-19 as an example, we know that a pandemic can have a huge impact on normal human life and the economy. Meanwhile, the population flow between countries and regions is the main factor affecting the changes in a pandemic, which is determined by the airline network. Therefore, realizing the overall control of airports is an effective way to control a pandemic. However, this is restricted by the differences in prevention and control policies in different areas and privacy issues, such as how a patient's personal data from a medical center cannot be effectively combined with their passenger personal data. This prevents more precise airport control decisions from being made. To address this, this paper designed a novel data-sharing framework (i.e., PPChain) based on blockchain and federated learning. The experiment uses a CPU i7-12800HX and uses Docker to simulate multiple virtual nodes. The model is deployed to run on an NVIDIA GeForce GTX 3090Ti GPU. The experiment shows that the relationship between a pandemic and aircraft transport can be effectively explored by PPChain without sharing raw data. This approach does not require centralized trust and improves the security of the sharing process. The scheme can help formulate more scientific and rational prevention and control policies for the control of airports. Additionally, it can use aerial data to predict pandemics more accurately.

Investigating the efficiency and capabilities of UAVs and Convolutional Neural Networks in the field of remote sensing as a land classification tool

The study aimed to determine the efficacy and capabilities of using high-resolution aerial imagery and a convolutional neural network (CNN) to identify plant species and monitor land cover and land change in the context of remote sensing. The full capabilities of a CNN were examined, including testing whether the platform could be used for land cover and the evaluation of land change over time. An unmanned aerial vehicle (UAV) was used to collect the aerial data of the study area. The CNN was encoded and operated in RStudio, while digitised data from the input imagery were used by the programme as training and validation data. The object in this respect was to learn about the relevant features of the landscape, and thereafter to classify the Opuntia invasive plant species. Accuracy assessments were carried out on the results to test the efficacy of the aerial imagery in terms of its accuracy and reliability. The classification achieved an overall accuracy of 93%, while the kappa coefficient score was 0.86. CNN was also able to predict the land coverage area of Opuntia to be within four percent (4%) of the ground truthing data. A change in land cover over time was detected by the programme after the manual clearing of the plant had been undertaken. This research has determined that the use of a CNN in remote sensing is a very powerful tool for supervised image classifications. It can be used for monitoring land cover in that it is able to accurately estimate the spatial distribution of plant species and to monitor the growth or decline in the species over time. As such, it is an efficient methodology and its use in remote sensing could be extended.

Target detection based on multi-scale feature fusion and cross-channel interactive attention mechanism

Aiming at the problems of complex background, target scale change and small target in aerial image detection, we propose a YOLOv5 target detection algorithm based on multi-scale feature fusion and cross-channel interactive attention mechanism. Including: M-PPM (Multi-scale pyramid pooling module) is designed as a replacement for the SPP (Spatial Pyramid Pooling) structure in YOLOv5, so as to make full use of different scale features to fuse global feature information; CCA (Cross-channel interactive attention mechanism) is designed to realize cross-channel information interaction and utilization, and enhance the network’s capability to generalize and fusion efficiency of small target features. Bi-directional Feature Pyramid Network (BiFPN) is utilized to solve scale difference problem in multi-target detection. The proposed algorithm’s experimental results is 2.3 % and 1.8 % higher than YOLOv5 on the VisDrone and UAVDT aerial data sets, respectively.

Flood Inundation and Depth Mapping Using Unmanned Aerial Vehicles Combined with High-Resolution Multispectral Imagery

The identification of flood hazards during emerging public safety crises such as hurricanes or flash floods is an invaluable tool for first responders and managers yet remains out of reach in any comprehensive sense when using traditional remote-sensing methods, due to cloud cover and other data-sourcing restrictions. While many remote-sensing techniques exist for floodwater identification and extraction, few studies demonstrate an up-to-day understanding with better techniques in isolating the spectral properties of floodwaters from collected data, which vary for each event. This study introduces a novel method for delineating near-real-time inundation flood extent and depth mapping for storm events, using an inexpensive unmanned aerial vehicle (UAV)-based multispectral remote-sensing platform, which was designed to be applicable for urban environments, under a wide range of atmospheric conditions. The methodology is demonstrated using an actual flooding-event—Hurricane Zeta during the 2020 Atlantic hurricane season. Referred to as the UAV and Floodwater Inundation and Depth Mapper (FIDM), the methodology consists of three major components, including aerial data collection, processing, and flood inundation (water surface extent) and depth mapping. The model results for inundation and depth were compared to a validation dataset and ground-truthing data, respectively. The results suggest that UAV-FIDM is able to predict inundation with a total error (sum of omission and commission errors) of 15.8% and produce flooding depth estimates that are accurate enough to be actionable to determine road closures for a real event.

Analysis of airport design for introducing infrastructure for autonomous drones

PurposeConnecting autonomous drones to ground operations and services is a prerequisite for the adoption of scalable and sustainable drone services in the built environment. Despite the rapid advance in the field of autonomous drones, the development of ground infrastructure has received less attention. Contemporary airport design offers potential solutions for the infrastructure serving autonomous drone services. To that end, this paper aims to construct a framework for connecting air and ground operations for autonomous drone services. Furthermore, the paper defines the minimum facilities needed to support unmanned aerial vehicles for autonomous logistics and the collection of aerial data.Design/methodology/approachThe paper reviews the state-of-the-art in airport design literature as the basis for analysing the guidelines of manned aviation applicable to the development of ground infrastructure for autonomous drone services. Socio-technical system analysis was used for identifying the service needs of drones.FindingsThe key findings are functional modularity based on the principles of airport design applies to micro-airports and modular service functions can be connected efficiently with an autonomous ground handling system in a sustainable manner addressing the concerns on maintenance, reliability and lifecycle.Research limitations/implicationsAs the study was limited to the airport design literature findings, the evolution of solutions may provide features supporting deviating approaches. The role of autonomy and cloud-based service processes are quintessentially different from the conventional airport design and are likely to impact real-life solutions as the area of future research.Practical implicationsThe findings of this study provided a framework for establishing the connection between the airside and the landside for the operations of autonomous aerial services. The lack of such framework and ground infrastructure has hindered the large-scale adoption and easy-to-use solutions for sustainable logistics and aerial data collection for decision-making in the built environment.Social implicationsThe evolution of future autonomous aerial services should be accessible to all users, “democratising” the use of drones. The data collected by drones should comply with the privacy-preserving use of the data. The proposed ground infrastructure can contribute to offloading, storing and handling aerial data to support drone services’ acceptability.Originality/valueTo the best of the authors’ knowledge, the paper describes the first design framework for creating a design concept for a modular and autonomous micro-airport system for unmanned aviation based on the applied functions of full-size conventional airports.

Modelling the spread of tree pests and pathogens in urban forests

Understanding the potential dynamics of tree pests and pathogens is a vital component for creating resilient urban treescapes. Epidemiologically relevant features include differences in environmental stress and tree management between street and garden trees, and variation in the potential for human-mediated spread due to intensity of human activity, traffic flow and buildings. We extend a standard spatially explicit raster-based model for pest and pathogen spread by dividing the urban tree population into roadside trees and park/garden trees. We also distinguish between naturally-driven radial spread of pests and pathogens and human-mediated linear spread along roads. The model behaviour is explored using landscape data for tree locations in an exemplar UK town. Two main sources of landscape data were available: commercially collated aerial data, which have high coverage but no information on species; and, an urban tree inventory, with low, non-random, coverage but with some species data. The data were insufficient to impute a species-specific host landscape accurately; however, by combining the two data sources, and applying either random or Matérn cluster point process driven selection of a subset of all trees, we create two sets of potential host landscapes. We find that combining the two mechanisms of dispersal has a non-additive effect, with the enhanced linear dispersal enabling new foci of infection to be established more rapidly than with radial dispersal alone; and clustering of trees by species slows down the expansion of epidemics when compared with random distribution of tree species within known host locations.

Digitalization and Spatial Documentation of Post-Earthquake Temporary Housing in Central Italy: An Integrated Geomatic Approach Involving UAV and a GIS-Based System

Geoinformation and aerial data collection are essential during post-earthquake emergency response. This research focuses on the long-lasting spatial impacts of temporary solutions, which have persisted in regions of Central Italy affected by catastrophic seismic events over the past 25 years, significantly and permanently altering their landscapes. The paper analyses the role of geomatic and photogrammetric tools in documenting the emergency process and projects in post-disaster phases. An Atlas of Temporary Architectures is proposed, which defines a common semantic and geometric codification for mapping temporary housing from territorial to urban and building scales. The paper presents an implementation of attribute specification in existing official cartographic data, including geometric entities in a 3D GIS data model platform for documenting and digitalising these provisional contexts. To achieve this platform, UAV point clouds are integrated with non-metric data to ensure a complete description in a multiscalar approach. Accurate topographic modifications can be captured by extracting very high-resolution orthophotos and elevation models (DSM and DTM). The results have been validated in Visso (Macerata), a small historical mountain village in Central Italy which was heavily damaged by the seismic events of 2016/2017. The integrated approach overcomes the existing gaps and emphasizes the importance of managing heterogeneous geospatial emergency data for classification purposes. It also highlights the need to enhance an interoperable knowledge base method for post-disaster temporary responses. By combining geomatic tools with architectural studies, these visualization techniques can support national and local organizations responsible for post-earthquake management through a 3D modelling method to aid future transformations or interventions following other natural disasters.

Multi-temporal Pacific madrone leaf blight assessment with unoccupied aircraft systems

Pacific madrone leaf blight (PMLB) is a contributing agent to the decline of Pacific madrone (Arbutus menziesii) trees. Multiple fungal pathogens cause PMLB, resulting in leaf spotting that can eventually kill leaves, increasing stress in individuals, and leaving them more susceptible to deadly cankers. Spores transmit via air and water droplets, particularly during wet Spring months. Unoccupied aircraft systems (UAS) technologies are in their relative infancy, but UAS are becoming more affordable and accessible. UAS promise increased efficiency in forest health monitoring applications, providing a safer aerial data collection method at a relatively-low cost when compared to occupied aircraft. In this study, we develop and present a UAS methodology to detect PMLB with a multispectral sensor. This methodology combines orthomosaic products derived from high-resolution (~4 cm) multirotor platform UAS multispectral imagery with machine learning and ground assessment of PMLB to classify visual presence of blight at the individual tree-level during multiple site revisits. The resulting model detected PMLB infection status of 29 field surveyed madrone trees with a kappa coefficient of , a balanced accuracy of 0.85, and a true positive rate of 0.92. The method presented here can be readily scaled to efficiently cover a much larger extent with a beyond-line-of-site capable UAS and minimal field sampling. The increased efficiency of this approach may be critical to characterizing PMLB in the near future as it is anticipated that PMLB prevalence will continue to increase as a result of climate change.

COMBINED 360° VIDEO AND UAV RECONSTRUCTION OF SMALL-TOWN HISTORIC CITY CENTERS

Abstract. Small-town historic urban environments increasingly face different pressures that may prevent their future sustainability. To promote a recovery plan for those centers, an interdisciplinary methodology is needed to orient future actions toward innovative solutions. However, the development of such a plan needs the definition of a dedicated methodology to drive the definition of recovery actions. A key role is the possibility of low-cost methods for mapping and classifying the built heritage. Both ground and aerial (UAV) solutions can be used to create 3D city models for further classification and analysis. However, integrating those two data in small city centers poses specific challenges. This paper presents a combined approach using ground and aerial (UAV) data to comprehensively reconstruct a part of a small-town city center. Ground data are video acquired with 360° (spherical) cameras. Previous works proved 360° videos can reconstruct with good metric accuracy and complete building façades of historic city centers. The combined reconstruction is tested in the so-called “Casa Teatro”, an abandoned building in the city center of Vogogna, a small town in the Ossola Valley (Northern Italy). The results of the combined image orientation are validated by using ground truth data (Check Points) acquired with a total station. In contrast, the final 3D model reconstruction results are compared with a laser scanning acquisition of the building to evaluate the accuracy and completeness of the photogrammetric point cloud. In addition, matching among 360° frames and UAV frames is studied by analysing connectivity graphs.

Energy Minimization of Dynamic Multi-UAV Communication Network for Cooperative MultiHop Data Gathering

This study proposes a minimum-energy operational planning problem for multiple unmanned aerial vehicles (UAVs) conducting a cooperative multi-hop data-gathering mission. A multi-UAV ad-hoc network collects data from multiple sources and transmits them to a ground base station. The proposed mixed-integer nonlinear programming (MINLP) formulation simultaneously determines cooperative task allocation, data gathering schedule, dynamic aerial data flow, and UAV trajectories to minimize operational energy consumption. A hierarchical two-phase solution procedure that decomposes the original problem into a series of less complex problems and solves them efficiently is developed. The effectiveness of the proposed formulation and the solution procedure is demonstrated through numerical experiments and comparison with benchmarking operational schemes.

Scalable modular synthetic data generation for advancing aerial autonomy

One major barrier to advancing aerial autonomy has been collecting large-scale aerial datasets for training machine learning models. Due to costly and time-consuming real-world data collection through deploying drones, there has been an increasing shift towards using synthetic data for training models in drone applications. However, to increase widespread generalization and transferring models to real-world, increasing the diversity of simulation environments to train a model over all the varieties and augmenting the training data, has been proved to be essential. Current synthetic aerial data generation tools either lack data augmentation or rely heavily on manual workload or real samples for configuring and generating diverse realistic simulation scenes for data collection. These dependencies limit scalability of the data generation workflow. Accordingly, there is a major challenge in balancing generalizability and scalability in synthetic data generation. To address these gaps, we introduce a scalable Aerial Synthetic Data Augmentation (ASDA) framework tailored to aerial autonomy applications. ASDA extends a central data collection engine with two scriptable pipelines that automatically perform scene and data augmentations to generate diverse aerial datasets for different training tasks. ASDA improves data generation workflow efficiency by providing a unified prompt-based interface over integrated pipelines for flexible control. The procedural generative approach of our data augmentation is performant and adaptable to different simulation environments, training tasks and data collection needs. We demonstrate the effectiveness of our method in automatically generating diverse datasets and show its potential for downstream performance optimization. Our work contributes to generating enhanced benchmark datasets for training models that can generalize better to real-world situations.Video:youtube.com/watch?v=eKpOh-K-NfQ

Architecture of Deep Convolutional Encoder-Decoder Networks for Building Footprint Semantic Segmentation

Building extraction from high-resolution aerial images is critical in geospatial applications such as telecommunications, dynamic urban monitoring, updating geographic databases, urban planning, disaster monitoring, and navigation. Automatic building extraction is a massive task because buildings in various places have varied spectral and geometric qualities. As a result, traditional image processing approaches are insufficient for autonomous building extraction from high-resolution aerial imaging applications. Automatic object extraction from high-resolution images has been achieved using semantic segmentation and deep learning models, which have become increasingly important in recent years. In this study, the U-Net model was used for building extraction, initially designed for biomedical image analysis. The encoder part of the U-Net model has been improved with ResNet50, VGG19, VGG16, DenseNet169, and Xception. However, three other models have been implemented to test the performance of the model studied: PSPNet, FPN, and LinkNet. The performance analysis through the intersection of union method has shown that U-Net with the VGG16 encoder presents the best results compared to the other models with a high IoU score of 83.06%. This research aims to examine the effectiveness of these four approaches for extracting buildings from high-resolution aerial data.

Evaluating a signalized intersection performance using unmanned aerial Data

ABSTRACT This paper presents a novel method to compute various measures of effectiveness (MOEs) at a signalized intersection using vehicle trajectory data collected by flying drones. Specifically, this study investigates the use of drone raw data at a busy three-way signalized intersection in Athens, Greece, and builds on the open data initiative of the pNEUMA experiment. Using a microscopic approach and shockwave analysis on data extracted from real-time videos, we estimated the maximum queue length, whether, when, and where a spillback occurred, vehicle stops, vehicle travel time and delay, crash rates, and fuel consumption. The results of the various MOEs were found to be promising. We also demonstrated that estimating MOEs in real-time is achievable using drone data. Such models can track individual vehicle movements within street networks and thus allow the modeler to consider any traffic conditions, ranging from highly under-saturated to highly over-saturated conditions.

Open Science Drone Toolkit: Open source hardware and software for aerial data capture.

Despite the increased access to scientific publications and data as a result of open science initiatives, access to scientific tools remains limited. Uncrewed aerial vehicles (UAVs, or drones) can be a powerful tool for research in disciplines such as agriculture and environmental sciences, but their use in research is currently dominated by proprietary, closed source tools. The objective of this work was to collect, curate, organize and test a set of open source tools for aerial data capture for research purposes. The Open Science Drone Toolkit was built through a collaborative and iterative process by more than 100 people in five countries, and comprises an open-hardware autonomous drone and off-the-shelf hardware, open-source software, and guides and protocols that enable the user to perform all the necessary tasks to obtain aerial data. Data obtained with this toolkit over a wheat field was compared to data from satellite imagery and a commercial hand-held sensor, finding a high correlation for both instruments. Our results demonstrate the possibility of capturing research-grade aerial data using affordable, accessible, and customizable open source software and hardware, and using open workflows.

Traffic Analysis Through Deep-Learning-Based Image Segmentation From UAV Streaming

Alongside many traditional as well as novel applications, in the latest years, drones have been widely adopted as remote sensing platforms for road traffic monitoring in urban areas and on highways. The problem of traffic monitoring on Region of Interest (RoI) based on drone imagery is a challenging task, especially when the surveillance drone is constantly moving. In this work, two specific subtasks have been addressed. The goal of the first stage is to predict the RoI in drone imagery of traffic scenes using deep-learning (DL)-based approaches instead of traditional image processing; in this connection, the goal of the second task is to perform vehicle detection on the selected RoI. To ensure diversity and robustness, drone images with different altitudes, positions, and view points have been considered. To achieve these goals, two custom aerial data sets for RoI extraction and detection were built by collecting aerial sequences from flying unmanned aerial vehicles (UAVs) and by transmitting them to the base station leveraging 5G technology. Two different ad hoc DL-based architectures have been designed for the RoI extraction task to maximize the accuracy and inference speed, respectively, and have been evaluated on two different data sets: 1) a custom-built data set and 2) a Massachusetts roads data set. Finally, the models providing the best performance have been combined to further improve the overall results. Experimental tests show that the proposed framework represents a promising solution for drone-based road traffic monitoring in critical areas, exploiting imagery from a variety of viewing angles and altitudes.