Aerial Drones Research Articles

Aerial drone fleet deployment optimization with endogenous battery replacements for direct delivery of time-sensitive products

Aerial drones offer a distinct potential to reduce the delivery time and energy consumption for the delivery of time-sensitive and small products. However, there is still a need in the relevant industry to understand the performance of drone-based delivery under different business needs and drone operating conditions. We studied a drone deployment optimization problem for direct delivery of time-sensitive products with release dates to customers maintaining a specified time window. This paper presents a new mixed-integer programming model, new valid inequalities, a new greedy heuristic algorithm, and a Genetic algorithm to help business owners optimally schedule and route their drone fleet minimizing the required fleet size, the required number of additional batteries, and total energy consumption. A realistic feature of the optimization method is that instead of replacing the drone battery after each return to the depot, it keeps track of the remaining energy in the drone battery and decides on battery replacements accounting for the drone routing and the user-specified minimum required battery energy. Numerical results based on real data from drone flight tests and prepared food delivery industry provide insights into the effect of different practical drone operating parameters on the required fleet size, the required number of battery replacements, and energy consumption. Results demonstrate that the proposed heuristic algorithm substantially outperforms the accelerated CPLEX in runtime while sacrificing the solution quality by a small amount. Additionally, results show that using a mixed fleet of hexacopter and quadcopter drones reduces the total energy consumption by 48.52% compared to using a homogeneous fleet of only hexacopters.

COMPARATIVE ANALYSIS OF LOW-COST CONSUMER-GRADE AND COMMERCIAL-GRADE DRONES FOR LAND SURVEYING APPLICATIONS

Abstract. This study investigates the feasibility of using low-cost consumer drones, specifically the DJI Mini 2, for land surveys compared to commercial-grade drones like the DJI Phantom 4 Pro V2. The research, conducted within the University of the Philippines - Diliman campus, evaluates the horizontal accuracy of resulting ortho-images and explores the advantages and disadvantages of each platform. Initiated by the recognition of aerial drones' transformative impact on land surveying, particularly acknowledged by the Land Management Bureau in the Philippines, the study addresses concerns about the use of consumer-grade drones for surveying. While some advocate for commercial-grade drones, citing concerns about accuracy, a prior study in India demonstrated the suitability of consumer-grade drones for geomatics applications. The methodology involves creating a flight plan with specified parameters for both drones, establishing Ground Control Points (GCPs), and conducting two flights. The results, processed using UAS Mapping Workflow, indicate comparable errors between the consumer-grade DJI Mini 2 and commercial-grade DJI Phantom 4 Pro V2. Despite slight differences, both drones meet the accuracy standards for land surveys. The study contributes to the discourse on affordable means of secure land tenure, envisioning consumer-grade drones as viable alternatives for cost-effective land surveys. The DJI Mini 2, at a fraction of the cost, demonstrates the capability to achieve accuracy levels comparable to its commercial counterpart. While acknowledging the need for further testing and optimization, the findings suggest the potential of consumer-grade drones in advancing sustainable urban development and poverty reduction through accessible land survey methods.

Active Faults, Kinematics, and Seismotectonic Evolution during Tajogaite Eruption 2021 (La Palma, Canary Islands, Spain)

During the 2021 La Palma strombolian and fissure eruption, two faults were identified that controlled the spatial distribution of earthquake hypocenters and effusive eruptive vents. One of these faults has a NW-SE trend (Tazacorte Fault: TZF) and the other one shows an ENE-WSW trend (Mazo Fault: MZF). Previous works on fault structural analysis in La Palma indicated that the eruption zone was compatible with an extensional tectonic strain ellipsoid which activated normal-strike-slip directional faults at the confluence of TZF and MZF. These fractures were activated during the 2021 Tajogaite eruption, determining the NW-SE and WSW-ENE spatial distribution of vents. Both faults were mapped in real time during the volcanic eruption from fieldwork and remote sensing imagery (aerial drone images). We have collected more than 300 fracture data associated with the effusive vents and post-eruption seismic creep. Since the affected area was densely inhabited, most of these fractures affect houses and infrastructures. Some of the houses affected by the TZF were damaged 9 months after the eruption, although they were not damaged during the eruption. Surprisingly, these houses already had repairs made to the same fractures since 1980, giving information of previous fault creep movement. During the 2021 Tajogaite eruption, shallow seismicity was spatially related to both faults, suggesting a seismic behavior instead of the precedent creep movement. However, the lack of seismicity after the eruption indicates that the faults went back to creep aseismic behavior, similarly to 1980. The mapping and monitoring of these faults (TZF and MZF) is relevant bearing in mind that they have been active since 1980 and the post-eruptive phase of the 2021 volcanic eruption, which has to be included in the land use planning in areas affected by the volcanic eruption and creep movement. Furthermore, both faults could act as seismogenic sources triggering volcanic earthquakes with potential high macroseismic intensities and mass movements. The data presented here show the importance of having this type of study before the onset of the eruption, thus allowing a better interpretation of seismic data during volcanic unrest.

The multidepot drone general routing problem with duration and capacity constraints

AbstractThis paper studies the multidepot drone general routing problem with duration and capacity constraints (MDdGRP), an extension of the classical general routing problem with several depots. A fleet of drones with limited flight time and payload, each one located in a different depot, must jointly perform the service. In this continuous optimization problem, a set of lines of a network has to be serviced and a set of nodes has to be visited to deliver a given commodity. The aim is to find a set of drone routes performing the complete service with the shortest total duration. Aerial drones can enter a line at any point, service a portion of that line, and exit through another point, without following the lines of the network. While this flexibility allows for potentially better solutions, it also adds complexity to the problem. To deal with this, we digitize the MDdGRP instances by approximating each line with a polygonal chain containing a finite number of points that can be used to enter and exit each line. Thus, an instance of a discrete multidepot general routing problem (MDGRP) is obtained. This paper proposes an integer formulation for the MDGRP, some families of valid inequalities, and a branch‐and‐cut algorithm based on the strengthened formulation. Two matheuristic algorithms are presented, focused on sequentially selecting (and adding) some promising intermediate points to enter and exit a line. Instances involving up to six depots, 22 delivery points, and 88 original lines have been generated to test the performance of the methods proposed.

Methods for economic assessment of forest resource degradation: A systematic review

Forest degradation is one of the challenges facing the planet today. Several methods have been used to measure forest degradation, including spatiotemporal model analysis, satellite analysis, remote sensing, time series data, geospatial techniques, and most recently aerial drone imagery. However, few studies have used economic valuation methods to assess forest degradation. Therefore, this research aimed to identify the methods used the economic assessment of forest degradation. This systematic review was carried out using PRISMA guidelines. Research articles on the economic valuation of forest resource loss, published from 2015 to 2022, were electronically collected from three databases. Three independent reviewers, with the third acting as referee, inventoried articles, extracted data, and assessed the risk of bias in the articles included in the study. A total of 10,095 articles were identified, including one article from the grey literature. Only five articles met the eligibility criteria. A qualitative content analysis was performed on the extracted data. The selected articles used various methods. However, only a few articles used the contingent valuation method, even though this is indicated for estimating the highest economic value of forests. Based on forest functions, the articles evaluated erosion due to the absence of trees, wood loss, recreation areas and externalities due to forest loss, air quality, water regulation, food supply, and wildlife. The main limitation of this review was the small number of studies included, which may have affected the findings. The study protocol is registered in PROSPERO under the number CRD42021223242

An algorithm for detecting dense small objects in aerial photography based on coordinate position attention module

AbstractTo address the challenges of detecting a large number of objects and a high proportion of small objects in aerial drone imagery, an aerial dense small object detection algorithm called coordinate position attention module you only look once (CPAM‐YOLO) is proposed based on the coordinate position attention module (CPAM). In the backbone network of CPAM‐YOLO, a CPAM is proposed and embedded that decomposes channel attention into two 1D feature encoding processes, and selectively combines the features of each position through the weighted sum of all position features. Finally, features are aggregated along two spatial directions, increasing the effective information utilization of input feature positions and channels. The backbone network, feature enhancement network, and detection heads have been optimized to improve detection accuracy while ensuring a lightweight detection network. Using lightweight backbone networks to significantly reduce the number of parameters while using high‐resolution feature enhancement networks to retain more semantic and detailed features. The algorithm's performance was evaluated using the publicly available VisDrone2019 dataset. Compared to the baseline network YOLOv5l, CPAM‐YOLO achieved a 4.5% improvement in mAP0.5 and a 3.2% improvement in mAP0.95. These experimental results demonstrate the strong practicality of the CPAM‐YOLO object detection network for detecting dense small objects in aerial image.

Debris flow at Luib on the A87 strategic road: Isle of Skye, Scotland

A rainfall-induced debris flow occurred on 30 September 2022, closing the A87 trunk (strategic) road on the Isle of Skye for around 2 h. Ground-based and aerial drone inspections were used to describe the event and a comprehensive rainfall analysis was undertaken. It is concluded that the event was triggered by overland water flow from the slopes above the source zone as a result of high rainfall. The resulting translational slide transitioned into a debris flow that reached a pre-existing channel where entrained superficial deposits were deposited, adding to pre-existing levee structures before reaching the road. The rainfall that led to the debris flow was high intensity–low duration; the rainfall analysis encompasses annual, monthly, daily, hourly and 15 min data, each aiding understanding of the climate of the area and/or the event rainfall. Comparison is made with a rainfall event the following month, which was of a greater magnitude but of longer duration and lower intensity. The hazard at this location is a concern but the risk is emphasized by the proximity of the road to the steep slopes above and the lack of a viable detour. Thematic collection: This article is part of the Geo-resilience and infrastructure collection available at: https://www.lyellcollection.org/topic/collections/geo-resilience-and-infrastructure

Zero emission freight transport and impacts on last mile delivery


 
 
 This literature review explores mainly the challenges and potential solutions in urban freight transportation, for last-mile logistics. The growing number of delivery vehicles in urban areas has led to problems such as traffic congestion, competition for parking spaces, noise, and air pollution. To address these issues, the paper focuses on the last mile problem in transportation logistics and the potential of disruptive innovations, a potential of disruptive innovations such as Unmanned Aerial Vehicles (UAVs) and Autonomous Delivery Robots (ADRs) as alternatives to traditional Internal Combustion Engine (ICE) vehicles. It highlights the drastic operational differences between aerial drones and ADRs and the need for changes in operational procedures to fully leverage these technologies. The study also discusses routing strategies and delivery routes, emphasizing the shift from one-route delivery to a point-to- point model with the advent of autonomous vehicle technologies. The paper concludes that while these technologies can reduce costs and emissions, their effectiveness depends on the specific urban setting and operational factors. 
 
 

Optimal sway motion reduction in forestry cranes.

The paper introduces a novel optimal feedforward controller for Hydraulic manipulators equipped with a passive grapple, addressing the issue of sway during and after movement. The controller is specifically applied to a forwarder machine used in forestry for log-loading tasks. The controller is designed for smooth operation, low computational demands, and efficient sway damping. Customizable parameters allow adjustments to suit operator preferences. The implementation was carried out using the Amesim model of a forwarder. Simulation results indicate a significant reduction in sway motions, averaging a decrease of more than 60%. This performance was achieved without the need for additional sway-detection sensors, which simplifies the system design and reduces costs. The proposed method demonstrates versatility and broad applicability, offering a new framework for anti-sway controllers in various fields such as construction cranes, forestry vehicles, aerial drones, and other robotic manipulators with passive end-effectors. This adaptability could lead to significant advances in safety and efficiency.

Research on efficient detection network method for remote sensing images based on self attention mechanism

Remote sensing images are widely used in aerial drones, satellites, and other fields. However, traditional object detection methods face challenges of low efficiency and accuracy due to the diverse scales, numerous target types, and complex backgrounds of remote sensing images. To address these issues, this paper proposes a remote sensing image detection network based on self-attention, which adaptively learns the relative importance of pixels to achieve more precise and efficient object detection in remote sensing images, meeting the demands of large-scale remote sensing image processing. Firstly, through end-to-end training, the object detection network is optimized as a whole to directly output detection results from the input remote sensing images, eliminating the need for additional intermediate steps. This not only reduces the impact of information loss and inconsistency but also simplifies the entire detection process. Secondly, we integrate the Faster R-CNN architecture as the foundation, combining region extraction and object classification into a unified process. Lastly, we embed self-attention mechanisms at different levels of the Faster R-CNN to progressively extract multi-scale and multi-level feature information, enhancing the network's ability to learn the correlation of information from different positions in the image, automatically capturing the relationships between objects, and improving the accuracy of object detection. This significantly reduces redundant computation, making it more efficient for large-scale remote sensing image processing. Experimental verification demonstrates that this approach outperforms traditional methods in terms of detection accuracy and efficiency, better addressing the particularities of remote sensing images, and providing an efficient and precise solution for aerial drone and satellite image processing. Remote sensing image detection has become one of the research hotspots in the field of remote sensing, holding significant theoretical significance and practical application value.

Quantitative Analysis of Dangerous Driving Behavior through Drone Footage using Mathematical Modeling

This research paper presents a method for analyzing driving using aerial drone images of traffic scenes. By applying principles of motion and calculus we introduce an approach that provides insights into driving dynamics without relying on complex vehicle markings or annotations. This method offers a perspective on road behaviors, allowing for an understanding of potential risks and ultimately enhancing road safety. In our experiment we examined a series of drone images capturing traffic scenarios. We provided a procedure and demonstration of applications of physics and calculus to reconstruct traffic scenes. We constructed a trajectory mechanism that uses all of said concepts to form a concrete plan to reconstruct scenes, using real drone footage with bird’s eye view. The combination of concepts with aerial image analysis showcases the potential for objective and data driven assessments of reckless driving behaviors. Overall this paper serves as a starting point for advancements, in utilizing models to analyze traffic patterns. It contributes to promoting road practices and improving transportation management through evidence based approaches.

An Effective Attention-based CNN Model for Fire Detection in Adverse Weather Conditions

Worldwide, fire disasters cause social, economic, and environmental damage, so their early detection and instant response is crucial for saving lives and property, which is possible via a soft computing approach. Researchers have therefore proposed different artificial intelligence-based approaches to detect fire at its early stages. However, several factors including domain shift problems, limited performance, and model complexity reduced their effectiveness in a real-world environment. To address these challenges, this study proposes an extensive data augmentation approach to tackle the domain shift problem by employing a mono-depth and atmospheric scattering model to generate and synthesise hazy, foggy, and night-time images. To further enhance the detection of small fire-affected areas, a refined DenseNet backbone is employed. This network is fine-tuned to extract intricate spatial details across different scales and establish meaningful connections between them. Subsequently, we introduce a soft attention mechanism into the backbone network. This entails utilising a 3D convolutional operation within the proposed attention framework to capture more extensive spatial features, which contributes to an enhanced discriminatory capability for detecting fire. Moreover, we have created a novel dataset explicitly designed for effective fire detection from a remote sensing perspective, known as the aerial drone and satellite fire (ADSF) dataset, encompasses images captured by drones and satellites. This broader range of image sources affords coverage across a wider expanse of geographical locations, surpassing the capabilities of CCTV cameras. Our proposed model achieves significant accuracy improvements, surpassing the state-of-the-art (SOTA) on both original and augmented datasets. For the Yar, FD, DFAN, and ADSF original datasets, we observed average accuracy increases of 1.25%, 1.95%, 2%, and 1.5%, respectively. In the comparisons with augmented versions of these datasets, our model consistently outperforms the SOTA, with accuracy improvements of 0.7, 1.97%, 1.42%, and 1.53%, respectively. Additionally, in our ablation studies, the proposed model excels in terms of both performance and resource efficiency. It achieves higher frame rates of 22.0, 72.5, and 7.0 FPS on CPU, GPU, and Raspberry Pi, respectively, with just 7.2 million learning parameters, making it well-suited for real-time deployment on edge devices. Finally, a Grad-CAM XAI method is utilised to visualise and highlight the most important regions in images emphasized by the model. The code and dataset are publicly available at https://github.com/Hikmat-Yar/ISPRS-Fire-Detection.

Countering Unmanned Aerial Systems (UAS) in Military Operations

Although contemporary unmanned systems are used in every environment, they overwhelmingly dominate the airspace. They are commonly called aerial drones or unmanned aerial vehicles (UAVs), while the systems supporting and controlling UAVs are called unmanned aerial systems (UASs). The widespread adoption of aerial drone technology has led to their increasing use on the battlefield. Therefore, finding an effective counter-drone (counter - UAS) weapon has become a serious challenge. This task is particularly difficult because we are dealing with a huge variety of unmanned aerial systems while their numbers are increasing at a record-breaking pace. At this point, there are also no fully proven counter-drone solutions, with most existing systems still in the research phase or newly introduced prototypes. The emerging problems and the set of uncertainties associated with them have become the fundamental basis for addressing and presenting this issue in this publication. This article discusses the crucial subject of defense against unmanned aerial systems from the perspective of modern warfare. The author addresses the question of how to plan and implement counter-drone defense in military operations, as well as what technical solutions could be useful for this purpose. Throughout this study, the author employed various methods, including analysis, synthesis, comparison, and case studies, primarily by examining available open-source information on recent armed conflicts in Nagorno-Karabakh and the ongoing conflict in Ukraine. These methods enabled the author to validate his main hypothesis: there are currently no effective drone countermeasures systems, particularly against drones that can operate autonomously, utilize artificial intelligence algorithms for guidance and decision-making, or engage in large-scale attacks, known as swarms. Simultaneously, the author points out what new technical solutions should be developed to enable effective countermeasures and what tactics should be incorporated into military training to address this threat. Additionally, the article discusses to what extent existing air defense systems can be adapted for counter-drone defense and whether there are effective methods of force protection against such threats.

HilbertSCNet: Self-attention networks for small target segmentation of aerial drone images

In recent years, many advanced semantic segmentation algorithms have made great progress in common datasets. However, in the drones aerial photography dataset, since some of the segmentation targets are very small in such high-resolution images, these current algorithms are not designed specifically for them resulting in their mediocre performance in the drones aerial photography dataset. To address the problem of difficult segmentation of these small targets, this paper proposes a new semantic segmentation network model-HilbertSCNet: (1) Combine the image dimensionality reduction algorithm of Hilbert curve traversal and the idea of dual pathway to design a new spatial computation module to solve the problem of small target information is easily lost in the downsampling; (2) Add a windowing algorithm in the image dimensionality reduction algorithm of Hilbert curve traversal to solve the problem that the computational complexity of the module is too high, which makes the module cannot be applied to high-resolution feature maps. Experiments show that the proposed network is very effective for segmentation of small targets under high-resolution maps such as drone aerial photography, with certain superiority and generalization. The overall segmentation performance is improved compared to the current new network, including 1.76% better than OCNet in MIoU and 4.55% better than the dedicated drone algorithm RCCT-ASPPNet.

A Novel Scouring Method to Monitor Nocturnal Mammals Using Uncrewed Aerial Vehicles and Thermal Cameras—A Comparison to Line Transect Spotlight Counts

Wildlife abundance surveys are important tools for making decisions regarding nature conservation and management. Cryptic and nocturnal mammals can be difficult to monitor, and methods to obtain more accurate data on density and population trends of these species are needed. We propose a novel monitoring method using an aerial drone with a laser rangefinder and high zoom capabilities for thermal imagery. By manually operating the drone, the survey area can be initially scanned in a radius of several kilometers, and when a point of interest is observed, animals could be identified from up to one kilometer away by zooming in while the drone maintains an altitude of 120 m. With the laser rangefinder, a precise coordinate of the detected animal could be recorded instantly. Over ten surveys, the scouring drone method recorded significantly more hares than traditional transect spotlight count surveys, conducted by trained volunteers scanning the same farmland area within the same timeframe (p = 0.002, Wilcoxon paired rank test). The difference between the drone method and the transect spotlight method was hare density-dependent (R = 0.45, p = 0.19, Pearson’s product–moment correlation); the larger the density of hares, the larger the difference between the two methods to the benefit of the drone method. There was a linear relation between the records of deer by the drone and by spotlight (R = 0.69, p = 0.027), while no relation was found between the records of carnivores by drone and spotlight counts. This may be due to carnivores’ speed and vigilance or lack of data. Furthermore, the drone method could cover up to three times the area within the same timeframe as the transect spotlight counts.

Innovative research on the application of TIRZ theory to multi-mode UAVs

According to the current development of Unmanned Aerial Vehicles (UAVs), it can be known that there are no cases where underwater drones and aerial drones are used in parallel. They are all affected by air and water resistance and are easily disturbed in various environments, making it difficult to move effectively. This research uses TRIZ theory to create a multi-modal unmanned vehicle to innovate and improve the existing situation. The type of equipment produced through this research can effectively move in the air, land, and water. The scope includes TRIZ theory, bionic technology, multi-modal innovative design, research, and application of UAV innovation, and monitoring the improvement of its operational resistance through fluid analysis. The output of this research will effectively promote the development of Multi-mode UAVs and improve the problems of existing UAVs.

A Dense Small Object Detection Algorithm Based on a Global Normalization Attention Mechanism

To address the challenges of detecting a large number of objects and a high proportion of small objects in aerial drone imagery, we proposed an aerial dense small object detection algorithm called Global Normalization Attention Mechanism You Only Look Once (GNYL) based on the Global Normalization Attention Mechanism. In the backbone network of GNYL, we embedded a GNAM (Global Normalization Attention Mechanism) that explores channel attention features and spatial attention features from input features in a concatenated manner. It utilizes batch normalization’s scale factors to suppress irrelevant channels or pixels. Furthermore, the spatial attention sub-module introduces a three-dimensional arrangement with a multi-layer perceptron to reduce information loss and amplify global interaction representation. Finally, the computed attention weights are weighted to form the global normalized attention weights, which increases the utilization of effective information in input feature channels and spatial dimensions. We have optimized the backbone network, feature enhancement network, and detection heads to improve detection accuracy while ensuring a lightweight detection network. Specifically, we have added a small object detection layer to enhance the localization accuracy for the abundant small objects in aerial imagery. The algorithm’s performance was evaluated using the publicly available VisDrone2019 dataset. Compared to the baseline network YOLOv8l, GNYL achieved a 7.2% improvement in mAP0.5 and a 5.0% improvement in mAP0.95. Compared to CDNet, GNYL showed a 14.5% improvement in mAP0.5 and a 9.1% improvement in mAP0.95. These experimental results demonstrate the strong practicality of the GNYL object detection network for detecting dense small objects in the aerial imagery captured by unmanned aerial vehicles.

Aerial Drone Imaging in Alongshore Marine Ecosystems: Small-Scale Detection of a Coastal Spring System in the North-Eastern Adriatic Sea

The eastern coastline of the Gulf of Trieste (north-eastern Adriatic Sea, Italy) is characterized by the occurrence of coastal and submarine freshwater springs of karstic origin. In one of these areas, we performed a survey with a drone with a thermal camera installed, in tandem with in situ oceanographic sampling with a CTD. Drone images revealed a small time-space scale (i.e., up to a few meters) phenomenon of freshwater plumes floating over seawater. Comparing sea surface temperature data with those acquired in situ revealed that the phenomenon was not clearly detectable by the classical oceanographic monitoring, this surface spring freshwater layer being too thin. Instead, the drone’s thermal camera detected these dynamics with great accuracy, indicating that aerial drones can be efficiently used for studying fine-scale events involving surface waters (e.g., spills/pollution). The experience gained allowed us to discuss some of the advantages and disadvantages of using drone thermal imaging for monitoring alongshore areas.

Functional additive manufacturing of large-size metastructure with efficient electromagnetic absorption and mechanical adaptation

Broadband microwave absorption as a hot research topic plays important roles on electromagnetic compatibility, electromagnetic radiation protection and radar detection disguise. However, traditional molding method is difficult to fabricate large-size curving structures for broadband microwave absorption. Herein, the material extrusion with high-speed printing process was effective to manufacture large-size metastructures for broadband microwave absorption. The polylactic acid was mixed and extruded with nano pyrolytic carbon in high temperature to manufacture functional filaments with high dielectric loss. The printed metastructure integrated with 0.5 mm quartz fiber reinforced composite (QFRC) achieved -10 dB broadband microwave absorption in 3.03-18 GHz and effective load bearing performance in tensile and flexural tests. Severe impedance mismatch brought by QFRC was overcome by appropriate structure design and high-speed printing technique. The proposed design-manufacturing closed loop is a promising approach to fabricate aircraft skin with broadband microwave absorption performance and suitable aerodynamic properties for heavy unmanned aerial drones.

Drones for litter monitoring on coasts and rivers: suitable flight altitude and image resolution

Multirotor drones can be efficiently used to monitor macro-litter in coastal and riverine environments. Litter on beaches, dunes and riverbanks, along with floating litter on coastal and river waters, can be spotted and mapped from aerial drone images. Items detection and classification are prone to image resolution, which is expressed in terms of Ground Sampling Distance (GSD). The GSD is determined by drone flight altitude and camera properties. This paper investigates what is a suitable GSD value for litter survey. Drone flight altitude and camera setup should be chosen to obtain a GSD between 0.5 cm/px and 1.25 cm/px. Within this range, the lowest GSD allows litter categorization and classification, whereas the highest value should be adopted for a coarser litter census. In the vision of drawing up a global protocol for drone-based litter surveys, this work sets the ground for homogenizing data collection and litter assessments.