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Systematic Review

Mapping Drone Applications in Rural and Regional Cities: A Scoping Review of the Australian State of Practice

by
Christine Steinmetz-Weiss
1,*,
Nancy Marshall
2,
Kate Bishop
1 and
Yuan Wei
2
1
School of Built Environment, UNSW Sydney, Kensington, NSW 2052, Australia
2
Sydney School of Architecture, Design and Planning, The University of Sydney, Darlington, NSW 2008, Australia
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(15), 8519; https://doi.org/10.3390/app15158519 (registering DOI)
Submission received: 26 May 2025 / Revised: 21 July 2025 / Accepted: 24 July 2025 / Published: 31 July 2025
(This article belongs to the Special Issue Advances in Fuzzy Optimization for Transportation Planning Under Uncertainty)
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Abstract

Consumer-accessible and user-friendly smart products such as unmanned aerial vehicles (UAVs), or drones, have become widely used, adaptable, and acceptable devices to observe, assess, measure, and explore urban and natural environments. A drone’s relatively low cost and flexibility in the level of expertise required to operate it has enabled users from novice to industry professionals to adapt a malleable technology to various disciplines. This review examines the academic literature and maps how drones are currently being used in 93 rural and regional city councils in New South Wales, Australia. Through a systematic review of the academic literature and scrutiny of current drone use in these councils using publicly available information found on council websites, findings reveal potential uses of drone technology for local governments who want to engage with smart technology devices. We looked at how drones were being used in the management of the council’s environment; health and safety initiatives; infrastructure; planning; social and community programmes; and waste and recycling. These findings suggest that drone technology is increasingly being utilised in rural and regional areas. While the focus is on rural and regional New South Wales, a review of the academic literature and local council websites provides a snapshot of drone use examples that holds global relevance for local councils in urban and remote areas seeking to incorporate drone technology into their daily practice of city, town, or region governance.

1. Introduction

Unmanned aerial vehicles (UAVs), drones, are a smart technology revolutionising a multitude of sectors with their advanced aerial capabilities and data-gathering efficiency. In recent years, the use of drone technology in the built environment has seen significant innovations, particularly in urban areas where the benefits of these unmanned aerial vehicles extend to various sectors such as arts and entertainment, real estate, crime prevention and construction [1,2,3]. Despite the burgeoning use of drones in urban landscapes, their use in rural areas remains comparatively limited and constrained to a narrow range of activities (e.g., crop monitoring and aerial surveying [4,5,6,7,8]). In less urbanised areas, smart technology can play a critical role in addressing unique challenges and opportunities in the fields of agricultural productivity, transportation of goods and services, remote healthcare, and digital education access [4]. Undoubtedly, the positive impacts of smart technologies could be just as important in rural and regional areas as in urban areas [9,10]. The disparity between extensive adaptation of drone use in urban areas versus non-urban areas is not just a reflection of differing rural and regional needs but also indicates a gap in exploring the full potential of drones. In Australia, there is a need to shift the conversation of smart technology adaptation from the urban to the rural and regional [4] for balanced technological advancement across diverse geographies.
The terms rural and regional are often used interchangeably [11] and are vaguely described as all areas outside of metropolitan boundaries. “Rural is often associated with a high proportion of a population in primary industries such as farming, mining and fishing and can relate to the population of a location, socio-cultural or ecological factors [12], pp. 5–6. Defining “regional” is much more complicated; in Australia, regions are determined by their purpose and they are “social constructs … that result from our attempts to impose some spatial order on surroundings” [13], p. 46. There are regional areas that support rural and remote areas and act as centres for small industry or transportation hubs and there are regions that cross state borders to service areas in both states with limited access to services. This article applies the regional classifications from the Standard Geographical Classification of the Australian Bureau of Statistics [14].
Unlike urban centres, where technological integration is ubiquitous, rural and regional areas present untapped opportunities for drone technology, such as environmental conservation, emergency medical deliveries, and connectivity solutions. Also, the gap between the theoretical potential of drone technology, as highlighted in research, and its practical adoption by rural and regional councils is significant and noteworthy. Academic research often showcases drones as multifaceted tools capable of transforming an industry [15,16]; however, in the context of rural and regional councils, the implementation of drones is frequently more conservative and focused on specific and limited tasks.
In Australia, local councils are tasked with responsibilities that serve as the backbone of community welfare and development. Economic duties range from finance management and fostering economic development, supporting small businesses and promoting inter-state and international tourism. Environmental stewardship also falls within the purview of a local council’s remit requiring them to work towards biodiversity conservation, be accountable for biosecurity hazards, mitigate climate change, and make fiscally and sustainably responsible decisions. In governance, councils tackle issues from policy development to structural reform. Councils also focus on health and safety, including emergency management and crime prevention, oversee the maintenance and development of essential infrastructure such as managing building regulations, and preserve heritage. Social and community engagement responsibilities are extensive, covering Indigenous affairs, ageing, arts and culture, and support for children, youth, and diverse cultural groups. Moreover, they handle the critical operations of waste management and recycling, ensuring resource recovery and sustainability. For councils abundant in resources, daily operations and upskilling a workforce is business-as-usual; however, for councils with less access to resources (due to geography, funding or staff), some of these responsibilities receive lower priority. Incorporating new technologies, such as drones, into council operations may be viewed as a practical innovation by resource-rich councils, but for smaller councils with limited funding, staffing, or geographical challenges, such advancements can often be out of reach or considered an unaffordable luxury [17].
This review seeks to understand what the current research priorities are for academics and how rural and regional councils are currently harnessing and anticipating harnessing the capabilities of drones to address their specific challenges associated with service delivery and operational efficiency. This paper is guided by a comprehensive review of the current literature on drone technology and complemented with an analysis of existing drone uses in 93 rural and regional councils in New South Wales (NSW) as it is discussed in publicly available information. These 93 councils include all NSW councils except for those in the Sydney basin, Newcastle, and Wollongong. While the local context varies, the insights gained from this review are not confined to NSW. The identified future possibilities for drone use hold potential relevance for less urbanised areas worldwide, suggesting a broader applicability of findings that transcend the experience in regional NSW. The authors are aware of regulatory frameworks and legal barriers that impact drone use [18,19]. Throughout Australia, for example, the peak body for drone pilots is the Civil Aviation Safety Authority (CASA). This requires users to complete certain courses and acquire a licence to operate drones in public spaces for commercial purposes [20]. These topics are beyond the scope of this paper but merit further research.

2. Methodology and Methods

This research employs a systematic review methodology to examine the current uses and future possibilities of drone technology in regional NSW councils. Undoubtedly, drone use is also employed by the military [21]. However, this information is not accessible to the public and, therefore, not included in this review. The first part of this study involves a systematic review of the academic literature, following the PRISMA 2020 framework, to identify key themes in drone uses relevant to rural and regional areas. This process includes identifying, screening, and selecting studies from Scopus and Web of Science databases based on predefined inclusion and exclusion criteria. The second part of this study involves a review of publicly available council information, including media releases and council publications, to assess how drones are currently being used across 93 regional councils in NSW. This analysis categorises drone-related initiatives into five key areas: safety policies, operational uses, educational initiatives, research partnerships, and future considerations. By integrating these two approaches, this research aims to compare theoretical insights with real-world implementations, identifying gaps between academic discourse and practical adoption. The findings will inform discussions on the feasibility of drone technology for local governments, particularly in resource-constrained councils. The research design diagram (Figure 1) represents the relationship between literature findings, current drone uses in NSW councils, and the broader framework of local government responsibilities, highlighting the intersection of present use and future potential. According to Local Government NSW [22], a peak body representing NSW local councils, the major responsibilities of a local council include economic, environment, governance, health and safety, infrastructure, planning, social and community, and waste and recycling. Across these diverse responsibilities, councils also maintain critical technical functions essential for service delivery and operational efficiency.

2.1. Part 1: Review of the Academic Literature

To ensure a comprehensive understanding of the current state and potential of drone technology uses in rural and regional areas, this paper adopts a systematic review methodology guided by the Systematic Reviews and Meta-Analyses (PRISMA 2020) statement [23]. This method offers several benefits: it allows for thematic analysis to map the current use of drones while also serving as a retrospective base for understanding their documented uses in the academic literature. Such an approach provides a structured foundation upon which practical information can later be integrated, enabling a robust synthesis of both theoretical insights and real-world uses. This method has been widely used in the discipline of built environments [24,25,26,27].
The PRISMA statement, initially published in 2009, aimed to provide transparent and clearly articulated guidelines for authors to apply when undertaking literature searches [28]. Considering technological advancements with search engines, in systematic review methodologies, and terminology over the past decade, a revision to the guidelines became essential. The PRISMA 2020 introduced updated reporting guidance that incorporates progress in the methods used to identify, select, assess, and synthesise studies [23]. The PRISMA 2020 statement features a standard three-stage flow diagram consisting of identification, screening, and inclusion (see Figure 2). For this research, Scopus and Web of Science databases were used to search for relevant literature in the identification stage. Keywords were searched in full text to find relevant studies: (drone) OR (drones) OR (unmanned aerial vehicles) OR (unmanned aircraft systems) OR (remotely piloted aircraft) AND (built environment) AND (rural areas) OR (regional areas) AND NOT (urban).
Table 1 sets out the criteria for the inclusion and exclusion of documents in the literature review. Articles published from 2018 to 2023 and written in English were the primary document type. Given the rapid advancements in drone technology, this review focuses on studies published within the recent five years to ensure the analysis reflects current innovations and uses. The review focused on articles’ subject areas in Scopus, such as environmental science, social sciences, and multidisciplinary fields, as well as business, management, accounting, arts, humanities, and agricultural and biological sciences. For Web of Science, categories included environmental sciences, environmental studies, and those pertaining to regional urban planning and urban studies. Documents such as conference papers or book chapters, publications prior to 2018, and those in languages other than English were excluded. Articles from unrelated subject areas, including mathematics, medicine, neuroscience, toxicology, limnology, and polymer science, were also omitted from the review.
Identifying relevant studies commenced with a comprehensive search across two databases, yielding a total of 354 records—135 from Scopus and 219 from Web of Science. Prior to screening, three duplicate records were removed. This meticulous approach ensured that only the most pertinent studies were considered for inclusion in the review, streamlining the focus to those that specifically aligned with the objectives of the research and the responsibilities of local councils.

2.2. Part 2: Review of Publicly Available Council Information

One of the aims of the literature search was to extrapolate the breadth of ways drones have been used and compare those uses with how the 93 NSW local rural councils were currently using or had used drones as part of the management of the local government area. The second part of the literature search was a comprehensive examination of drone uses across 93 regional councils in NSW through a review of media releases and council publications, including their websites. The council search did not adhere to PRISMA systematic literature search protocol as discussed earlier. In the PRISMA method, we used Scopus and Web of Science as search engines for the academic literature. However, due to the nature of council information, we went to each of the 93 regional council websites to search for keywords. Hence, the search protocol varies in two parts. It is difficult to ensure the accuracy and reliability of information collected from publicly available data on council websites as we can only verify website management from what is published on their homepage. Social media platforms, such as Facebook, Instagram, or LinkedIn, were not included. As council webpages are a public-facing, government means of communication for local community members and other stakeholders, we assumed that information would be current and that council initiatives would respond to relevant federal government policies and objectives. These sources were carefully examined using targeted keywords such as “drone,” “UAV,” and related terms to locate relevant content. Since this information is publicly published by councils, it reflects official records and statements, minimising the risk of inaccuracies. The council-specific literature extracted from their websites was coded as “drone-related” council initiatives. The search included systematically reviewing official communications such external and in-house publications authored by the council available for public viewing on their website.
A map of the drone discussion and types of use as currently acknowledged in Council information was developed as part of this study. Using the five categories derived from the review of the information itself, we plotted a map of drone use in NSW regional councils.

3. Results

3.1. Part 1: Academic Literature Review Findings

Table 2 provides a thematic overview of literature review findings from scholarly publications, categorising the major responsibilities of councils into specific detailed aspects, along with associated sources from the academic literature. This table captures the major areas reflected in the literature at present including environmental concerns, health and safety, infrastructure, planning, social and community responsibilities, and waste and recycling. Instances without listed literature indicate that no relevant research could be identified at the time of completion of this review. Following the table is a discussion of literature review findings organised by topic as presented in the table.

3.1.1. Environment

Biodiversity
Drones are revolutionising the study of ecosystems and wildlife. For example, drones equipped with multispectral sensors have been effectively deployed over coral reefs, providing high-resolution data that can be significantly improved through optimised distortion correction methods [33]. Drones have been found to complement biodiversity studies by providing unique aerial perspectives, enabling more accurate and comprehensive observations of humpback whales and other cetacean species [30]. Even when equipped with basic consumer-level digital cameras, drones can play a crucial role in facilitating large-scale wildlife censuses by enabling the efficient and safe estimation of animal populations in large reserves, significantly enhancing the effectiveness in detecting animals while reducing false positives and manual verification workload [29]. Furthermore, drones played a critical role in monitoring bird populations, offering a more effective and often less invasive method compared to traditional human observation [35].
Biosecurity
In biosecurity, drones are making significant strides in agricultural monitoring. Researchers assessed the accuracy of mapping maize-weed infestations using drone and satellite imagery [36]. The results showed that there is a higher accuracy of detection of weeds during the mid-to-late stages of maize crop growth. Furthermore, the images captured by drone reveal higher accuracy than satellite imagery.
Climate Change
To mitigate climate change, drones have been used for evidence-based environmental monitoring. Researchers combined in situ water temperature observations with thermal infrared imagery collected via drones to measure the stream temperature that is relevant to water quality [37]. Furthermore, in regions with low forest cover, elevated river temperature associated with climatic warming is often related to low bankside vegetation cover. To analyse the river temperature, researchers use drones to capture images of canopy cover and tree height data [38]. Also, the use of a low-cost drone enhances stream monitoring and management, offering high-resolution imagery and comprehensive data that effectively parallel traditional methods. With drone assistance, geographical measurements of stream attributes such as width, course, physical variation, water flow, and gravel coverage, present a viable, more efficient alternative or supplementary tool for mapping open stream types [41]. Tucci et al. [39] adopted drones with thermal sensors to understand microclimate dynamics for a vineyard. The aerial surveys revealed significant temperature variations within the vineyard, highlighting the impact of dry-stone terracing on local climate, thereby justifying further research in this area using advanced drone and sensor technologies.
Sustainable Solutions
Drones are a viable technology to facilitate sustainable practices in various sectors. Drones offer versatile solutions for tasks like ecological monitoring and environmental control over land and water surfaces, including the integration of small-class photography systems for aerial imaging [54]. Drones equipped with advanced sensors and image processing software emerge as a valuable enhancement for rangeland inventory and monitoring, offering higher confidence in landscape-scale indicator estimations and potential to complement traditional field methods [47]. Windle and Silsbe [55] found that drones equipped with multispectral sensors originally designed for terrestrial use are effectively employed to enhance water quality monitoring in coastal waters by accurately measuring and analysing radiance data, thereby facilitating improved assessment and management of water quality trends and risks. Sensors mounted on drones on a farm collect various environmental parameters and send them to a platform that can help farmers, government agencies or manufacturers to predict environmental data over the geographically large farm field [50]. Additionally, drones equipped with ground-penetrating radar can significantly aid in high-resolution mapping of soil moisture at a field scale, proving beneficial for precision agriculture and environmental monitoring [44]. Fathipoor et al. [43] found that drones can effectively predict corn forage yield and model plant height, enhancing precision farming through cost-effective, high-resolution data.
Drones are equipped with a software system for efficient charging station placement and path specification, enhancing agricultural surveillance by optimising coverage of large, inaccessible areas with 70–90% optimal performance in minimising mission time [49]. Researchers used drones to capture aerial images to establish a digital surface model, combining with the soil sample test results in the laboratory in soil survey [59]. Drones can also assist in identifying soil erosion hotspots and assessing the vulnerability of different regions, thus contribute to the development of effective soil and water conservation strategies [63]. Drones have also been used for monitoring mine restoration and assessing the progression of mined sites [46]. Drone mapping provides land cover maps, drainage network evaluation maps, detection of erosion problems, and volume variation calculations. Kim, D, Kim, S and Back, K [56] confirmed the efficacy of drone photogrammetry in monitoring changes and rehabilitation in mines, demonstrating its potential for widespread use in generating and analysing substantial spatial data.
Forest Classification and Management
Drones are playing a key role in forest area classification and management. Drones equipped with advanced remote sensing capabilities and programmed with deep learning algorithms, that are able to enhance the accuracy and efficiency of forest area classification and management, particularly in areas threatened by deforestation [74]. The use of drones with multispectral sensors can eliminate some of the major limitations associated with satellite imagery, particularly when dealing with tiny plants such as native desert vegetation [45]. The use of consumer-grade cameras mounted on drones offers an affordable and accurate option of investigating tree crowns across specific areas [69]. Through the drone’s aerial system imagery, researchers conducted tree crown mapping and quantified percent canopy mortality [70]. Dixon et al. [51] presented a method to combine drone and satellite images to produce landscape-scale map of flowering dynamics so that to better understand the condition of forest ecosystems. Research found that drones equipped with sub-metre resolution capabilities, are vital in accurately mapping vegetation in spatially heterogeneous landscapes, offering enhanced precision in training dataset construction and filling gaps where other high-resolution data is unavailable [68]. Furthermore, drones mounted with colour sensors can be used to detect some vegetation diseases in forests, such as pine wilt [76].
Disaster Management
Drones are being used in disaster management prior to or post events. The geo-surveying images collected by drones, combined with other primary and secondary data can be input to GIS software (i.e., ArcGIS, QGIS, AutoCAD Map 3D) to digitally map the landslide prone areas [88]. Post landslide events, drones can be used to investigate the damage and impact areas. For example, Koutalakis et al. [81] conducted morphometric measurements in a landslide event using orthoimages captured by drones. The collected data was then analysed in ArcGIS to digitise and estimate the morphometric parameters, including the area and volume of the landslides. In many cases, drones were proven to be effective in disaster victim identification process [83,117]. For example, drone-based geospatial surveillance presented advantages in guiding rescue operations in darkness caused by power cuts during natural disaster events [84]. Also, through the integration of drone laser scanning and mobile laser scanning, drones are instrumental in quantifying post-fire tree structures with high precision [87]. This advanced technique facilitates the development of site-specific forest management plans, especially in controlling burn severities by managing crown fuel abundance and configuration.

3.1.2. Health and Safety

Studies revealed that drones with multi-gas detectors and high-definition video cameras significantly aid rescuers in environments with fire, toxic, or flammable hazards, operating effectively even without visual contact or GPS signals, due to their high-power engines and multiple propellers [89]. Drones can also help in reducing distribution risk and improving the efficiency of medical materials delivery associated with public health emergencies [92]. Munawar et al. [91] proposed an AI-powered model that adopts drones to deliver test kits to patients, as well as collect the test samples back. The potential for non-contact medical supply distribution is crucial in highly infectious health events such as pandemics to minimise person-to-person contact.

3.1.3. Infrastructure

Roads and Transport
Drones can assist in transport analysis in many ways. Figliozzi [40] compared the three types of autonomous vehicles for air and ground delivery, including drones, sidewalk autonomous delivery robots, and road autonomous delivery robots. The study investigated and analysed the number of deliveries, service time, area of service, and service distance, resulting that drones can act as an efficient last-mile delivery that reduce carbon emissions. Furthermore, the truck-drone delivery modes that combine one truck and multiple drones are proven to be effective to serve real-time requests [100].
Drone technology has been used to observe transport methods. For example, Kim [94] presented a method that can convert the footage collected by drones to analyse pedestrian and bicycle volume data. In the monitoring of pedestrian activities, drones provide an efficient way of counting and mapping pedestrians compared with on-the-ground observation tools [97]. To recognise vehicles accurately and effectively, Peng et al. [98] proposed a method for locating and identifying vehicle models from videos captured by drones. Furthermore, Zong et al. [99] presented a methodology integrating drones in real-time analysis of vehicle movement to prevent collision to ensure this is not mistaken for surveillance. The combination of drone images that captured at different times with deep learning models can identify the changes in road surface [95]. This helps local authorities to understand the road quality associated with ageing and deterioration of road surfaces.

3.1.4. Planning

Building and Construction
In terms of building and construction, drones are revolutionising the way rural landscapes are visualised and analysed. Onitsuka et al. [103] illustrate how aerial drone photography can generate detailed 3D models of villages, enabling decision-makers and stakeholders to gain a comprehensive understanding of local conditions from multiple vantage points. Also, drones are gaining recognition for their precision in identifying small solar home systems, a task where satellite imagery falls short due to resolution limitations [106]. Research evaluates drone imagery’s effectiveness, considering both accuracy and cost, thereby advancing sustainable development goals like universal electrification [106]. Kim, S. et al. [104] demonstrated that drone-based photogrammetry using point cloud data is highly effective for monitoring construction progress. Similarly, Mehranfar et al. [105] found drone-based photogrammetry invaluable for reconstructing 3D models of bridges. Drone regulations in the construction industry, as studied by Wang et al. [108], highlight the need for a comprehensive regulatory framework. Additionally, drones combined with AI technology, offer a sustainable approach to detecting thermal losses in buildings, enhancing safety and efficiency in various climatic conditions [107].
Heritage
In heritage conservation, drones are emerging as key tools for documentation and risk assessment. Gutiérrez-Pérez [110] highlights their use in creating digital archives of heritage ruins through combined laser scanning and aerial surveying. As Leon et al. [109] note, drones offer accessible and affordable means to digitally capture heritage sites, safeguarding their exterior features and layouts against potential risks and aiding in their preservation.

3.1.5. Social and Community

Arts and Culture
The fusion of drone technology with AI is revolutionising aerial journalism, enhancing its scope in fields like investigative reporting and event coverage. This integration is crucial for capturing dynamic footage of news events and facilitating live streams for events of various scales [111]. Although drones are being used in tourism and city-making activities (e.g., real estate for sale, marketing campaigns, and future development sites), there is very little academic literature reporting these activities and this study invites further research in this area.
Children and Youth
The after-school drone-flying programme significantly improved students’ learning of spatial visualisation and sequencing skills [112]. Drones also play a vital role in identifying potential traffic conflicts in the school areas to improve road safety for school kids [90]. The video captured by drones can assist in the process of gathering information about driving behaviours and the exact positions of road users.

3.1.6. Waste and Recycling

Waste Management
The sensors installed on a small rotary-wing drone can play the role of “stifling” odours emitted by wastewater treatment plants [114]. Under this setting, it can minimise the measurement bias caused by human analysis. Through the adoption of drone mapping, the researchers were able to visually analyse the spectrum for signs in the early stage malfunctions of the local septic system [116]. Martin et al. [113] conducted aerial surveys for beach litter assessments using drones to obtain high-resolution images. Furthermore, multi-rotor drones can be utilised to identify locations of illegal waste incineration, employing simple algorithms for pollution source detection with a focus on system autonomy and effectiveness in varying environmental conditions [115].
The literature review reveals the diverse and transformative uses of drone technology across multiple domains. Building on these findings, this paper transitions to examining the specific uses of drones within the 93 regional councils of NSW. This analysis explores how these councils integrate drone technology to address local environmental, social, and infrastructural priorities, highlighting its role in enhancing governance and operational efficiency at the regional level.

3.2. Part 2: Drone Use in Regional Local Government Areas

NSW is home to 93 rural and regional councils, each with unique challenges and opportunities shaped by their local contexts. These city councils play a pivotal role in regional governance, and their adoption of drone technology offers valuable insights into how emerging tools are being utilised to address environmental, social, and infrastructural needs across diverse communities. Council information was reviewed between November 2023 and December 2024. Figure 3 depicts the NSW regional councils with evidence of drone use and discussion of drone uses in their publicly available material.
In reviewing the available material, we observed that it fell into five different categories for discussion. These included the following: Drone safety rules and policies are set by local councils but adhering to a broader CASA (Civil Aviation Safety Authority) framework. This is essential for the safe and lawful operation of drones, safeguarding both operators and the public. Council uses demonstrate the versatility of drones in tasks such as monitoring infrastructure, managing land use, and supporting environmental conservation. As educational resources, drones play a key role in promoting skill development and raising awareness about their wide-ranging capabilities. In the area of research and partnerships, collaborations between councils, academic institutions, and industry are driving innovation and expanding the scope of drone uses for public benefit. Finally, future considerations address challenges and explore opportunities for advancing drone technology and integrating it into broader community and organisational strategies.
Table 3 and Figure 3 demonstrate the presence of the five category areas: (1) drone safety rules and policies, (2) council uses, (3) educational resources, (4) research and partnerships, and (5) future considerations.
In this figure, the numbers ranging from 0 to 5 represent the varying levels of drone uses across LGAs. A value of 0 indicates no drone use in an LGA, while 5 represents drone deployment in five different application categories. A colour gradient from grey (lowest use) to dark green (highest use) visually represents this variation. Notably, no LGA has implemented drones across all five potential application categories.
Based on the analysis of the drone usage across 93 regional councils in NSW, a diverse picture emerges regarding the integration and use of drone technology in local governance and services. Firstly, drone safety rules and policies for drone operators are prominent. Some councils have established specific guidelines and policies governing drone usage, particularly in sensitive areas near airports.
The use of drones across the councils varies, encompassing activities like environmental monitoring, community updates, and more specialised uses. Albury City Council has set out distinct drone usage rules around Albury Airport reflecting a growing consciousness and requirement for regulation in drone operations to ensure safety and compliance with national aviation standards. Councils like Armidale Regional Council and Ballina Shire Council actively utilise drones for environmental purposes. This trend indicates a move towards leveraging drone technology for more efficient and effective local governance, especially in tasks that necessitate aerial views or are otherwise challenging to access. Furthermore, councils are engaging beyond operational use of drones by taking that knowledge to community members. For example, Balranald Shire Council integrates drones into their educational programmes to incorporate new technology into community learning and skill development.
The geographical distribution of drone usage among the regional councils in NSW, as depicted in Figure 3, indicates a distinct cluster pattern. This pattern suggests that drone uses are more concentrated around certain major regional centres, which act as hubs of technological adoption. One reason for the clustering is that councils are engaging in a knowledge-sharing initiative about technology adoption, specifically in drone adoption. Councils in the vicinity of regional centres like Bathurst and Armidale exhibit a higher use of drones. It could be surmised that if these hubs were to adopt drone technology, they could possibly influence their neighbouring councils, possibly due to shared initiatives, closer collaboration, and the spill over of technological expertise and resources. The clustering also suggests that these centres may have more developed infrastructure or policies supporting drone usage, which could serve as models for other regions. It is also plausible that these regional centres have higher economic activity or more pressing logistical needs that drones help to address, which could explain the higher concentration of drone uses in these areas.
The use of drones across the 93 regional councils in NSW is multifaceted and in a state of flux. While some councils are leading the way in using drone technology for a variety of uses, including policy implementation and council engagement, others are still in the nascent stages of adoption. The following discussion presents a dynamic and evolving landscape for drone use in local governance, underscoring the potential for broader adoption and innovation in the future.

4. Discussion of Academic Literature and Council Uses

Table 4 is designed to cross-check findings from the academic literature review with uses of drone technology identified on the websites of 93 regional councils. Examples include biodiversity, emergency management, public health, heritage, and children and youth. This comparison highlights gaps between academic recommendations and real-world council practices.
Using the seven categories indicated by either the academic literature review or the council documents review, this section will discuss what is in evidence for both the current drone research priorities and the current uses of drone technology in regional councils.

4.1. Economic

Drone footage has emerged as a dynamic tool in marketing campaigns by local councils, showcasing the unique landscapes, infrastructure, and cultural assets of their regions. By providing aerial perspectives, drones enhance the visual appeal of promotional materials, attracting tourists, investors, and businesses.

4.2. Environment

Diverse environmental tasks are increasingly being tackled by councils through drones. For biosecurity, drones are being used for weed detection and spraying. In addressing climate change, drones help in coastal erosion detection and water quality monitoring, combining thermal imagery within situ observations for comprehensive climate impact assessments. They are pivotal in environmental monitoring in sectors like agriculture, where they aid in rangeland inventory, soil moisture assessment, and crop yield prediction, enhancing sustainability in farming practices. For forest classification and management, drones monitor and manage urban greenery, assess desert vegetation, and enhance forest management with remote sensing and disease detection capabilities. In disaster management, drones facilitate landslide susceptibility analysis, post-fire assessments, and support rescue operations with geospatial surveillance, playing a crucial role in bespoke forest management and disaster mitigation planning. There is no council use of drones in biodiversity. From the literature review findings, drones with multispectral sensors provide reliable ecosystem monitoring, aiding in wildlife studies and population censuses with greater accuracy and less invasiveness. This provides future possibilities in council uses.

4.3. Health and Safety

In health and safety uses, councils are already leveraging drone technology, notably in shark attack mitigation programmes and beach safety monitoring. Expanding upon these uses, drones have potential to be utilised in emergency management and public health. For example, research indicates that drones equipped with multi-gas detectors and high-definition video cameras can significantly bolster rescue operations in hazardous environments, such as fires or toxic areas, by functioning effectively even in the absence of visual contact or GPS signals. Furthermore, drones can play a pivotal role in public health emergencies by enhancing the distribution efficiency of medical materials and reducing associated risks. Their ability to navigate through hazardous or inaccessible terrain makes drones an invaluable tool for improving disaster response efficiency, minimising delays, and addressing critical health and safety needs in future emergencies. By incorporating drone technology into emergency management frameworks, councils could significantly enhance their preparedness and response capabilities, particularly in vulnerable regional areas.

4.4. Infrastructure

For key infrastructure tasks, councils are increasingly relying on drones, including for inspections of road conditions, dams, and cemeteries. Beyond these uses, drones offer significant potential in improving last-mile logistics. Their use in reducing carbon emissions and alleviating road congestion, especially in last-mile delivery scenarios, is particularly notable. The integration of truck-drone delivery modes offers an effective solution for real-time requests, combining the advantages of road and aerial transport. Furthermore, drones are invaluable in transport analysis, such as for analysing pedestrian and bicycle volume data and for vehicle identification from aerial footage. A significant application is the use of drone imagery over time, combined with deep learning models, which enables local authorities to monitor the deterioration of road surfaces. This capability is crucial for timely maintenance and improving road safety.

4.5. Planning

In planning and development, councils are finding innovative uses for drones, such as conducting surveys for local plans, monitoring development sites, and inspecting private properties. Apart from that, drones can capture detailed 3D models of rural landscapes, aiding decision-makers in visualising and comprehending local conditions from multiple perspectives. In construction, drone-based photogrammetry provides valuable geometric data for tracking building progress and reconstructing structures like bridges. Drones also enhance the identification of small solar home systems, supporting sustainable development goals. Further, when integrated with AI, they offer innovative solutions for assessing thermal losses in buildings, revolutionising the inspection process by factoring in diverse climatic conditions. There are future possibilities of drone uses in heritage conservation, where drones facilitate the creation of digital archives and the inspection of heritage sites, offering accessible and cost-effective means to safeguard and document these elements.

4.6. Social and Community

In capturing community events, councils are already utilising drones to offer a new perspective and enhance the documentation of local activities. Beyond these, drones, integrated with AI, have the potential to revolutionise aerial journalism, aiding in investigative reporting and providing live coverage of news and large-scale events. Moreover, in educational contexts, drones can significantly augment learning, particularly in developing children’s spatial visualisation and sequencing skills through after-school drone-flying programmes. Furthermore, drones are instrumental in enhancing road safety around schools, by monitoring traffic conflicts and capturing vital information about driving behaviours and the positions of road users.

4.7. Waste and Recycling

In the field of waste management and recycling, some councils are turning to drones to detect illegal waste, marking a significant advancement in this area. Beyond the usage, drones equipped with sensors can identify odours from wastewater treatment plants, reducing the need for human analysis and improving the early detection of issues in septic systems. Drone mapping is also employed for high-resolution aerial surveys, which are particularly useful in tasks like beach litter assessments. Moreover, drones can pinpoint locations of illegal waste incineration, using algorithms designed for pollution source detection.

5. Conclusions

While academic research has extensively explored the potential of drones across environmental monitoring, biosecurity, climate change mitigation, and disaster management, its primary focus remains on leveraging drones for ecological conservation, precision agriculture, urban planning, and emergency response. Researchers highlight the ability of drones to enhance data accuracy, reduce operational costs, and improve efficiency in traditionally labour-intensive tasks. However, challenges persist, including regulatory constraints, ethical considerations, technological limitations, and financial barriers. Despite these hurdles, drones have proven particularly effective in biodiversity assessments, disaster response, and infrastructure monitoring, demonstrating their increasing relevance in supporting sustainable and resilient communities.
Findings from regional NSW councils indicate that drones are already integrated into various governance functions, spanning environmental management, infrastructure maintenance, public safety, and social initiatives. While many of these uses align with academic research, councils also employ drones for practical, region-specific purposes, such as weed detection, coastal erosion monitoring, and land use planning. Additionally, their role in tourism, heritage documentation, and potential for remote community capacity building, highlight the expanding scope of drone technology beyond purely technical uses. However, the degree of adoption varies based on resource availability, regulatory support, and technological expertise, presenting opportunities for further development and targeted investment.
Looking ahead, future research could play a crucial role in overcoming existing barriers by developing cost-effective, scalable drone solutions tailored to regional council needs. Expanding drone uses into underutilised sectors, such as healthcare logistics, waste management, and climate resilience, could further enhance their impact. Strengthening collaboration between researchers and councils would enable knowledge-sharing, the establishment of best-practice guidelines, and improved policy frameworks to facilitate adoption. Such partnerships will be essential in ensuring that drone technology continues to evolve as a smart, adaptable, and accessible tool for regional governance.
This review of current research and the use of drone technology in regional areas indicates that drones embodying smart technology are poised to redefine operational efficiency and service delivery in rural and regional councils. This paper highlights the use of drones in rural and regional towns, but also showcases unique, new and targeted uses, and the untapped potential that remains. Our findings provide a starting point for councils to explore new uses that might be suitable to their areas. The map (Figure 3) also gives readers a visual representation of which councils are utilising drone technology, as well as the present level of engagement with drone technology as a future smart management tool for regional councils.
The review of the academic literature and council websites has revealed a significant spread of drone uses and uses already in play, suggesting that the scope of their usage, if adopted at scale, could be transformative in rural settings. From enhancing the efficiency of last-mile deliveries to providing novel solutions for planning and public health, drones stand as a testament to innovation that can cross geographical divides. Findings from this review highlight strategies and examples developed in NSW and from global examples that could be relevant to rural areas worldwide.
A limitation in the systematic literature review pertains to the date of publications searched. Our search parameter dates were 2018–2023; the article was completed and submitted in 2025. We acknowledge the possibility of publications missed as a result of procedural review processes and timing. Additionally, it is arguable that the literature search could have been more robust had we included articles from industry-based publications or even mainstream tech magazines and not been limited to peer-reviewed journal articles. Another limitation is that the research did not incorporate any qualitative aspects. Given the extensive dataset extracted from 93 councils noting their uptake (or not) or drone use, we could have interviewed councils that currently incorporate drones. Finally, defining rural and regional areas proved to be difficult. What is considered rural in one country can have a different meaning in another; for example, rural towns in China are not comparable to rural Australia. With this, we endeavoured to adopt the Australian definition of rural and regional.
To conclude, the future of drones in rural and regional councils is not limited to technological capabilities but also hinges on policy development, educational initiatives, and collaborative research. The insights gleaned from this review could serve as a cornerstone for policymakers and local governments to harness drone technology’s full potential, fostering a future where smart technology transcends the urban–rural divide and delivers equitable benefits to all regions.

Funding

The Smart Regional Spaces project was funded through the New South Wales Digital Restart Fund, Department of Regional New South Wales, Smart Places Acceleration Program Grant no. RG212528.

Acknowledgments

During the preparation of this manuscript, the authors used Microsoft Word (Version 16.98), Microsoft Excel (Version 16.97), Zotero (Version 6.0.37), Adobe Photoshop 2024 (Version 25.12.3), and Grammarly Desktop (Version 1.128.1) for the purposes of manuscript drafting, table preparation, referencing, image generation and grammar checking. The authors have reviewed and edited the output and take full responsibility for the content of this publication.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Patel, T.; Suthar, V.; Bhatt, N. Application of Remotely Piloted Unmanned Aerial Vehicle in Construction Management. In Recent Trends in Civil Engineering; Lecture Notes in Civil Engineering; Pathak, K.K., Bandara, J.M.S.J., Agrawal, R., Eds.; Springer: Singapore, 2021; pp. 319–329. [Google Scholar]
  2. Alvarado-Robles, G.; Garduño-Ramón, M.A.; Osornio-Ríos, R.A.; Morales-Hernandez, L.A. Methodology for Urban Vegetation Segmentation Based on Cartesian Hue and Saturation Characteristics Using UAV Images. Urban For. Urban Green. 2022, 78, 127785. [Google Scholar] [CrossRef]
  3. Escobar-Silva, E.V.; Bourscheidt, V.; Daughtry, C.S.T.; Kiniry, J.R.; Backes, A.R.; Chaves, M.E.D. A General Grass Growth Model for Urban Green Spaces Management in Tropical Regions: A Case Study with Bahiagrass in Southeastern Brazil. Urban For. Urban Green. 2022, 73, 127583. [Google Scholar] [CrossRef]
  4. Steinmetz-Weiss, C.; Marshall, N.; Bishop, K.; Dutia, E.; Wei, Y.; Maalsen, S.; Dowling, R.; Baker, A. Smart Regional Spaces: Ready Set Go! Aust. Plan. 2022, 58, 110–114. [Google Scholar] [CrossRef]
  5. Ezat, M.A.; Fritsch, C.J.; Downs, C.T. Use of an Unmanned Aerial Vehicle (Drone) to Survey Nile Crocodile Populations: A Case Study at Lake Nyamithi, Ndumo Game Reserve, South Africa. Biol. Conserv. 2018, 223, 76–81. [Google Scholar] [CrossRef]
  6. Kalamkar, R.B.; Ahire, M.C.; Ghadge, P.A.; Dhenge, S.A.; Anarase, M.S. Drone and Its Applications in Agriculture. Int. J. Curr. Microbiol. Appl. Sci. 2020, 9, 3022–3026. [Google Scholar] [CrossRef]
  7. Shah, S.A.; Lakho, G.M.; Keerio, H.A.; Sattar, M.N.; Hussain, G.; Mehdi, M.; Vistro, R.B.; Mahmoud, E.A.; Elansary, H.O. Application of Drone Surveillance for Advance Agriculture Monitoring by Android Application Using Convolution Neural Network. Agronomy 2023, 13, 1764. [Google Scholar] [CrossRef]
  8. Sahni, R.K.; Kumar, S.P.; Thorat, D.; Rajwade, Y.; Jyoti, B.; Ranjan, J.; Anand, R. Drone Spraying System for Efficient Agrochemical Application in Precision Agriculture. In Applications of Computer Vision and Drone Technology in Agriculture 4.0; Chouhan, S.S., Singh, U.P., Jain, S., Eds.; Springer Nature: Singapore, 2024; pp. 225–244. ISBN 978-981-9986-84-2. [Google Scholar]
  9. Hosseini, S.; Frank, L.; Fridgen, G.; Heger, S. Do Not Forget About Smart Towns. Bus. Inf. Syst. Eng. 2018, 60, 243–257. [Google Scholar] [CrossRef]
  10. Cowie, P.; Townsend, L.; Salemink, K. Smart Rural Futures: Will Rural Areas Be Left behind in the 4th Industrial Revolution? J. Rural. Stud. 2020, 79, 169–176. [Google Scholar] [CrossRef]
  11. Buckle, C.; Drozdzewski, D. Urban Perceptions of Tree-Change Migration. Rural. Soc. 2018, 27, 192–207. [Google Scholar] [CrossRef]
  12. Bourke, L.; Lockie, S. Rural Australia: An Introduction. In Rurality Bites: The Social and Environmental Transformation of Rural Australia; Pluto Press: Sydney, Australia, 2001; pp. 1–13. [Google Scholar]
  13. Beer, A.; Maude, A.; Pritchard, B. Developing Australia’s Regions: Theory and Practice; University of New South Wales: Sydney, Australia, 2003; ISBN 0-86840-548-5. [Google Scholar]
  14. Australian Bureau of Statistics Remoteness Areas. Available online: https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs-edition-3/jul2021-jun2026/remoteness-structure/remoteness-areas (accessed on 4 July 2025).
  15. Kullmann, K. The Drone’s Eye: Applications and Implications for Landscape Architecture. Landsc. Res. 2018, 43, 906–921. [Google Scholar] [CrossRef]
  16. Telli, K.; Kraa, O.; Himeur, Y.; Ouamane, A.; Boumehraz, M.; Atalla, S.; Mansoor, W. A Comprehensive Review of Recent Research Trends on Unmanned Aerial Vehicles (UAVs). Systems 2023, 11, 400. [Google Scholar] [CrossRef]
  17. David, A.; Yigitcanlar, T.; Li, R.Y.; Corchado, J.M.; Cheong, P.H.; Mossberger, K.; Mehmood, R. Understanding Local Government Digital Technology Adoption Strategies: A PRISMA Review. Sustainability 2023, 15, 9645. [Google Scholar] [CrossRef]
  18. EASA Unmanned Aircraft Systems (UAS)|EASA. Available online: https://www.easa.europa.eu/en/regulations/unmanned-aircraft-systems-uas (accessed on 14 April 2025).
  19. CASA CASA’s Regulatory Framework. Available online: https://www.casa.gov.au/rules/regulatory-framework/casas-regulatory-framework (accessed on 14 April 2025).
  20. CASA Get Your Remotely Piloted Aircraft Operator’s Certificate (ReOC). Available online: https://www.casa.gov.au/drones/remotely-piloted-aircraft-operators-certificate/get-your-reoc (accessed on 14 April 2025).
  21. Australian Army Unmanned Aerial Vehicles. Available online: https://www.army.gov.au/equipment/vehicles-and-surveillance/unmanned-aerial-vehicles (accessed on 14 April 2025).
  22. Local Government NSW About LGNSW. Available online: https://lgnsw.org.au/Public/Public/About-LGNSW/About-LGNSW.aspx (accessed on 22 November 2023).
  23. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews. Syst. Rev. 2021, 10, 89. [Google Scholar] [CrossRef] [PubMed]
  24. Khalili, S.M.; Mojtahedi, M.; Steinmetz-Weiss, C.; Sanderson, D. A Systematic Literature Review on Transit-Based Evacuation Planning in Emergency Logistics Management: Optimisation and Modelling Approaches. Buildings 2024, 14, 176. [Google Scholar] [CrossRef]
  25. Foroughi, M.; de Andrade, B.; Roders, A.P.; Wang, T. Public Participation and Consensus-Building in Urban Planning from the Lens of Heritage Planning: A Systematic Literature Review. Cities 2023, 135, 104235. [Google Scholar] [CrossRef]
  26. Li, D.; Xu, H.; Kang, Y.; Steemers, K. Systematic Review: Landscape Characteristics Correlated with Physical Activity of the Elderly People. Land 2023, 12, 605. [Google Scholar] [CrossRef]
  27. Rice, L.; Drane, M. Indicators of Healthy Architecture—A Systematic Literature Review. J. Urban Health 2020, 97, 899–911. [Google Scholar] [CrossRef]
  28. Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. BMJ 2009, 339, b2535. [Google Scholar] [CrossRef]
  29. Kellenberger, B.; Marcos, D.; Tuia, D. Detecting Mammals in UAV Images: Best Practices to Address a Substantially Imbalanced Dataset with Deep Learning. Remote Sens. Environ. 2018, 216, 139–153. [Google Scholar] [CrossRef]
  30. Pirotta, V.; Reynolds, W.; Ross, G.; Jonsen, I.; Grech, A.; Slip, D.; Harcourt, R. A Citizen Science Approach to Long-Term Monitoring of Humpback Whales (Megaptera Novaeangliae) off Sydney, Australia. Mari. Mamm. Sci. 2020, 36, 472–485. [Google Scholar] [CrossRef]
  31. Escobar-Flores, J.; Sandoval, S.; Gámiz-Romero, E. Unmanned Aerial Vehicle Images in the Machine Learning for Agave Detection. Environ. Sci. Pollut. Res. 2022, 29, 61662–61673. [Google Scholar] [CrossRef]
  32. Mirka, B.; Stow, D.; Paulus, G.; Loerch, A.; Coulter, L.; An, L.; Lewison, R.; Pflüger, L. Evaluation of Thermal Infrared Imaging from Uninhabited Aerial Vehicles for Arboreal Wildlife Surveillance. Environ. Monit. Assess. 2022, 194, 512. [Google Scholar] [CrossRef]
  33. Mohamad, M.N.; Reba, M.N.M.; Hossain, M.S. A Screening Approach for the Correction of Distortion in UAV Data for Coral Community Mapping. Geocarto Int. 2022, 37, 7089–7121. [Google Scholar] [CrossRef]
  34. Zaki, N.H.M.; Chong, W.S.; Muslim, A.M.; Reba, M.N.M.; Hossain, M.S. Assessing Optimal UAV-Data Pre-Processing Workflows for Quality Ortho-Image Generation to Support Coral Reef Mapping. Geocarto Int. 2022, 37, 10556–10580. [Google Scholar] [CrossRef]
  35. Tobółka, M.; Aguirre, J.I.; Dylewski, Ł.; López-García, A.; Martínez, R.G.; Zbyryt, A. The Usefulness of Unmanned Aerial Vehicles (UAV) in White Stork Censusing. Eur. J. Wildl. Res. 2023, 69, 67. [Google Scholar] [CrossRef]
  36. de Villiers, C.; Munghemezulu, C.; Mashaba-Munghemezulu, Z.; Chirima, G.; Tesfamichael, S. Weed Detection in Rainfed Maize Crops Using UAV and PlanetScope Imagery. Sustainability 2023, 15, 13416. [Google Scholar] [CrossRef]
  37. Caldwell, S.; Kelleher, C.; Baker, E.; Lautz, L. Relative Information from Thermal Infrared Imagery via Unoccupied Aerial Vehicle Informs Simulations and Spatially-Distributed Assessments of Stream Temperature. Sci. Total Environ. 2019, 661, 364–374. [Google Scholar] [CrossRef]
  38. Dugdale, S.; Malcolm, L.; Hannah, D. Drone-Based Structure-from-Motion Provides Accurate Forest Canopy Data to Assess Shading Effects in River Temperature Models. Sci. Total Environ. 2019, 678, 326–340. [Google Scholar] [CrossRef]
  39. Tucci, G.; Parisi, E.I.; Castelli, G.; Errico, A.; Corongiu, M.; Sona, G.; Viviani, E.; Bresci, E.; Preti, F. Multi-Sensor UAV Application for Thermal Analysis on a Dry-Stone Terraced Vineyard in Rural Tuscany Landscape. ISPRS Int. J. Geo-Inf. 2019, 8, 87. [Google Scholar] [CrossRef]
  40. Figliozzi, M. Carbon Emissions Reductions in Last Mile and Grocery Deliveries Utilizing Air and Ground Autonomous Vehicles. Transp. Res. Part D Transp. Environ. 2020, 85, 102443. [Google Scholar] [CrossRef]
  41. Svane, N.; Flindt, M.; Petersen, R.; Egemose, S. Physical Stream Quality Measured by Drones and Image Analysis versus the Traditional Manual Method. Environ. Technol. 2022, 43, 1237–1247. [Google Scholar] [CrossRef]
  42. Enciso, J.; Jung, J.; Chang, A.; Chavez, J.; Yeom, J.; Landivar, J.; Cavazos, G. Assessing Land Leveling Needs and Performance with Unmanned Aerial System. J. Appl. Remote Sens. 2018, 12, 016001. [Google Scholar] [CrossRef]
  43. Fathipoor, H.; Arefi, H.; Shah-Hosseini, R.; Moghadam, H. Corn Forage Yield Prediction Using Unmanned Aerial Vehicle Images at Mid-Season Growth Stage. J. Appl. Remote Sens. 2019, 13, 034503. [Google Scholar] [CrossRef]
  44. Wu, K.; Rodriguez, G.; Zajc, M.; Jacquemin, E.; Clément, M.; De Coster, A.; Lambot, S. A New Drone-Borne GPR for Soil Moisture Mapping. Remote Sens. Environ. 2019, 235, 111456. [Google Scholar] [CrossRef]
  45. Al-Ali, Z.M.; Abdullah, M.M.; Asadalla, N.B.; Gholoum, M. A Comparative Study of Remote Sensing Classification Methods for Monitoring and Assessing Desert Vegetation Using a UAV-Based Multispectral Sensor. Environ. Monit. Assess. 2020, 192, 389. [Google Scholar] [CrossRef]
  46. Carabassa, V.; Montero, P.; Crespo, M.; Padró, J.; Pons, X.; Balagué, J.; Brotons, L.; Alcañiz, J. Unmanned Aerial System Protocol for Quarry Restoration and Mineral Extraction Monitoring. J. Environ. Manag. 2020, 270, 110717. [Google Scholar] [CrossRef]
  47. Gillan, J.; Karl, J.; van Leeuwen, W. Integrating Drone Imagery with Existing Rangeland Monitoring Programs. Environ. Monit. Assess. 2020, 192, 269. [Google Scholar] [CrossRef]
  48. Lin, S.-W.; Yen, C.-F.; Chang, C.-H.; Wang, L.-J.; Shih, H.-J. Comprehensive Natural Environment and Landscape Signs in Coastal Settlement Hazard Assessment: Case of East Taiwan between the Coastal Mountain and the Pacific Ocean. J. Mar. Sci. Eng. 2020, 8, 478. [Google Scholar] [CrossRef]
  49. Tamayo, L.; Thron, C.; Fendji, J.; Thomas, S.; Förster, A. Cost-Minimizing System Design for Surveillance of Large, Inaccessible Agricultural Areas Using Drones of Limited Range. Sustainability 2020, 12, 8878. [Google Scholar] [CrossRef]
  50. Almalki, F.; Soufiene, B.; Alsamhi, S.; Sakli, H. A Low-Cost Platform for Environmental Smart Farming Monitoring System Based on IoT and UAVs. Sustainability 2021, 13, 5908. [Google Scholar] [CrossRef]
  51. Dixon, D.; Callow, J.; Duncan, J.; Setterfield, S.; Pauli, N. Satellite Prediction of Forest Flowering Phenology. Remote Sens. Environ. 2021, 255, 112197. [Google Scholar] [CrossRef]
  52. Klauser, F.; Pauschinger, D. Entrepreneurs of the Air: Sprayer Drones as Mediators of Volumetric Agriculture. J. Rural. Stud. 2021, 84, 55–62. [Google Scholar] [CrossRef]
  53. Lin, Y.; Habib, A. Quality Control and Crop Characterization Framework for Multi-Temporal UAV LiDAR Data over Mechanized Agricultural Fields. Remote Sens. Environ. 2021, 256, 112299. [Google Scholar] [CrossRef]
  54. Petrova, T.; Petkova-Georgieva, S.; Petrov, Z. Improving the Environmental Monitoring Process Through the Application of Unmanned Aerial Vehicles. J. Environ. Prot. Ecol. 2021, 22, 1144–1150. [Google Scholar]
  55. Windle, A.; Silsbe, G. Evaluation of Unoccupied Aircraft System (UAS) Remote Sensing Reflectance Retrievals for Water Quality Monitoring in Coastal Waters. Front. Environ. Sci. 2021, 9, 674247. [Google Scholar] [CrossRef]
  56. Kim, D.; Kim, S.; Back, K. Analysis of Mine Change Using 3D Spatial Information Based on Drone Image. Sustainability 2022, 14, 3433. [Google Scholar] [CrossRef]
  57. Moisa, M.B.; Dejene, I.N.; Hirko, O.; Gemeda, D.O. Impact of Deforestation on Soil Erosion in the Highland Areas of Western Ethiopia Using Geospatial Techniques: A Case Study of the Upper Anger Watershed. Asia-Pac. J. Reg. Sci. 2022, 6, 489–514. [Google Scholar] [CrossRef]
  58. Umeda, S.; Yoshikawa, N.; Seo, Y. Cost and Workload Assessment of Agricultural Drone Sprayer: A Case Study of Rice Production in Japan. Sustainability 2022, 14, 10850. [Google Scholar] [CrossRef]
  59. de Mello, D.C.; Barros Souza, A.; Mello, F.A.O.; Marques, K.P.P.; Poppiel, R.R.; Belinasso, H.; Di Raimo, L.D.L.; Francelino, M.R.; Fernandes-Filho, E.I.; Veloso, G.V.; et al. Sensor-Based Field Methods for Pedology and Soil Surveys: Protocol Suggestions for Brazilian Tropical Soils. Geoderma Reg. 2023, 33, e00651. [Google Scholar] [CrossRef]
  60. Ma, B.; Cao, G.; Hu, C.; Chen, C. Monitoring the Rice Panicle Blast Control Period Based on UAV Multispectral Remote Sensing and Machine Learning. Land 2023, 12, 469. [Google Scholar] [CrossRef]
  61. Nur, N.B.; Bachmann, C.M. Comparison of Soil Moisture Content Retrieval Models Utilizing Hyperspectral Goniometer Data and Hyperspectral Imagery From an Unmanned Aerial System. J. Geophys. Res. Biogeosci. 2023, 128, e2023JG007381. [Google Scholar] [CrossRef]
  62. Oliveira, R.A.; Näsi, R.; Korhonen, P.; Mustonen, A.; Niemeläinen, O.; Koivumäki, N.; Hakala, T.; Suomalainen, J.; Kaivosoja, J.; Honkavaara, E. High-Precision Estimation of Grass Quality and Quantity Using UAS-Based VNIR and SWIR Hyperspectral Cameras and Machine Learning. Precis. Agric. 2023, 25, 186–220. [Google Scholar] [CrossRef]
  63. Sestras, P.; Mircea, S.; Roșca, S.; Bilașco, Ș.; Sălăgean, T.; Dragomir, L.O.; Herbei, M.V.; Bruma, S.; Sabou, C.; Marković, R.; et al. GIS Based Soil Erosion Assessment Using the USLE Model for Efficient Land Management: A Case Study in an Area with Diverse Pedogeomorphological and Bioclimatic Characteristics. Not. Bot. Horti Agrobot. Cluj-Napoca 2023, 51, 13263. [Google Scholar] [CrossRef]
  64. Shu, M.; Bai, K.; Meng, L.; Yang, X.; Li, B.; Ma, Y. Assessing Maize Lodging Severity Using Multitemporal UAV-Based Digital Images. Eur. J. Agron. 2023, 144, 126754. [Google Scholar] [CrossRef]
  65. Puliti, S.; Saarela, S.; Gobakken, T.; Ståhl, G.; Næsset, E. Combining UAV and Sentinel-2 Auxiliary Data for Forest Growing Stock Volume Estimation through Hierarchical Model-Based Inference. Remote Sens. Environ. 2018, 204, 485–497. [Google Scholar] [CrossRef]
  66. Webster, C.; Jonas, T. Influence of Canopy Shading and Snow Coverage on Effective Albedo in a Snow-Dominated Evergreen Needleleaf Forest. Remote Sens. Environ. 2018, 214, 48–58. [Google Scholar] [CrossRef]
  67. Riihimäki, H.; Luoto, M.; Heiskanen, J. Estimating Fractional Cover of Tundra Vegetation at Multiple Scales Using Unmanned Aerial Systems and Optical Satellite Data. Remote Sens. Environ. 2019, 224, 119–132. [Google Scholar] [CrossRef]
  68. Räsänen, A.; Virtanen, T. Data and Resolution Requirements in Mapping Vegetation in Spatially Heterogeneous Landscapes. Remote Sens. Environ. 2019, 230, 111207. [Google Scholar] [CrossRef]
  69. Berra, E. Individual Tree Crown Detection and Delineation across a Woodland Using Leaf-on and Leaf-off Imagery from a UAV Consumer-Grade Camera. J. Appl. Remote Sens. 2020, 14, 034501. [Google Scholar] [CrossRef]
  70. Campbell, M.; Dennison, P.; Tune, J.; Kannenberg, S.; Kerr, K.; Codding, B.; Anderegg, W. A Multi-Sensor, Multi-Scale Approach to Mapping Tree Mortality in Woodland Ecosystems. Remote Sens. Environ. 2020, 245, 111853. [Google Scholar] [CrossRef]
  71. Kotivuori, E.; Kukkonen, M.; Mehtätalo, L.; Maltamo, M.; Korhonen, L.; Packalen, P. Forest Inventories for Small Areas Using Drone Imagery without In-Situ Field Measurements. Remote Sens. Environ. 2020, 237, 111404. [Google Scholar] [CrossRef]
  72. Navarro, A.; Young, M.; Allan, B.; Carnell, P.; Macreadie, P.; Ierodiaconou, D. The Application of Unmanned Aerial Vehicles (UAVs) to Estimate above-Ground Biomass of Mangrove Ecosystems. Remote Sens. Environ. 2020, 242, 111747. [Google Scholar] [CrossRef]
  73. Zhu, Y.; Jeon, S.; Sung, H.; Kim, Y.; Park, C.; Cha, S.; Jo, H.; Lee, W. Developing UAV-Based Forest Spatial Information and Evaluation Technology for Efficient Forest Management. Sustainability 2020, 12, 10150. [Google Scholar] [CrossRef]
  74. Haq, M.; Rahaman, G.; Baral, P.; Ghosh, A. Deep Learning Based Supervised Image Classification Using UAV Images for Forest Areas Classification. J. Indian Soc. Remote Sens. 2021, 49, 601–606. [Google Scholar] [CrossRef]
  75. Meivel, S.; Maheswari, S. Quality Management of Healthcare Food Production in Agricultural Forest Fields Using Vegetation Indices with Multispectral Drone Mapping Images. J. Environ. Prot. Ecol. 2022, 23, 266–279. [Google Scholar]
  76. Oide, A.; Nagasaka, Y.; Tanaka, K. Performance of Machine Learning Algorithms for Detecting Pine Wilt Disease Infection Using Visible Color Imagery by UAV Remote Sensing. Remote Sens. Appl. Soc. Environ. 2022, 28, 100869. [Google Scholar] [CrossRef]
  77. Zhang, Y.; Onda, Y.; Kato, H.; Feng, B.; Gomi, T. Understory Biomass Measurement in a Dense Plantation Forest Based on Drone-SfM Data by a Manual Low-Flying Drone under the Canopy. J. Environ. Manag. 2022, 312, 114862. [Google Scholar] [CrossRef]
  78. Gupta, S.; Shukla, D. Application of Drone for Landslide Mapping, Dimension Estimation and Its 3D Reconstruction. J. Indian Soc. Remote Sens. 2018, 46, 903–914. [Google Scholar] [CrossRef]
  79. Zweglinski, T. The Use of Drones in Disaster Aerial Needs Reconnaissance and Damage Assessment—Three-Dimensional Modeling and Orthophoto Map Study. Sustainability 2020, 12, 6080. [Google Scholar] [CrossRef]
  80. Akumu, C.; Amadi, E.; Dennis, S. Application of Drone and WorldView-4 Satellite Data in Mapping and Monitoring Grazing Land Cover and Pasture Quality: Pre- and Post-Flooding. Land 2021, 10, 321. [Google Scholar] [CrossRef]
  81. Koutalakis, P.; Tzoraki, O.; Prazioutis, G.; Gkiatas, G.; Zaimes, G. Can Drones Map Earth Cracks? Landslide Measurements in North Greece Using UAV Photogrammetry for Nature-Based Solutions. Sustainability 2021, 13, 4697. [Google Scholar] [CrossRef]
  82. Munawar, H.; Ullah, F.; Qayyum, S.; Khan, S.; Mojtahedi, M. UAVs in Disaster Management: Application of Integrated Aerial Imagery and Convolutional Neural Network for Flood Detection. Sustainability 2021, 13, 7547. [Google Scholar] [CrossRef]
  83. Prakash, N.; Guru, R.; Mathankumar, M. Computer Vision Based Autonomous Unmanned Aerial Vehicle for Human and Object Detection in Post-Disaster Zones. J. Environ. Prot. Ecol. 2021, 22, 2583–2592. [Google Scholar]
  84. Rai, A.; Kannan, R. Auto Neural Architecture Generator for UAV-Based Geospatial Surveillance for Aerial Crisis Mapping in Dark. J. Indian Soc. Remote Sens. 2021, 49, 507–514. [Google Scholar] [CrossRef]
  85. Rodriguez-Caballero, E.; Rodriguez-Lozano, B.; Segura-Tejada, R.; Blanco-Sacristán, J.; Cantón, Y. Landslides on Dry Badlands: UAV Images to Identify the Drivers Controlling Their Unexpected Occurrence on Vegetated Hillslopes. J. Arid Environ. 2021, 187, 104434. [Google Scholar] [CrossRef]
  86. Ullah, F.; Khan, S.; Munawar, H.; Qadir, Z.; Qayyum, S. UAV Based Spatiotemporal Analysis of the 2019-2020 New South Wales Bushfires. Sustainability 2021, 13, 10207. [Google Scholar] [CrossRef]
  87. Qi, Y.; Coops, N.; Daniels, L.; Butson, C. Assessing the Effects of Burn Severity on Post-Fire Tree Structures Using the Fused Drone and Mobile Laser Scanning Point Clouds. Front. Environ. Sci. 2022, 10, 949442. [Google Scholar] [CrossRef]
  88. Maha Agung, P.A.; Rouf Hasan, M.F.; Susilo, A.; Ahmad, M.A.; Bin Ahmad, M.J.; Abdurrahman, U.A.; Sudjianto, A.T.; Suryo, E.A. Compilation of Parameter Control for Mapping the Potential Landslide Areas. Civ. Eng. J. 2023, 9, 974–989. [Google Scholar] [CrossRef]
  89. Irimia, A.; Gaman, G.; Pupazan, D.; Ilie, C.; Nicolescu, C. Using Drones in Support of Rescue Interventions Teams in Toxic/Flammable/Explosive Environments. Environ. Eng. Manag. J. 2019, 18, 831–837. [Google Scholar] [CrossRef]
  90. Renard, A.; Novacko, L.; Babojelic, K.; Kozul, N. Analysis of Child Traffic Safety near Primary School Areas Using UAV Technology. Sustainability 2022, 14, 1144. [Google Scholar] [CrossRef]
  91. Munawar, H.; Inam, H.; Ullah, F.; Qayyum, S.; Kouzani, A.; Mahmud, M. Towards Smart Healthcare: UAV-Based Optimized Path Planning for Delivering COVID-19 Self-Testing Kits Using Cutting Edge Technologies. Sustainability 2021, 13, 10426. [Google Scholar] [CrossRef]
  92. Du, L.; Li, X.; Gan, Y.; Leng, K. Optimal Model and Algorithm of Medical Materials Delivery Drone Routing Problem under Major Public Health Emergencies. Sustainability 2022, 14, 4651. [Google Scholar] [CrossRef]
  93. Liu, M.; Liu, X.; Zhu, M.; Zheng, F. Stochastic Drone Fleet Deployment and Planning Problem Considering Multiple-Type Delivery Service. Sustainability 2019, 11, 3871. [Google Scholar] [CrossRef]
  94. Kim, D. Pedestrian and Bicycle Volume Data Collection Using Drone Technology. J. Urban Technol. 2020, 27, 45–60. [Google Scholar] [CrossRef]
  95. Nguyen, T.; Han, D. Detection of Road Surface Changes from Multi-Temporal Unmanned Aerial Vehicle Images Using a Convolutional Siamese Network. Sustainability 2020, 12, 2482. [Google Scholar] [CrossRef]
  96. Borghetti, F.; Caballini, C.; Carboni, A.; Grossato, G.; Maja, R.; Barabino, B. The Use of Drones for Last-Mile Delivery: A Numerical Case Study in Milan, Italy. Sustainability 2022, 14, 1766. [Google Scholar] [CrossRef]
  97. Park, K.; Ewing, R. The Usability of Unmanned Aerial Vehicles (UAVs) for Pedestrian Observation. J. Plan. Educ. Res. 2022, 42, 206–217. [Google Scholar] [CrossRef]
  98. Peng, B.; Zhang, H.; Yang, N.; Xie, J. Vehicle Recognition from Unmanned Aerial Vehicle Videos Based on Fusion of Target Pre-Detection and Deep Learning. Sustainability 2022, 14, 7912. [Google Scholar] [CrossRef]
  99. Zong, S.; Chen, S.; Alinizzi, M.; Labi, S. Leveraging UAV Capabilities for Vehicle Tracking and Collision Risk Assessment at Road Intersections. Sustainability 2022, 14, 4034. [Google Scholar] [CrossRef]
  100. Gou, M.; Yu, H. Online Delivery Problem for Hybrid Truck-Drone System with Independent and Truck-Carried Drones. Sustainability 2023, 15, 1584. [Google Scholar] [CrossRef]
  101. Hwang, J.; Joo, K.; Moon, J. A Study on Behavioral Intentions in the Field of Eco-Friendly Drone Food Delivery Services: Focusing on Demographic Characteristics and Past Experiences. Sustainability 2023, 15, 6253. [Google Scholar] [CrossRef]
  102. Min, H. Leveraging Drone Technology for Last-Mile Deliveries in the e-Tailing Ecosystem. Sustainability 2023, 15, 11588. [Google Scholar] [CrossRef]
  103. Onitsuka, K.; Ninomiya, K.; Hoshino, S. Potential of 3D Visualization for Collaborative Rural Landscape Planning with Remote Participants. Sustainability 2018, 10, 3059. [Google Scholar] [CrossRef]
  104. Kim, S.; Kim, S.; Lee, D. Sustainable Application of Hybrid Point Cloud and BIM Method for Tracking Construction Progress. Sustainability 2020, 12, 4106. [Google Scholar] [CrossRef]
  105. Mehranfar, M.; Arefi, H.; Alidoost, F. Knowledge-Based 3D Reconstruction of Bridge Structures Using UAV-Based Photogrammetric Point Cloud. J. Appl. Remote Sens. 2021, 15, 044503. [Google Scholar] [CrossRef]
  106. Ren, S.; Malof, J.; Fetter, R.; Beach, R.; Rineer, J.; Bradbury, K. Utilizing Geospatial Data for Assessing Energy Security: Mapping Small Solar Home Systems Using Unmanned Aerial Vehicles and Deep Learning. ISPRS Int. J. Geo-Inf. 2022, 11, 222. [Google Scholar] [CrossRef]
  107. Khan, O.; Parvez, M.; Alansari, M.; Farid, M.; Devarajan, Y.; Thanappan, S. Application of Artificial Intelligence in Green Building Concept for Energy Auditing Using Drone Technology under Different Environmental Conditions. Sci. Rep. 2023, 13, 8200. [Google Scholar] [CrossRef]
  108. Wang, X.; Yang, Y.; Chan, A.P.C.; Chi, H.-L.; Yung, E.H.K. A Regulatory Framework for the Use of Small Unmanned Aircrafts (SUAs) in the Construction Industry. Eng. Constr. Archit. Manag. 2023, 31, 3024–3049. [Google Scholar] [CrossRef]
  109. Leon, I.; Pérez, J.J.; Senderos, M. Advanced Techniques for Fast and Accurate Heritage Digitisation in Multiple Case Studies. Sustainability 2020, 12, 6068. [Google Scholar] [CrossRef]
  110. Gutiérrez-Pérez, N. Cultural Heritage Recovery in Rural Spain Through Digital Means of Surveying and Graphic Reconstruction. Case Study of El Paular Wool Complex. SCIRES-IT-Sci. Res. Inf. Technol. 2023, 13, 67–82. [Google Scholar] [CrossRef]
  111. Almalki, F.; Aljohani, M.; Algethami, M.; Soufiene, B. Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model. Sustainability 2022, 14, 3758. [Google Scholar] [CrossRef]
  112. Chou, P. Smart Technology for Sustainable Curriculum: Using Drone to Support Young Students’ Learning. Sustainability 2018, 10, 3819. [Google Scholar] [CrossRef]
  113. Martin, C.; Zhang, Q.; Zhai, D.; Zhang, X.; Duarte, C. Enabling a Large-Scale Assessment of Litter along Saudi Arabian Red Sea Shores by Combining Drones and Machine Learning. Environ. Pollut. 2021, 277, 116730. [Google Scholar] [CrossRef] [PubMed]
  114. Burgués, J.; Doñate, S.; Esclapez, M.; Saúco, L.; Marco, S. Characterization of Odour Emissions in a Wastewater Treatment Plant Using a Drone-Based Chemical Sensor System. Sci. Total Environ. 2022, 846, 157290. [Google Scholar] [CrossRef] [PubMed]
  115. Suchanek, G.; Woloszyn, J.; Golas, A. Evaluation of Selected Algorithms for Air Pollution Source Localisation Using Drones. Sustainability 2022, 14, 3049. [Google Scholar] [CrossRef]
  116. Reckling, W.; Levine, J.; Nelson, S.; Mitasova, H. Predicting Residential Septic System Malfunctions for Targeted Drone Inspections. Remote Sens. Appl. Soc. Environ. 2023, 30, 100936. [Google Scholar] [CrossRef]
  117. Muhamat, A.; Zulkifli, A.; Ibrahim, M.; Sulaiman, S.; Subramaniam, G.; Mohamad, S.; Suzuki, Y. Realising the Corporate Social Performance (CSP) of Takaful (Islamic Insurance) Operators through Drone-Assisted Disaster Victim Identification (DVI). Sustainability 2022, 14, 5440. [Google Scholar] [CrossRef]
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Figure 1. Research design.
Figure 1. Research design.
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Figure 2. Systematic literature review process.
Figure 2. Systematic literature review process.
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Figure 3. Drone uses in 93 NSW local councils.
Figure 3. Drone uses in 93 NSW local councils.
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Table 1. Search criteria for relevant studies.
Table 1. Search criteria for relevant studies.
CriteriaInclusion CriteriaExclusion Criteria
Document typesArticleOther document types (conference papers or book chapters *)
Year rangeRecent 5 years (2018–2023)Prior to 2018
LanguagesEnglishA language other than English
GeographyRural or regional areasUrban areas
Scopus—subject areasEnvironment science; social sciences; multidisciplinary; business, management and accounting; arts and humanities; agricultural and biological sciencesOther subject areas (mathematics; medicine; neuroscience; etc.)
Web of Science—categoriesEnvironmental sciences; environmental studies; regional urban planning; urban studiesOther categories (toxicology; limnology; polymer science; etc.)
* Conference papers and book chapters were excluded due to potential variability in peer-review standards, incomplete or preliminary findings, retrieval challenges, and risk of duplication, unless justified by field-specific relevance.
Table 2. A summary of literature review findings: academic literature.
Table 2. A summary of literature review findings: academic literature.
Major ResponsibilitiesDetailed AspectsSources
EnvironmentBiodiversity[29,30,31,32,33,34,35]
Biosecurity[36]
Climate Change[37,38,39,40,41]
Sustainable Solutions[33,39,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64]
Forest Classification and Management[38,45,51,65,66,67,68,69,70,71,72,73,74,75,76,77]
Disaster Management[78,79,80,81,82,83,84,85,86,87,88]
Health and SafetyEmergency Management[89]
Crime Prevention and Safety[90]
Public health[91,92]
InfrastructureRoads and Transport[40,93,94,95,96,97,98,99,100,101,102]
PlanningBuilding and Construction[103,104,105,106,107,108]
Heritage[109,110]
Social and CommunityArts and Culture[111]
Children and Youth[90,112]
Waste and RecyclingWaste Management[113,114,115,116]
Table 3. A summary of literature review findings: publicly available council documents.
Table 3. A summary of literature review findings: publicly available council documents.
Major Responsibilities Detailed AspectsNSW Regional Council Uses
EconomicTourism
  • Drone footage as marketing campaigns
EnvironmentBiosecurity
  • Weed detection
  • Weed spray
Climate Change
  • Coastal erosion monitoring
Sustainable Solutions
  • Environmental monitoring
  • Agricultural purposes
  • Monitor air quality
Forest Classification and Management
  • Monitoring green infrastructure
Disaster Management
  • Demonstrating flood impacts
  • Landslide analysis
  • Post-fire assessment
Health and SafetyCrime Prevention and Safety
  • Shark attack mitigation programme
  • Beach safety
InfrastructureRoads and Transport
  • Inspecting road
PlanningBuilding and Construction
  • Drone survey for local planning
  • Monitoring of large development sites
  • Inspecting private property
Community Engagement
  • Collecting data for the community uses
Social and CommunityIndigenous Affairs
  • Capacity building
Arts and Culture
  • Capturing footage in events
Waste Management
  • Waste detection
Table 4. A summary of findings from two parts.
Table 4. A summary of findings from two parts.
Major ResponsibilitiesDetailed AspectsPart 1: Literature Review ResultsPart 2: NSW Regional Council Uses
EconomicTourism ✓
EnvironmentBiodiversity✓
Biosecurity✓✓
Climate Change✓✓
Sustainable Solutions✓✓
Forest Classification and Management✓✓
Disaster Management✓✓
Health and SafetyEmergency Management✓
Crime Prevention and Safety✓✓
Public health✓
InfrastructureRoads and Transport✓✓
PlanningBuilding and Construction✓✓
Community Engagement ✓
Heritage✓
Social and CommunityIndigenous Affairs ✓
Arts and Culture✓✓
Children and Youth✓
Waste and RecyclingWaste Management✓✓
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MDPI and ACS Style

Steinmetz-Weiss, C.; Marshall, N.; Bishop, K.; Wei, Y. Mapping Drone Applications in Rural and Regional Cities: A Scoping Review of the Australian State of Practice. Appl. Sci. 2025, 15, 8519. https://doi.org/10.3390/app15158519

AMA Style

Steinmetz-Weiss C, Marshall N, Bishop K, Wei Y. Mapping Drone Applications in Rural and Regional Cities: A Scoping Review of the Australian State of Practice. Applied Sciences. 2025; 15(15):8519. https://doi.org/10.3390/app15158519

Chicago/Turabian Style

Steinmetz-Weiss, Christine, Nancy Marshall, Kate Bishop, and Yuan Wei. 2025. "Mapping Drone Applications in Rural and Regional Cities: A Scoping Review of the Australian State of Practice" Applied Sciences 15, no. 15: 8519. https://doi.org/10.3390/app15158519

APA Style

Steinmetz-Weiss, C., Marshall, N., Bishop, K., & Wei, Y. (2025). Mapping Drone Applications in Rural and Regional Cities: A Scoping Review of the Australian State of Practice. Applied Sciences, 15(15), 8519. https://doi.org/10.3390/app15158519

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