Table 1.

Examples of the use of uncrewed aerial vehicles in vector-borne diseases showing ecosystems, purpose of the study, mosquito species targeted and technical characteristics of the drone surveys

Location	Landscape/ecosystem	Area	Purpose	Feature of interest	Target vector	Equipment	Pixel resolution	Ground-based activities	Image processing	Analysis	Other	Outcomes	References
Westchester and Suffolk Counties, NY, USA	Suburban neighbourhoods	Westchester, 0.05 km2; Suffolk, 0.14 km2	Determine capabilities of UAVs to image preferred oviposition sites of Aedes albopictus; validate results by entomological survey	Oviposition containers	Aedes albopictus	DJI Mavic Pro drone (DJI, Shenzhen, China), with custom-built UAV designed around 3DR Pixhawk 1 flight controller (3DR, Berkeley, CA, USA) using a GoPro Hero 5 camera (GoPro, Inc., San Mateo, CA, USA)	DJI Mavic < 1 cm; Go Pro, 1.5—4 cm/pixel	Yes: entomological and container ground survey	Outer 500 pixels cropped to account for lens distortion with GO PRO photos using the open-source ImageMagick (2020); images sliced into 512 × 512-pixel images to translate to SSD network preinitialized weights and bounding box anchors; annotated aerial imagery (LabelImg) to determine container types UAVs could see; piexif in Python used to convert container locations into GPS	CNN based on the SSD300 algorithm using API Keras on top of Tensorflow. Amazon EC2 p2.xlarge instance handled computations using Amazon Machine Image	Flight at altitude of 50 m; pre-programmed flight paths (Ardu Pilot Development Team software suite 2019) for Pixhawk quadricopter and DJI GO 4 for Mavic Pro	CNN trained on UAV imagery detected up to 67% Ae. albopictus habitat; classified whole properties as positive or negative for larvae 80% of the time	[36]
Cote d'Ivoire	Rural, agricultural landscape	30.42 km2	To develop a technical workflow to integrate drone surveys and mosquito larval sampling. To characterize Anopheles funestus breeding sites in an agricultural setting	Large, semi-permanent water bodies	Anopheles, Anopheles funestus	DJI Phantom 4 Pro quadcopter drone, fitted with a DJI 4 K camera (8.8 mm/24 mm; f/2.8; 1″ CMOS sensor; 20 MP)	~ 4 cm	Yes: GPS coordinates and photos were collected via ODK, and the predominant land cover type was recorded	AgiSoft Metashape Pro (AgiSoft LLC, St. Petersburg, Russian Federation)	Images classified using collaborative online image labelling tool Groundwork (https://groundwork.azavea.com); developing of deep learning approaches (u-Net) integrating drone and satellite data on Amazon Web Services	Flight at an altitude of 150 m Flight plans were programmed using Pix4Dcapture and DJI GS Pro mapping applications	Development of land cover classification scheme with classes relative to An. funestus breeding ecology; developed protocols to integrate drone surveys and statistically rigorous entomological sampling	[113]
Maynas Province, Peru	Riverine/rainforest	~ 1 km2	Use of UAVs to identify breeding sites	Water bodies	Nyssorhynchus darlingi (formerly Anopheles darlingi)	DJI Phantom 4 Pro quadcopter drone, fitted with a DJI 4 K camera (8.8 mm/24 mm; f/2.8; 1'' CMOS; 20 MP) 3DR Solo (3DR) quadcopter fitted with a Parrot Sequoia multispectral sensor (Parrot SA, Paris, France)	DJI Phantom 4 Pro, GSD or spatial resolution of 0.1 m/pixel. 3DR Solo GSD of 0.02 m/pixel	31 water bodies located within 1 km of each of 4 villages surveyed were identified, characterized and sampled along edges for larvae using standard dippers	AgiSoft Photoscan Pro (AgiSoft LLC)	GEE. Integrated Development Environment at https://code.earthengine.google.com used to implement pixel-based random forest classification algorithm	DJI Phantom 4 Pro—altitude of ~ 100 m.3DR Solo drone was flown to an altitude of approximately 50 m	High-resolution multispectral imagery discriminated water bodies likely to be positive for Ny. darlingi with 86.73–96.98% accuracy (k-fold cross validation), with a moderate differentiation of spectral bands	[22]
Sabah, Malaysia/Palawan	Forest/agriculture	3 case study areas of 50–100 km2	To characterize land-use types and create a spatial sampling frame	Land cover	Habitats of Anopheles balabacensis and non-human primate reservoirs of Plasmodium knowlesi	senseFLY eBee drone (senseFLY, Cheseaux-sur-Lausanne, Switzerland), fitted with 16-megapixel digital camera, eMotion2 software (Ageagle Aerial Systems, Wichita, KS, USA)	Average resolution of 11.22 cm	Yes: ground-truth land cover classes; larval and adult mosquito surveys	Postflight Terra3D software (Terra3D, Paris, France)	Land cover classes manually digitized, used to identify training data for pixel-based classification of Landsat satellite data using random forest algorithms	350–400 m altitude	Use of UAVs most appropriate for detailed mapping of relatively small geographical areas where high-resolution satellite data are not readily available	[46]
Kinabatangan, Sabah, Malaysia	Riverine forest	10 × 1-km transects along 20 km of riverbank	To compare visual counting versus use of thermal cameras for primate census	8 diurnal primate species	N/A, reservoir	Custom built hexacopter drone with FLIR Thermal Camera (Teledyne FLIR LLC, Wisonville, OR, USA)		Yes: boat surveys of primates in early morning	FLIR Tools (Tledyne FLIR LLC Co.)	Visual identification and counting		Thermal cameras detected 1.78x (P < 0.001) more primates than were detected by eye; ground-truthing must be conducted during or immediately following to verify species, sexes, age classes and closely aggregated animals	[81]
Kasunga town, Malawi	Rural area surrounding artificial lakes in designated "humanitarian drone testing corridor"	8.9 km2 across 8 sites	Determining the feasibility of drone-led mosquito larval habitat identification in rural environments to inform local malaria control	Water bodies and aquatic vegetation	Anopheles	DJI Phantom 4 Pro quadcopter drone, fitted with NIR sensor(Sentera) vs. eBee SQ fixed-wing drone fitted with Parrot Sequoia multispectral camera (Parrot SA)	RGB camera: 3.3 for Phantom 4 Pro; 3.7 for eBee SQ; NIR sensor, 11 for both	Yes: larval surveys conducted concurrently with drone image capture; GPS co-ordinates recorded using ODK app; photographs taken using smartphone; aerial imagery were captured	Spervised classification of land cover classes (AgiSoft Metashape Pro; AgiSoft LLC)	Land cover and surface water layer classes were manually digitized, used to identify training data for pixel-based classification using random forest algorithms	flying at 120 m	Demonstration of potential for drone imagery as a tool to support identification of mosquito larval habitat in malaria endemic settings Cannot completely replace larval surveys	[71]

CMOS Complementary metal-oxide semiconductor, CNN convolutional neural network, GEE Google Earth Engine, GPS Global Positioning System, GSD ground sampling distance, N/A not available, NIR near infrared, ODK data collection tool, RGB red, green, blue colour model, SSD single-shot detector, UAVs uncrewed aerial vehicles