MM4Drone: A Multi-spectral Image and mmWave Radar Approach for Identifying Mosquito Breeding Grounds via Aerial Drones

Mahima, K. T. Y.; Weerasekara, M.M.; Zoysa, Kasun De; Keppitiyagama, Chamath; Flierl, Markus; Mottola, Luca; Voigt, Thiemo

doi:10.1007/978-3-031-34586-9_27

Cited by 3 publications

(4 citation statements)

References 27 publications

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“…Fig. 16(a) indicates that in LoS scenarios, the mean error of AIM is 1.43m while those of LinkTrack and OptiTrack are 0.37m and 0.03m, respectively 2 . AIM is, therefore, the least accurate system in LoS scenarios, which however, arguably rare in real applications.…”

Section: General Performancementioning

confidence: 99%

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Indoor Drone Localization and Tracking Based on Acoustic Inertial Measurement

Sun,

Wang,

Mottola

et al. 2024

IEEE Trans. on Mobile Comput.

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We present Acoustic Inertial Measurement (AIM), a one-of-a-kind technique for indoor drone localization and tracking. Indoor drone localization and tracking are arguably a crucial, yet unsolved challenge: in GPS-denied environments, existing approaches enjoy limited applicability, especially in Non-Line of Sight (NLoS), require extensive environment instrumentation, or demand considerable hardware/software changes on drones. In contrast, AIM exploits the acoustic characteristics of the drones to estimate their location and derive their motion, even in NLoS settings. We tame location estimation errors using a dedicated Kalman filter and the Interquartile Range rule (IQR) and demonstrate that AIM can support indoor spaces with arbitrary ranges and layouts. We implement AIM using an off-the-shelf microphone array and evaluate its performance with a commercial drone under varied settings. Results indicate that the mean localization error of AIM is 46% lower than that of commercial UWB-based systems in a complex 10m×10m indoor scenario, where state-of-the-art infrared systems would not even work because of NLoS situations. When distributed microphone arrays are deployed, the mean error can be reduced to less than 0.5m in a 20m range, and even support spaces with arbitrary ranges and layouts.

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Section: General Performancementioning

confidence: 99%

“…Location information is crucial for drone operation [1], [2], regardless of the application and target deployment environment [3], [4], [5]. For example, in an indoor warehouse like the one of Fig.…”

Section: Introductionmentioning

confidence: 99%

Indoor Drone Localization and Tracking Based on Acoustic Inertial Measurement

Sun,

Wang,

Mottola

et al. 2024

IEEE Trans. on Mobile Comput.

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“…UAV technology allows rapid and wide-scaled detection of water bodies and other habitat conditions conducive to Arbovirus vector proliferation. Recent research has demonstrated drones for mapping and surveillance of these habitats and are also increasingly used in control measures [5][6][7][8][9]. Several researchers identi ed an Arboviruses' site by analysing natural colour ariel imagery (RGB type) to pinpoint near-standing water bodies or areas prone to water retention [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…Recent research has demonstrated drones for mapping and surveillance of these habitats and are also increasingly used in control measures [5][6][7][8][9]. Several researchers identi ed an Arboviruses' site by analysing natural colour ariel imagery (RGB type) to pinpoint near-standing water bodies or areas prone to water retention [5][6][7]. Implementing this method, while helpful, has certain limitations in the accuracy level of small and medium object information.…”

Section: Introductionmentioning

confidence: 99%

Use of low-cost drones to map arbovirus vector habitats with multispectral aerial imagery

Hoang,

Linh,

Long

et al. 2024

Preprint

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Background This article introduces WaterMAI, a novel multispectral aerial imagery dataset that is optimized for detecting small to medium water bodies and is essential for mapping arbovirus vector habitats. While satellite datasets provide broad coverage and are valuable in many contexts, WaterMAI concentrates on utilizing high-resolution aerial imagery. This approach is suitable for capturing detailed information about water bodies, which may contain vectors for arboviruses. Materials and methods We benchmarked baseline deep learning algorithms on our WaterMAI dataset for water body detection, employing both bounding box and segmentation approaches, establishing new baselines for this domain. Furthermore, we extensively investigate the effectiveness of various spectral band combinations, including Near-infrared (NIR), Red, Green, Blue (RGB), and the Normalized Difference Water Index (NDWI), to determine the potential configuration for accurate water body detection. Results The WaterMAI dataset, covering 16 rural and sub-tropical regions with varied water bodies, increases the utility of research through multiple spectral bands, including visible and near-infrared. The findings demonstrate the potential of multispectral imagery that shall enhance the understanding and monitoring of water bodies in rural and subtropical regions. The WaterMAI dataset, orthomosaic images, and the implementation of the segmentation models for benchmarking are available in GitHub database. Conclusion Our result suggests incorporating NDWI and NIR spectral bands with RGB images potentially improves the water body detection algorithm.

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CNN-Based Estimation of Water Depth from Multispectral Drone Imagery for Mosquito Control

Shen,

Mahima,

De Zoysa

et al. 2023

2023 IEEE International Conference on Image Processing (ICIP)

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We present a machine learning approach that uses a custom Convolutional Neural Network (CNN) for estimating the depth of water pools from multispectral drone imagery. Using drones to obtain this information offers a cheaper, timely, and more accurate solution compared to alternative methods, such as manual inspection. This information, in turn, represents an asset to identify potential breeding sites of mosquito larvae, which grow only in shallow water pools. As a significant part of the world's population is affected by mosquito-borne viral infections, including Dengue and Zika, identifying mosquito breeding sites is key to control their spread. Experiments with 5-band drone imagery show that our CNN-based approach is able to measure shallow water depths accurately up to a root mean square error of less than 0.5 cm, outperforming state-ofthe-art Random Forest methods and empirical approaches.

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MM4Drone: A Multi-spectral Image and mmWave Radar Approach for Identifying Mosquito Breeding Grounds via Aerial Drones

Cited by 3 publications

References 27 publications

Indoor Drone Localization and Tracking Based on Acoustic Inertial Measurement

Indoor Drone Localization and Tracking Based on Acoustic Inertial Measurement

Use of low-cost drones to map arbovirus vector habitats with multispectral aerial imagery

CNN-Based Estimation of Water Depth from Multispectral Drone Imagery for Mosquito Control

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