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    Unmanned Aerial Vehicles (UAVs) in environmental biology: A Review

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    Acquiring information about the environment is a key step during each study in the field of environmental biology at different levels, from an individual species to community and biome. However, obtaining information about the environment is frequently difficult because of, for example, the phenological timing, spatial distribution of a species or limited accessibility of a particular area for the field survey. Moreover, remote sensing technology, which enables the observation of the Earth’s surface and is currently very common in environmental research, has many limitations such as insufficient spatial, spectral and temporal resolution and a high cost of data acquisition. Since the 1990s, researchers have been exploring the potential of different types of unmanned aerial vehicles (UAVs) for monitoring Earth’s surface. The present study reviews recent scientific literature dealing with the use of UAV in environmental biology. Amongst numerous papers, short communications and conference abstracts, we selected 110 original studies of how UAVs can be used in environmental biology and which organisms can be studied in this manner. Most of these studies concerned the use of UAV to measure the vegetation parameters such as crown height, volume, number of individuals (14 studies) and quantification of the spatio-temporal dynamics of vegetation changes (12 studies). UAVs were also frequently applied to count birds and mammals, especially those living in the water. Generally, the analytical part of the present study was divided into following sections: (1) detecting, assessing and predicting threats on vegetation, (2) measuring the biophysical parameters of vegetation, (3) quantifying the dynamics of changes in plants and habitats and (4) population and behaviour studies of animals. At the end, we also synthesised all the information showing, amongst others, the advances in environmental biology because of UAV application. Considering that 33% of studies found and included in this review were published in 2017 and 2018, it is expected that the number and variety of applications of UAVs in environmental biology will increase in the future

    무인비행체 νƒ‘μž¬ 열화상 및 싀화상 이미지λ₯Ό ν™œμš©ν•œ 야생동물 탐지 κ°€λŠ₯μ„± 연ꡬ

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    ν•™μœ„λ…Όλ¬Έ(석사) -- μ„œμšΈλŒ€ν•™κ΅λŒ€ν•™μ› : ν™˜κ²½λŒ€ν•™μ› ν™˜κ²½μ‘°κ²½ν•™κ³Ό, 2022.2. μ†‘μ˜κ·Ό.μ•Όμƒλ™λ¬Όμ˜ 탐지와 λͺ¨λ‹ˆν„°λ§μ„ μœ„ν•΄, ν˜„μž₯ 직접 κ΄€μ°°, 포획-재포획과 같은 전톡적 쑰사 방법이 λ‹€μ–‘ν•œ λͺ©μ μœΌλ‘œ μˆ˜ν–‰λ˜μ–΄μ™”λ‹€. ν•˜μ§€λ§Œ, μ΄λŸ¬ν•œ 방법듀은 λ§Žμ€ μ‹œκ°„κ³Ό μƒλŒ€μ μœΌλ‘œ λΉ„μ‹Ό λΉ„μš©μ΄ ν•„μš”ν•˜λ©°, μ‹ λ’° κ°€λŠ₯ν•œ 탐지 κ²°κ³Όλ₯Ό μ–»κΈ° μœ„ν•΄μ„  μˆ™λ ¨λœ ν˜„μž₯ μ „λ¬Έκ°€κ°€ ν•„μš”ν•˜λ‹€. κ²Œλ‹€κ°€, 전톡적인 ν˜„μž₯ 쑰사 방법은 ν˜„μž₯μ—μ„œ 야생동물을 λ§ˆμ£ΌμΉ˜λŠ” λ“± μœ„ν—˜ν•œ 상황에 μ²˜ν•  수 μžˆλ‹€. 이에 따라, 카메라 νŠΈλž˜ν•‘, GPS 좔적, eDNA μƒ˜ν”Œλ§κ³Ό 같은 원격 쑰사 방법이 기쑴의 전톡적 쑰사방법을 λŒ€μ²΄ν•˜λ©° λ”μš± 빈번히 μ‚¬μš©λ˜κΈ° μ‹œμž‘ν–ˆλ‹€. ν•˜μ§€λ§Œ, μ΄λŸ¬ν•œ 방법듀은 μ—¬μ „νžˆ λͺ©ν‘œλ‘œ ν•˜λŠ” λŒ€μƒμ˜ 전체 면적과, κ°œλ³„ 개체λ₯Ό 탐지할 수 μ—†λ‹€λŠ” ν•œκ³„λ₯Ό 가지고 μžˆλ‹€. μ΄λŸ¬ν•œ ν•œκ³„λ₯Ό κ·Ήλ³΅ν•˜κΈ° μœ„ν•΄, 무인비행체 (UAV, Unmanned Aerial Vehicle)κ°€ 야생동물 νƒμ§€μ˜ λŒ€μ€‘μ μΈ λ„κ΅¬λ‘œ μžλ¦¬λ§€κΉ€ν•˜κ³  μžˆλ‹€. UAV의 κ°€μž₯ 큰 μž₯점은, μ„ λͺ…ν•˜κ³  μ΄˜μ΄˜ν•œ 곡간 및 μ‹œκ°„ν•΄μƒλ„μ™€ ν•¨κ»˜ 전체 연ꡬ 지역에 λŒ€ν•œ 동물 탐지가 κ°€λŠ₯ν•˜λ‹€λŠ” 것이닀. 이에 더해, UAVλ₯Ό μ‚¬μš©ν•¨μœΌλ‘œμ¨, μ ‘κ·Όν•˜κΈ° μ–΄λ €μš΄ μ§€μ—­μ΄λ‚˜ μœ„ν—˜ν•œ 곳에 λŒ€ν•œ 쑰사가 κ°€λŠ₯해진닀. ν•˜μ§€λ§Œ, μ΄λŸ¬ν•œ 이점 외에, UAV의 단점도 λͺ…ν™•νžˆ μ‘΄μž¬ν•œλ‹€. λŒ€μƒμ§€, λΉ„ν–‰ 속도 및 높이 λ“±κ³Ό 같이 UAVλ₯Ό μ‚¬μš©ν•˜λŠ” ν™˜κ²½μ— 따라, μž‘μ€ 동물, μšΈμ°½ν•œ μˆ²μ†μ— μžˆλŠ” 개체, λΉ λ₯΄κ²Œ μ›€μ§μ΄λŠ” 동물을 νƒμ§€ν•˜λŠ” 것이 μ œν•œλœλ‹€. λ˜ν•œ, κΈ°μƒν™˜κ²½μ— λ”°λΌμ„œλ„ 비행이 λΆˆκ°€ν•  수 있고, 배터리 μš©λŸ‰μœΌλ‘œ μΈν•œ λΉ„ν–‰μ‹œκ°„μ˜ μ œν•œλ„ μ‘΄μž¬ν•œλ‹€. ν•˜μ§€λ§Œ, μ •λ°€ν•œ 탐지가 λΆˆκ°€λŠ₯ν•˜λ”λΌλ„, 이와 κ΄€λ ¨ 연ꡬ가 κΎΈμ€€νžˆ μˆ˜ν–‰λ˜κ³  있으며, 선행연ꡬ듀은 μœ‘μƒ 및 해상 포유λ₯˜, μ‘°λ₯˜, 그리고 파좩λ₯˜ 등을 νƒμ§€ν•˜λŠ” 데에 μ„±κ³΅ν•˜μ˜€λ‹€. UAVλ₯Ό 톡해 μ–»μ–΄μ§€λŠ” κ°€μž₯ λŒ€ν‘œμ μΈ λ°μ΄ν„°λŠ” 싀화상 이미지이닀. 이λ₯Ό μ‚¬μš©ν•΄ λ¨Έμ‹ λŸ¬λ‹ 및 λ”₯λŸ¬λ‹ (ML-DL, Machine Learning and Deep Learning) 방법이 주둜 μ‚¬μš©λ˜κ³  μžˆλ‹€. μ΄λŸ¬ν•œ 방법은 μƒλŒ€μ μœΌλ‘œ μ •ν™•ν•œ 탐지 κ²°κ³Όλ₯Ό λ³΄μ—¬μ£Όμ§€λ§Œ, νŠΉμ • 쒅을 탐지할 수 μžˆλŠ” λͺ¨λΈμ˜ κ°œλ°œμ„ μœ„ν•΄μ„  μ΅œμ†Œν•œ 천 μž₯의 이미지가 ν•„μš”ν•˜λ‹€. 싀화상 이미지 외에도, 열화상 이미지 λ˜ν•œ UAVλ₯Ό 톡해 νšλ“ 될 수 μžˆλ‹€. 열화상 μ„Όμ„œ 기술의 개발과 μ„Όμ„œ κ°€κ²©μ˜ ν•˜λ½μ€ λ§Žμ€ 야생동물 μ—°κ΅¬μžλ“€μ˜ 관심을 μ‚¬λ‘œμž‘μ•˜λ‹€. 열화상 카메라λ₯Ό μ‚¬μš©ν•˜λ©΄ λ™λ¬Όμ˜ 체온과 μ£Όλ³€ν™˜κ²½κ³Όμ˜ μ˜¨λ„ 차이λ₯Ό 톡해 μ •μ˜¨λ™λ¬Όμ„ νƒμ§€ν•˜λŠ” 것이 κ°€λŠ₯ν•˜λ‹€. ν•˜μ§€λ§Œ, μƒˆλ‘œμš΄ 데이터가 μ‚¬μš©λ˜λ”λΌλ„, μ—¬μ „νžˆ ML-DL 방법이 동물 탐지에 주둜 μ‚¬μš©λ˜κ³  있으며, μ΄λŸ¬ν•œ 방법은 UAVλ₯Ό ν™œμš©ν•œ μ•Όμƒλ™λ¬Όμ˜ μ‹€μ‹œκ°„ 탐지λ₯Ό μ œν•œν•œλ‹€. λ”°λΌμ„œ, λ³Έ μ—°κ΅¬λŠ” 열화상과 싀화상 이미지λ₯Ό ν™œμš©ν•œ 동물 μžλ™ 탐지 λ°©λ²•μ˜ 개발과, 개발된 방법이 이전 λ°©λ²•λ“€μ˜ 평균 μ΄μƒμ˜ 정확도와 ν•¨κ»˜ ν˜„μž₯μ—μ„œ μ‹€μ‹œκ°„μœΌλ‘œ μ‚¬μš©λ  수 μžˆλ„λ‘ ν•˜λŠ” 것을 λͺ©ν‘œλ‘œ ν•œλ‹€.For wildlife detection and monitoring, traditional methods such as direct observation and capture-recapture have been carried out for diverse purposes. However, these methods require a large amount of time, considerable expense, and field-skilled experts to obtain reliable results. Furthermore, performing a traditional field survey can result in dangerous situations, such as an encounter with wild animals. Remote monitoring methods, such as those based on camera trapping, GPS collars, and environmental DNA sampling, have been used more frequently, mostly replacing traditional survey methods, as the technologies have developed. But these methods still have limitations, such as the lack of ability to cover an entire region or detect individual targets. To overcome those limitations, the unmanned aerial vehicle (UAV) is becoming a popular tool for conducting a wildlife census. The main benefits of UAVs are able to detect animals remotely covering a wider region with clear and fine spatial and temporal resolutions. In addition, by operating UAVs investigate hard to access or dangerous areas become possible. However, besides these advantages, the limitations of UAVs clearly exist. By UAV operating environments such as study site, flying height or speed, the ability to detect small animals, targets in the dense forest, tracking fast-moving animals can be limited. And by the weather, operating UAV is unable, and the flight time is limited by the battery matters. Although detailed detection is unavailable, related researches are developing and previous studies used UAV to detect terrestrial and marine mammals, avian and reptile species. The most common type of data acquired by UAVs is RGB images. Using these images, machine-learning and deep-learning (ML–DL) methods were mainly used for wildlife detection. ML–DL methods provide relatively accurate results, but at least 1,000 images are required to develop a proper detection model for specific species. Instead of RGB images, thermal images can be acquired by a UAV. The development of thermal sensor technology and sensor price reduction has attracted the interest of wildlife researchers. Using a thermal camera, homeothermic animals can be detected based on the temperature difference between their bodies and the surrounding environment. Although the technology and data are new, the same ML–DL methods were typically used for animal detection. These ML-DL methods limit the use of UAVs for real-time wildlife detection in the field. Therefore, this paper aims to develop an automated animal detection method with thermal and RGB image datasets and to utilize it under in situ conditions in real-time while ensuring the average-above detection ability of previous methods.Abstract I Contents IV List of Tables VII List of Figures VIII Chapter 1. Introduction 1 1.1 Research background 1 1.2 Research goals and objectives 10 1.2.1 Research goals 10 1.2.2 Research objectives 11 1.3 Theoretical background 13 1.3.1 Concept of the UAV 13 1.3.2 Concept of the thermal camera 13 Chapter 2. Methods 15 2.1 Study site 15 2.2 Data acquisition and preprocessing 16 2.2.1 Data acquisition 16 2.2.2 RGB lens distortion correction and clipping 19 2.2.3 Thermal image correction by fur color 21 2.2.4 Unnatural object removal 22 2.3 Animal detection 24 2.3.1 Sobel edge creation and contour generation 24 2.3.2 Object detection and sorting 26 Chapter 3. Results 30 3.1 Number of counted objects 31 3.2 Time costs of image types 33 Chapter 4. Discussion 36 4.1 Reference comparison 36 4.2 Instant detection 40 4.3 Supplemental usage 41 4.4 Utility of thermal sensors 42 4.5 Applications in other fields 43 Chapter 5. Conclusions 47 References 49 Appendix: Glossary 61 초둝 62석

    A Comprehensive Review on Computer Vision Analysis of Aerial Data

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    With the emergence of new technologies in the field of airborne platforms and imaging sensors, aerial data analysis is becoming very popular, capitalizing on its advantages over land data. This paper presents a comprehensive review of the computer vision tasks within the domain of aerial data analysis. While addressing fundamental aspects such as object detection and tracking, the primary focus is on pivotal tasks like change detection, object segmentation, and scene-level analysis. The paper provides the comparison of various hyper parameters employed across diverse architectures and tasks. A substantial section is dedicated to an in-depth discussion on libraries, their categorization, and their relevance to different domain expertise. The paper encompasses aerial datasets, the architectural nuances adopted, and the evaluation metrics associated with all the tasks in aerial data analysis. Applications of computer vision tasks in aerial data across different domains are explored, with case studies providing further insights. The paper thoroughly examines the challenges inherent in aerial data analysis, offering practical solutions. Additionally, unresolved issues of significance are identified, paving the way for future research directions in the field of aerial data analysis.Comment: 112 page

    Small-Object Detection in Remote Sensing Images with End-to-End Edge-Enhanced GAN and Object Detector Network

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    The detection performance of small objects in remote sensing images is not satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) shows remarkable image enhancement performance, but reconstructed images miss high-frequency edge information. Therefore, object detection performance degrades for small objects on recovered noisy and low-resolution remote sensing images. Inspired by the success of edge enhanced GAN (EEGAN) and ESRGAN, we apply a new edge-enhanced super-resolution GAN (EESRGAN) to improve the image quality of remote sensing images and use different detector networks in an end-to-end manner where detector loss is backpropagated into the EESRGAN to improve the detection performance. We propose an architecture with three components: ESRGAN, Edge Enhancement Network (EEN), and Detection network. We use residual-in-residual dense blocks (RRDB) for both the ESRGAN and EEN, and for the detector network, we use the faster region-based convolutional network (FRCNN) (two-stage detector) and single-shot multi-box detector (SSD) (one stage detector). Extensive experiments on a public (car overhead with context) and a self-assembled (oil and gas storage tank) satellite dataset show superior performance of our method compared to the standalone state-of-the-art object detectors.Comment: This paper contains 27 pages and accepted for publication in MDPI remote sensing journal. GitHub Repository: https://github.com/Jakaria08/EESRGAN (Implementation

    Real-time Aerial Detection and Reasoning on Embedded-UAVs

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    We present a unified pipeline architecture for a real-time detection system on an embedded system for UAVs. Neural architectures have been the industry standard for computer vision. However, most existing works focus solely on concatenating deeper layers to achieve higher accuracy with run-time performance as the trade-off. This pipeline of networks can exploit the domain-specific knowledge on aerial pedestrian detection and activity recognition for the emerging UAV applications of autonomous surveying and activity reporting. In particular, our pipeline architectures operate in a time-sensitive manner, have high accuracy in detecting pedestrians from various aerial orientations, use a novel attention map for multi-activities recognition, and jointly refine its detection with temporal information. Numerically, we demonstrate our model's accuracy and fast inference speed on embedded systems. We empirically deployed our prototype hardware with full live feeds in a real-world open-field environment.Comment: In TGR

    Multiview Aerial Visual Recognition (MAVREC): Can Multi-view Improve Aerial Visual Perception?

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    Despite the commercial abundance of UAVs, aerial data acquisition remains challenging, and the existing Asia and North America-centric open-source UAV datasets are small-scale or low-resolution and lack diversity in scene contextuality. Additionally, the color content of the scenes, solar-zenith angle, and population density of different geographies influence the data diversity. These two factors conjointly render suboptimal aerial-visual perception of the deep neural network (DNN) models trained primarily on the ground-view data, including the open-world foundational models. To pave the way for a transformative era of aerial detection, we present Multiview Aerial Visual RECognition or MAVREC, a video dataset where we record synchronized scenes from different perspectives -- ground camera and drone-mounted camera. MAVREC consists of around 2.5 hours of industry-standard 2.7K resolution video sequences, more than 0.5 million frames, and 1.1 million annotated bounding boxes. This makes MAVREC the largest ground and aerial-view dataset, and the fourth largest among all drone-based datasets across all modalities and tasks. Through our extensive benchmarking on MAVREC, we recognize that augmenting object detectors with ground-view images from the corresponding geographical location is a superior pre-training strategy for aerial detection. Building on this strategy, we benchmark MAVREC with a curriculum-based semi-supervised object detection approach that leverages labeled (ground and aerial) and unlabeled (only aerial) images to enhance the aerial detection. We publicly release the MAVREC dataset: https://mavrec.github.io
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