Mosquitoes are known vectors for disease transmission that cause over one million deaths globally each year. The majority of natural mosquito habitats are areas containing standing water that are challenging to detect using conventional ground-based technology on a macro scale. Contemporary approaches, such as drones, UAVs, and other aerial imaging technology are costly when implemented and are only most accurate on a finer spatial scale whereas the proposed convolutional neural network(CNN) approach can be applied for disease risk mapping and further guide preventative efforts on a more global scale. By assessing the performance of autonomous mosquito habitat detection technology, the transmission of mosquito-borne diseases can be prevented in a cost-effective manner. This approach aims to identify the spatiotemporal distribution of mosquito habitats in extensive areas that are difficult to survey using ground-based technology by employing computer vision on satellite imagery for proof of concept. The research presents an evaluation and the results of 3 different CNN models to determine their accuracy of predicting large-scale mosquito habitats. For this approach, a dataset was constructed containing a variety of geographical features. Larger land cover variables such as ponds/lakes, inlets, and rivers were utilized to classify mosquito habitats while minute sites were omitted for higher accuracy on a larger scale. Using the dataset, multiple CNN networks were trained and evaluated for accuracy of habitat prediction. Utilizing a CNN-based approach on readily available satellite imagery is cost-effective and scalable, unlike most aerial imaging technology. Testing revealed that YOLOv4 obtained greater accuracy in mosquito habitat detection for identifying large-scale mosquito habitats.
Habitat Classification from Ground-Level Imagery Using Deep Neural Networks
organization= School of Engineering \& Physical Sciences, University of Lincoln , addressline= Brayford Pool , city= Lincoln , postcode= LN6 7TS , country= United Kingdom；organization= UK Centre for Ecology \& Hydrology , addressline= Lancaster Environment Centre, Library Avenue, Bailrigg , city= Lancaster , postcode= LA1 4AP , country= United Kingdom；organization= UK Centre for Ecology \& Hydrology , addressline= Maclean Building, Benson Lane, Crowmarsh Gifford, Wallingford , city= Oxfordshire , postcode= OX10 8BB , country= United Kingdom；organization= School of Natural Sciences, University of Lincoln , addressline= Brayford Pool , city= Lincoln , postcode= LN6 7TS , country= United Kingdom；organization= Department of Advanced Computing Sciences, Maastricht University , city= Maastricht , postcode= 6229 GS , country= The Netherlands
Automated classification of natural habitats using ground-level imagery
Centre for Environmental Intelligence, University of Exeter（埃克塞特大学环境智能中心）；Department of Computer Science, Faculty of Environment, Science and Economy, University of Exeter（埃克塞特大学环境、科学与经济学院计算机科学系）；Natural England（自然英格兰）
Much environmental enforcement in the United States has historically relied on either self-reported data or physical, resource-intensive, infrequent inspections. Advances in remote sensing and computer vision, however, have the potential to augment compliance monitoring by detecting early warning signs of noncompliance. We demonstrate a process for rapid identification of significant structural expansion using Planet's 3m/pixel satellite imagery products and focusing on Concentrated Animal Feeding Operations (CAFOs) in the US as a test case. Unpermitted building expansion has been a particular…
Australian Centre for Field Robotics（澳大利亚现场机器人中心）；University of Sydney（悉尼大学）
Autonomous Underwater Vehicles (AUVs) are increasingly being used to support scientific research and monitoring studies. One such application is in benthic habitat mapping where these vehicles collect seafloor imagery that complements broadscale bathymetric data collected using sonar. Using these two data sources, the relationship between remotely-sensed acoustic data and the sampled imagery can be learned, creating a habitat model. As the areas to be mapped are often very large and AUV systems collecting seafloor imagery can only sample from a small portion of the survey area, the information…
High-resolution optical and acoustic remote sensing datasets of the Puck Lagoon, Southern Baltic
Gdynia Maritime University（格丁尼亚海事大学）；Maritime Institute（海事研究所）；Cyprus University of Technology（塞浦路斯理工大学）；Technische Universität Berlin（柏林工业大学）；Gdańsk University of Technology（格但斯克理工大学）；Nicolaus Copernicus University in Toruń（托伦尼古拉·哥白尼大学）；Centre for Underwater Archaeology（水下考古中心）；University of Adelaide（阿德莱德大学）；Technical University Wien（维也纳工业大学）；MEWO SA（MEWO股份公司）；National Technical University of Athens（雅典国立技术大学）
Univ. of Almería（阿尔梅里亚大学）；Univ. of Granada（格拉纳达大学）
There is a growing demand for accurate high-resolution land cover maps in many fields, e.g., in land-use planning and biodiversity conservation. Developing such maps has been performed using Object-Based Image Analysis (OBIA) methods, which usually reach good accuracies, but require a high human supervision and the best configuration for one image can hardly be extrapolated to a different image. Recently, the deep learning Convolutional Neural Networks (CNNs) have shown outstanding results in object recognition in the field of computer vision. However, they have not been fully explored yet in …
Finnish Environment Institute（芬兰环境研究所）；Metsähallitus, National Parks（芬兰林业管理局国家公园）
Remote sensing benefits habitat conservation by making monitoring of large areas easier compared to field surveying especially if the remote sensed data can be automatically analyzed. An important aspect of monitoring is classifying and mapping habitat types present in the monitored area. Automatic classification is a difficult task, as classes have fine-grained differences and their distributions are long-tailed and unbalanced. Usually training data used for automatic land cover classification relies on fully annotated segmentation maps, annotated from remote sensed imagery to a fairly high-l…
The Amazon rainforests have been suffering widespread damage, both via natural and artificial means. Every minute, it is estimated that the world loses forest cover the size of 48 football fields. Deforestation in the Amazon rainforest has led to drastically reduced biodiversity, loss of habitat, climate change, and other biological losses. In this respect, it has become essential to track how the nature of these forests change over time. Image classification using deep learning can help speed up this process by removing the manual task of classifying each image. Here, it is shown how convolut…
Seabed-Net: A multi-task network for joint bathymetry estimation and seabed classification from remote sensing imagery in shallow waters
Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin（技术大学柏林电子工程与计算机科学学院）；BIFOLD - Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）
Automated Linear Disturbance Mapping via Semantic Segmentation of Sentinel-2 Imagery
University of Winnipeg（温尼伯大学）；Hatfield Consultants Group（哈特菲尔德咨询集团）；University of Manitoba（曼尼托巴大学）；Pachama（帕查玛公司）；Environment and Climate Change Canada（加拿大环境与气候变化部）
Satellite imagery has long been an attractive data source that provides a wealth of information on human-inhabited areas. While super resolution satellite images are rapidly becoming available, little study has focused on how to extract meaningful information about human habitation patterns and economic scales from such data. We present READ, a new approach for obtaining essential spatial representation for any given district from high-resolution satellite imagery based on deep neural networks. Our method combines transfer learning and embedded statistics to efficiently learn critical spatial …
Detecting Endangered Marine Species in Autonomous Underwater Vehicle Imagery Using Point Annotations and Few-Shot Learning
Australian Centre for Robotics, University of Sydney（悉尼大学澳大利亚机器人中心）；Marine Technology Department, Norwegian University of Science and Technology（挪威科技大学海洋技术系）；Institute for Marine and Antarctic Studies, University of Tasmania（塔斯马尼亚大学海洋与南极研究所）
The Hong Kong Polytechnic University（香港理工大学）；Bavarian Forest National Park（巴伐利亚森林国家公园）
In this paper, we introduce a framework for segmenting instances of a common object class by multiple active contour evolution over semantic segmentation maps of images obtained through fully convolutional networks. The contour evolution is cast as an energy minimization problem, where the aggregate energy functional incorporates a data fit term, an explicit shape model, and accounts for object overlap. Efficient solution neighborhood operators are proposed, enabling optimization through metaheuristics such as simulated annealing. We instantiate the proposed framework in the context of segment…
The UN-Habitat estimates that over one billion people live in slums around the world. However, state-of-the-art techniques to detect the location of slum areas employ high-resolution satellite imagery, which is costly to obtain and process. As a result, researchers have started to look at utilising free and open-access medium resolution satellite imagery. Yet, there is no clear consensus on which data preparation and machine learning approaches are the most appropriate to use with such imagery data. In this paper, we evaluate two techniques (multi-spectral data and grey-level co-occurrence mat…
Avian botulism is a paralytic bacterial disease in birds often leading to high fatality. In-vitro diagnostic techniques such as Mouse Bioassay, ELISA, PCR are usually non-preventive, post-mortem in nature, and require invasive sample collection from affected sites or dead birds. In this study, we build a first-ever multi-spectral, remote-sensing imagery based global Bird-Area Water-bodies Dataset (BAWD) (i.e. fused satellite images of warm-water lakes/marshy-lands or similar water-body sites that are important for avian fauna) backed by on-ground reporting evidence of outbreaks. BAWD consists …
New York University（纽约大学）；Information Technology University, Pakistan（巴基斯坦信息技术大学）
The global population at risk of mosquito-borne diseases such as dengue, yellow fever, chikungunya and Zika is expanding. Infectious disease models commonly incorporate environmental measures like temperature and precipitation. Given increasing availability of high-resolution satellite imagery, here we consider including landscape features from satellite imagery into infectious disease prediction models. To do so, we implement a Convolutional Neural Network (CNN) model trained on Imagenet data and labelled landscape features in satellite data from London. We then incorporate landscape features…
Public health and habitat quality are crucial goals of urban planning. In recent years, the severe social and environmental impact of illegal waste dumping sites has made them one of the most serious problems faced by cities in the Global South, in a context of scarce information available for decision making. To help identify the location of dumping sites and track their evolution over time we adopt a data-driven model from the machine learning domain, analyzing satellite images. This allows us to take advantage of the increasing availability of geo-spatial open-data, high-resolution satellit…
Beluga Whale Detection from Satellite Imagery with Point Labels
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院 aerospace information research institute）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子、电气与通信工程学院）；College of Resources and Environment, University of Chinese Academy of Sciences（中国科学院大学资源与环境学院）；Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences（中国科学院地理科学与自然资源研究所）；Chinese Research Academy of Environmental Sciences（中国环境科学研究院）；School of Future Technology, University of Chinese Academy of Sciences（中国科学院大学未来技术学院）；Technical Institute of Physics and Chemistry, Chinese Academy of Sciences（中国科学院物理化学技术研究所）
本文提出结合点标注与 SAM 自动生成边界框并训练 YOLOv8 的流程，实现 VHR 卫星影像中确定鲸鱼、不确定鲸鱼和竖琴海豹的多类别检测。
Predicting butterfly species presence from satellite imagery using soft contrastive regularisation
The Alan Turing Institute, the UK（英国阿尔安·图灵研究所）；Wageningen University & Research, the NL（瓦赫宁根大学与研究中心）；University College London, the UK（伦敦大学学院）；UK Centre for Ecology & Hydrology, the UK（英国生态与水文研究中心）
Spatial variability has been observed in many geo-phenomena including climatic zones, USDA plant hardiness zones, and terrestrial habitat types (e.g., forest, grasslands, wetlands, and deserts). However, current deep learning methods follow a spatial-one-size-fits-all(OSFA) approach to train single deep neural network models that do not account for spatial variability. In this work, we propose and investigate a spatial-variability aware deep neural network(SVANN) approach, where distinct deep neural network models are built for each geographic area. We evaluate this approach using aerial image…
