Monitoring underwater habitats is a vital part of observing the condition of the environment. The detection and mapping of underwater vegetation, especially seagrass has drawn the attention of the research community as early as the nineteen eighties. Initially, this monitoring relied on in situ observation by experts. Later, advances in remote-sensing technology, satellite-monitoring techniques and, digital photo- and video-based techniques opened a window to quicker, cheaper, and, potentially, more accurate seagrass-monitoring methods. So far, for seagrass detection and mapping, digital images from airborne cameras, spectral images from satellites, acoustic image data using underwater sonar technology, and digital underwater photo and video images have been used to map the seagrass meadows or monitor their condition. In this article, we have reviewed the recent approaches to seagrass detection and mapping to understand the gaps of the present approaches and determine further research scope to monitor the ocean health more easily. We have identified four classes of approach to seagrass mapping and assessment: still image-, video data-, acoustic image-, and spectral image data-based techniques. We have critically analysed the surveyed approaches and found the research gaps including the need for quick, cheap and effective imaging techniques robust to depth, turbidity, location and weather conditions, fully automated seagrass detectors that can work in real-time, accurate techniques for estimating the seagrass density, and the availability of high computation facilities for processing large scale data. For addressing these gaps, future research should focus on developing cheaper image and video data collection techniques, deep learning based automatic annotation and classification, and real-time percentage-cover calculation.
Rutgers, The State University of New Jersey（罗格斯大学，新泽西州立大学）；NEC Laboratories America Inc.（NEC美国实验室公司）
Urban flooding is becoming a common and devastating hazard to cause life loss and economic damage. Monitoring and understanding urban flooding in the local scale is a challenging task due to the complicated urban landscape, intricate hydraulic process, and the lack of high-quality and resolution data. The emerging smart city technology such as monitoring cameras provides an unprecedented opportunity to address the data issue. However, estimating the water accumulation on the land surface based on the monitoring footage is unreliable using the traditional segmentation technique because the boun…
Mila（米拉）；University of Texas at El Paso（得克萨斯大学埃尔帕索分校）；Integrated Centre for Mountain Development（山地发展综合中心）；Microsoft AI for Good Research Lab（微软AI公益研究实验室）；University of Wisconsin - Madison（威斯康星大学麦迪逊分校）
Glacier mapping is key to ecological monitoring in the hkh region. Climate change poses a risk to individuals whose livelihoods depend on the health of glacier ecosystems. In this work, we present a machine learning based approach to support ecological monitoring, with a focus on glaciers. Our approach is based on semi-automated mapping from satellite images. We utilize readily available remote sensing data to create a model to identify and outline both clean ice and debris-covered glaciers from satellite imagery. We also release data and develop a web tool that allows experts to visualize and…
Large scale cross-regional remote sensing flood monitoring framework for operative mapping and impact analysis
Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究所）；Trofimuk Institute of Petroleum Geology and Geophysics SB RAS（俄罗斯科学院西伯利亚分院特罗菲穆克石油天然气地质与地球物理研究所）；King Fahd University of Petroleum and Minerals（法赫德国王石油与矿产大学）；Tyumen Industrial University（秋明工业大学）；Huawei Russian Research Institute（华为俄罗斯研究院）
JOANNEUM RESEARCH Forschungsgesellschaft mbH（JOANNEUM RESEARCH 研究有限公司）
The study of human factors in the frame of interaction studies has been relevant for usability engi-neering and ergonomics for decades. Today, with the advent of wearable eye-tracking and Google glasses, monitoring of human factors will soon become ubiquitous. This work describes a computer vision system that enables pervasive mapping and monitoring of human attention. The key contribu-tion is that our methodology enables full 3D recovery of the gaze pointer, human view frustum and associated human centred measurements directly into an automatically computed 3D model in real-time. We apply RGB…
SHAZAM: Self-Supervised Change Monitoring for Hazard Detection and Mapping
The University of Sydney（悉尼大学）；The University of New England（新英格兰大学）；Australian Research Council (ARC) Training Centre for CubeSats, UAVs & Their Applications (CUAVA)（澳大利亚研究理事会立方卫星、无人机及其应用培训中心）
Mapping Global Floods with 10 Years of Satellite Radar Data
Microsoft AI for Good Research Lab（微软人工智能公益研究院）；Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison（威斯康星大学麦迪逊分校合作气象卫星研究所）
INRAE（法国国家农业、食品与环境研究院）；University of Montpellier（蒙彼利埃大学）；CIRAD（法国国际农业发展研究中心）
Nowadays, there is a general agreement on the need to better characterize agricultural monitoring systems in response to the global changes. Timely and accurate land use/land cover mapping can support this vision by providing useful information at fine scale. Here, a deep learning approach is proposed to deal with multi-source land cover mapping at object level. The approach is based on an extension of Recurrent Neural Network enriched via an attention mechanism dedicated to multi-temporal data context. Moreover, a new hierarchical pretraining strategy designed to exploit specific domain knowl…
Riverine Land Cover Mapping through Semantic Segmentation of Multispectral Point Clouds
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI（遥感与摄影测量系，芬兰地理研究所）；Faculty of Technology, University of Turku（技术学院，图尔库大学）；Department of Geography and Geology, University of Turku（地理与地质系，图尔库大学）；Faculty of Geological Sciences, Complutense University of Madrid（地质科学学院，马德里卡洛斯三世大学）
With increasing urbanization, in recent years there has been a growing interest and need in monitoring and analyzing urban flood events. Social media, as a new data source, can provide real-time information for flood monitoring. The social media posts with locations are often referred to as Volunteered Geographic Information (VGI), which can reveal the spatial pattern of such events. Since more images are shared on social media than ever before, recent research focused on the extraction of flood-related posts by analyzing images in addition to texts. Apart from merely classifying posts as floo…
Wuhan University（武汉大学）；University of Arkansas（阿肯色大学）；Leador Spatial Information Technology Corporation（立得空间信息技术股份有限公司）
In a complex urban scene, observation from a single sensor unavoidably leads to voids in observations, failing to describe urban objects in a comprehensive manner. In this paper, we propose a spatio-temporal-spectral-angular observation model to integrate observations from UAV and mobile mapping vehicle platform, realizing a joint, coordinated observation operation from both air and ground. We develop a multi-source remote sensing data acquisition system to effectively acquire multi-angle data of complex urban scenes. Multi-source data fusion solves the missing data problem caused by occlusion…
CIRAD（法国国际发展农业研究中心）；University of Montpellier（蒙彼利埃大学）；AgroParisTech（巴黎高科农业学院）；CNRS（法国国家科学研究中心）；IRSTEA（法国国家环境与农业科学技术研究院）；LIRMM（蒙彼利埃计算机、机器人与微电子实验室）
Nowadays, Earth Observation systems provide a multitude of heterogeneous remote sensing data. How to manage such richness leveraging its complementarity is a crucial chal- lenge in modern remote sensing analysis. Data Fusion techniques deal with this point proposing method to combine and exploit complementarity among the different data sensors. Considering optical Very High Spatial Resolution (VHSR) images, satellites obtain both Multi Spectral (MS) and panchro- matic (PAN) images at different spatial resolution. VHSR images are extensively exploited to produce land cover maps to deal with agr…
University of Minnesota（明尼苏达大学）；University of Pittsburgh（匹兹堡大学）
Land cover mapping is essential for monitoring global environmental change and managing natural resources. Unfortunately, traditional classification models are plagued by limited training data available in existing land cover products and data heterogeneity over space and time. In this survey, we provide a structured and comprehensive overview of challenges in land cover mapping and machine learning methods used to address these problems. We also discuss the gaps and opportunities that exist for advancing research in this promising direction.
Westlake University（西湖大学）；University of Calgary（卡尔加里大学）
Wide dynamic range (WDR) image tone mapping is in high demand in many applications like film production, security monitoring, and photography. It is especially crucial for mobile devices because most of the images taken today are from mobile phones, hence such technology is highly demanded in the consumer market of mobile devices and is essential for a good customer experience. However, high-quality and high-performance WDR image tone mapping implementations are rarely found in the mobile-end. In this paper, we introduce a high performance, mobile-end WDR image tone mapping implementation. It …
YOLO-Based Pipeline Monitoring in Challenging Visual Environments
Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genoa（电子电气与航海工程系（DITEN），热那亚大学）；Department of Bio-systems Engineering, Gyeongsang National University（生物系统工程系，全州国立大学）；Department of Mechanical Engineering and Maritime Studies, Western Norway University of Applied Sciences（机械工程与航海研究系，西挪威应用科学大学）
Two Scalable Approaches for Burned-Area Mapping Using U-Net and Landsat Imagery
University of Chile（智利大学）；Universidad Tecnológica Metropolitana（大都会理工大学）；Complex Engineering System Institute（复杂工程系统研究所）；University of California Berkeley（加州大学伯克利分校）；Center for Climate and Resilience Research（气候与恢复力研究中心）；Universidad de La Frontera（拉弗朗特拉大学）
本文提出两种基于 U-Net 的可扩展过火区制图方法（128 和 AllSizes），通过在智利火灾多发区的 Landsat 数据上训练与测试，证明提升数据集类别平衡的 AS 模型性能更优，为自动过火区制图工具开发提供基础。
Blood pressure (BP) monitoring is vital in daily healthcare, especially for cardiovascular diseases. However, BP values are mainly acquired through the contact sensing method, which is inconvenient and unfriendly to continuous BP measurement. Hence, we propose an efficient end-to-end network to estimate the BP values from a facial video to achieve remote BP measurement in daily life. In this study, we first derived a Spatial-temporal map of a short-time (~15s) facial video. According to the Spatial-temporal map, we then regressed the BP ranges by a designed blood pressure classifier and simult…
With the growing global deployment of carbon capture and sequestration technology to combat climate change, monitoring and detection of potential CO2 leakage through existing or storage induced faults are critical to the safe and long-term viability of the technology. Recent work on time-lapse seismic monitoring of CO2 storage has shown promising results in its ability to monitor the growth of the CO2 plume from surface recorded seismic data. However, due to the low sensitivity of seismic imaging to CO2 concentration, additional developments are required to efficiently interpret the seismic im…
This paper addresses outdoor terrain mapping using overhead images obtained from an unmanned aerial vehicle. Dense depth estimation from aerial images during flight is challenging. While feature-based localization and mapping techniques can deliver real-time odometry and sparse points reconstruction, a dense environment model is generally recovered offline with significant computation and storage. This paper develops a joint 2D-3D learning approach to reconstruct local meshes at each camera keyframe, which can be assembled into a global environment model. Each local mesh is initialized from sp…
Harnessing Self-Supervised Deep Learning and Geostationary Remote Sensing for Advancing Wildfire and Associated Air Quality Monitoring: Improved Smoke and Fire Front Masking using GOES and TEMPO Radiance Data
Spatial Informatics Group, LLC.（空间信息集团有限责任公司）；ADNET systems, INC.（ADNET系统公司）；NASA Langley Research Center（美国国家航空航天局兰利研究中心）；University at Albany（奥尔巴尼大学）
Robert Bosch Center for Cyber Physical Systems（罗伯特·博世信息物理系统研究中心）；Indian Institute of Science（印度科学学院）；Indian Institute of Technology Bombay（印度理工学院孟买分校）
More than one billion people live in slums around the world. In some developing countries, slum residents make up for more than half of the population and lack reliable sanitation services, clean water, electricity, other basic services. Thus, slum rehabilitation and improvement is an important global challenge, and a significant amount of effort and resources have been put into this endeavor. These initiatives rely heavily on slum mapping and monitoring, and it is essential to have robust and efficient methods for mapping and monitoring existing slum settlements. In this work, we introduce an…
