Global surface water detection in very-high-resolution (VHR) satellite imagery can directly serve major applications such as refined flood mapping and water resource assessment. Although achievements have been made in detecting surface water in small-size satellite images corresponding to local geographic scales, datasets and methods suitable for mapping and analyzing global surface water have yet to be explored. To encourage the development of this task and facilitate the implementation of relevant applications, we propose the GLH-water dataset that consists of 250 satellite images and manually labeled surface water annotations that are distributed globally and contain water bodies exhibiting a wide variety of types (e.g., rivers, lakes, and ponds in forests, irrigated fields, bare areas, and urban areas). Each image is of the size 12,800 $\times$ 12,800 pixels at 0.3 meter spatial resolution. To build a benchmark for GLH-water, we perform extensive experiments employing representative surface water detection models, popular semantic segmentation models, and ultra-high resolution segmentation models. Furthermore, we also design a strong baseline with the novel pyramid consistency loss (PCL) to initially explore this challenge. Finally, we implement the cross-dataset and pilot area generalization experiments, and the superior performance illustrates the strong generalization and practical application of GLH-water. The dataset is available at https://jack-bo1220.github.io/project/GLH-water.html.
Learning to Holistically Detect Bridges from Large-Size VHR Remote Sensing Imagery
Wuhan University（武汉大学）；Huazhong University of Science and Technology（华中科技大学）；South China University of Technology（华南理工大学）；ByteDance AI Lab（字节跳动人工智能实验室）
Improving Satellite Imagery Masking using Multi-task and Transfer Learning
College of Information and Computer Sciences, University of Massachusetts（马萨诸塞大学信息与计算机科学学院）；University of Pittsburgh（匹兹堡大学）；University of North Carolina（北卡罗来纳大学）
Microsoft AI for Good Research Lab（微软人工智能公益研究实验室）；Stanford RegLab（斯坦福监管实验室）
Concentrated Animal Feeding Operations (CAFOs) pose serious risks to air, water, and public health, but have proven to be challenging to regulate. The U.S. Government Accountability Office notes that a basic challenge is the lack of comprehensive location information on CAFOs. We use the USDA's National Agricultural Imagery Program (NAIP) 1m/pixel aerial imagery to detect poultry CAFOs across the continental United States. We train convolutional neural network (CNN) models to identify individual poultry barns and apply the best performing model to over 42 TB of imagery to create the first nati…
Urban Flood Observations: A hand-labeled training and validation dataset of post-flood inundation
Pacific Northwest National Laboratory（太平洋西北国家实验室）；University of Arizona（亚利桑那大学）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；Utah State University（犹他州立大学）；Massachusetts Institute of Technology（麻省理工学院）；Columbia University（哥伦比亚大学）；University of Virginia（弗吉尼亚大学）
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Institute for Global Environmental Strategies (IGES)（全球环境战略研究所（IGES））
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 globa…
Hyper-spectral satellite imagery is now widely being used for accurate disaster prediction and terrain feature classification. However, in such classification tasks, most of the present approaches use only the spectral information contained in the images. Therefore, in this paper, we present a novel framework that takes into account both the spectral and spatial information contained in the data for land cover classification. For this purpose, we use the Gaussian Maximum Likelihood (GML) and Convolutional Neural Network methods for the pixel-wise spectral classification and then, using segment…
Department of Computer and Electrical Engineering, Rutgers University（计算机与电气工程系，罗格斯大学）；Cloud to Street（云街（Cloud to Street））
Accurate flood detection in near real time via high resolution, high latency satellite imagery is essential to prevent loss of lives by providing quick and actionable information. Instruments and sensors useful for flood detection are only available in low resolution, low latency satellites with region re-visit periods of up to 16 days, making flood alerting systems that use such satellites unreliable. This work presents H2O-Network, a self supervised deep learning method to segment floods from satellites and aerial imagery by bridging domain gap between low and high latency satellite and coar…
Monitoring of colonial waterbird nesting islands is essential to tracking waterbird population trends, which are used for evaluating ecosystem health and informing conservation management decisions. Recently, unmanned aerial vehicles, or drones, have emerged as a viable technology to precisely monitor waterbird colonies. However, manually counting waterbirds from hundreds, or potentially thousands, of aerial images is both difficult and time-consuming. In this work, we present a deep learning pipeline that can be used to precisely detect, count, and monitor waterbirds using aerial imagery coll…
King’s College London（伦敦国王学院）；UN Environment World Conservation Monitoring Centre（联合国环境规划署世界保护监测中心）
Dam reservoirs play an important role in meeting sustainable development goals and global climate targets. However, particularly for small dam reservoirs, there is a lack of consistent data on their geographical location. To address this data gap, a promising approach is to perform automated dam reservoir extraction based on globally available remote sensing imagery. It can be considered as a fine-grained task of water body extraction, which involves extracting water areas in images and then separating dam reservoirs from natural water bodies. We propose a novel deep neural network (DNN) based…
Linux Polska（波兰Linux公司）；University of Warsaw（华沙大学）；Agency for Restructuring and Modernisation of Agriculture（农业结构调整与现代化署）
Monitoring of land cover and land use is crucial in natural resources management. Automatic visual mapping can carry enormous economic value for agriculture, forestry, or public administration. Satellite or aerial images combined with computer vision and deep learning enable precise assessment and can significantly speed up change detection. Aerial imagery usually provides images with much higher pixel resolution than satellite data allowing more detailed mapping. However, there is still a lack of aerial datasets made for the segmentation, covering rural areas with a resolution of tens centime…
Urban Waterlogging Detection: A Challenging Benchmark and Large-Small Model Co-Adapter
School of Microelectronics and Communication Engineering, Chongqing University（重庆大学微电子与通信工程学院）；Huawei Technologies Co., Ltd.（华为技术有限公司）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）
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 …
Enhancing coastal water body segmentation with Landsat Irish Coastal Segmentation (LICS) dataset
ADAPT SFI Research Centre（ADAPT SFI研究中心）；School of Computer Science, University College Dublin（都柏林大学学院计算机科学学院）；Indian Institute of Technology Delhi（印度理工学院德里分校）；Envo-Geo Environmental Geoinformatics（Envo-Geo环境地理信息学机构）；Geological Survey Ireland（爱尔兰地质调查局）
National Technical University of Athens（雅典国家技术大学）；Cyprus University of Technology（塞浦路斯理工大学）
The determination of accurate bathymetric information is a key element for near offshore activities, hydrological studies such as coastal engineering applications, sedimentary processes, hydrographic surveying as well as archaeological mapping and biological research. UAV imagery processed with Structure from Motion (SfM) and Multi View Stereo (MVS) techniques can provide a low-cost alternative to established shallow seabed mapping techniques offering as well the important visual information. Nevertheless, water refraction poses significant challenges on depth determination. Till now, this pro…
Tsinghua University（清华大学）；Chinese Academy of Sciences（中国科学院）；University of California, Berkeley（加州大学伯克利分校）；Joint Center for Global Change Studies（全球变化研究联合中心）
The objective of this paper is to evaluate the potential of Gaofen-2 (GF-2) high resolution multispectral sensor (MS) and panchromatic (PAN) imagery on water mapping. Difficulties of water mapping on high resolution data includes: 1) misclassification between water and shadows or other low-reflectance ground objects, which is mostly caused by the spectral similarity within the given band range; 2) small water bodies with size smaller than the spatial resolution of MS image. To solve the confusion between water and low-reflectance objects, the Landsat 8 time series with two shortwave infrared (…
ELTE Eötvös Loránd University（厄特沃什·罗兰大学（ELTE））；Tisza Plastic Cup Initiative（蒂萨塑料杯倡议组织）
One of the biggest environmental problems of our time is the increase in illegal landfills in forests, rivers, on river banks and other secluded places. In addition, waste in rivers causes damage not only locally, but also downstream, both in the water and washed ashore. Large islands of waste can also form at hydroelectric power stations and dams, and if they continue to flow, they can cause further damage to the natural environment along the river. Recent studies have also proved that rivers are the main source of plastic pollution in marine environments. Monitoring potential sources of dang…
Hunan University（湖南大学）；State Key Laboratory of Integrated Services Networks, Xidian University（西安电子科技大学综合业务网理论及关键技术国家重点实验室）；Peng Cheng Laboratory（鹏城实验室）；Australian National University（澳大利亚国立大学）；University of Macau（澳门大学）；Sichuan University（四川大学）；Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿-罗森多夫研究中心）；Helmholtz Institute Freiberg for Resource Technology（亥姆霍兹弗莱贝格资源技术研究所）
The use of deep learning for water extraction requires precise pixel-level labels. However, it is very difficult to label high-resolution remote sensing images at the pixel level. Therefore, we study how to utilize point labels to extract water bodies and propose a novel method called the neighbor feature aggregation network (NFANet). Compared with pixellevel labels, point labels are much easier to obtain, but they will lose much information. In this paper, we take advantage of the similarity between the adjacent pixels of a local water-body, and propose a neighbor sampler to resample remote s…
University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；Texas A&M University（得克萨斯农工大学）
Visual scene understanding is the core task in making any crucial decision in any computer vision system. Although popular computer vision datasets like Cityscapes, MS-COCO, PASCAL provide good benchmarks for several tasks (e.g. image classification, segmentation, object detection), these datasets are hardly suitable for post disaster damage assessments. On the other hand, existing natural disaster datasets include mainly satellite imagery which have low spatial resolution and a high revisit period. Therefore, they do not have a scope to provide quick and efficient damage assessment tasks. Unm…
School of Information, University of California, Berkeley（加州大学伯克利分校信息学院）；IBM Chief Analytics Office（IBM首席分析办公室）
Climate change has caused reductions in river runoffs and aquifer recharge resulting in an increasingly unsustainable crop water demand from reduced freshwater availability. Achieving food security while deploying water in a sustainable manner will continue to be a major challenge necessitating careful monitoring and tracking of agricultural water usage. Historically, monitoring water usage has been a slow and expensive manual process with many imperfections and abuses. Ma-chine learning and remote sensing developments have increased the ability to automatically monitor irrigation patterns, bu…
NICTA（国家信息与通信技术研究院）；Yale-NUS College（耶鲁-新加坡国立大学学院）；The University of Tokyo（东京大学）；NAIST（奈良先端科学技术大学院大学）；Microsoft Research（微软研究院）
This paper introduces depth estimation from water drops. The key idea is that a single water drop adhered to window glass is totally transparent and convex, and thus optically acts like a fisheye lens. If we have more than one water drop in a single image, then through each of them we can see the environment with different view points, similar to stereo. To realize this idea, we need to rectify every water drop imagery to make radially distorted planar surfaces look flat. For this rectification, we consider two physical properties of water drops: (1) A static water drop has constant volume, an…
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）
An U-Net-Based Deep Neural Network for Cloud Shadow and Sun-Glint Correction of Unmanned Aerial System (UAS) Imagery
Department of Industrial and Systems Engineering, Mississippi State University, Mississippi State, MS, USA（工业与系统工程系，密苏里州立大学）；Department of Geosciences, Mississippi State University, Mississippi State, MS, USA（地球科学系，密苏里州立大学）
Carnegie Mellon University（卡内基梅隆大学）；Software Engineering Institute（软件工程研究所）；Defense Innovation Unit（国防创新部门）；Department of Defense（国防部）；CrowdAI, Inc.（CrowdAI公司）
We present xBD, a new, large-scale dataset for the advancement of change detection and building damage assessment for humanitarian assistance and disaster recovery research. Natural disaster response requires an accurate understanding of damaged buildings in an affected region. Current response strategies require in-person damage assessments within 24-48 hours of a disaster. Massive potential exists for using aerial imagery combined with computer vision algorithms to assess damage and reduce the potential danger to human life. In collaboration with multiple disaster response agencies, xBD prov…
Stanford University（斯坦福大学）；UC Berkeley（加州大学伯克利分校）；The California Institute of Technology（加州理工学院）
This paper introduces a new, highly consequential setting for the use of computer vision for environmental sustainability. Concentrated Animal Feeding Operations (CAFOs) (aka intensive livestock farms or "factory farms") produce significant manure and pollution. Dumping manure in the winter months poses significant environmental risks and violates environmental law in many states. Yet the federal Environmental Protection Agency (EPA) and state agencies have relied primarily on self-reporting to monitor such instances of "land application." Our paper makes four contributions. First, we introduc…
Extending Dynamic World Surface Water Mapping to Sentinel-1 with AlphaEarth Embeddings
Pacific Northwest National Laboratory（太平洋西北国家实验室）；NASA Goddard Space Flight Center（美国国家航空航天局戈达德太空飞行中心）；Fujitsu Research of America（富士通美国研究院）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）
For most hyperspectral remote sensing applications, removing bad bands, such as water absorption bands, is a required preprocessing step. Currently, the commonly applied method is by visual inspection, which is very time-consuming and it is easy to overlook some noisy bands. In this study, we find an inherent connection between target detection algorithms and the corrupted band removal. As an example, for the matched filter (MF), which is the most widely used target detection method for hyperspectral data, we present an automatic MF-based algorithm for bad band identification. The MF detector …
Seeing SDG 6 from space: local-scale monitoring of piped water and sewage systems across Africa using satellite imagery and self-supervised learning
Mila – Quebec AI Institute（魁北克人工智能研究所）；School of Computer Science, McGill University（麦吉尔大学计算机科学学院）；Department of Earth and Environmental Engineering, Columbia University（哥伦比亚大学地球与环境工程系）；Center for Learning the Earth with Artificial Intelligence and Physics (LEAP)（人工智能与物理学习地球中心（LEAP））；Division of Natural and Applied Sciences, Duke Kunshan University（杜克-昆山大学自然科学与应用科学系）
The Ohio State University（俄亥俄州立大学）；Byrd Polar and Climate Research Center（伯德极地与气候研究中心）
Remote sensing of the Earth's surface water is critical in a wide range of environmental studies, from evaluating the societal impacts of seasonal droughts and floods to the large-scale implications of climate change. Consequently, a large literature exists on the classification of water from satellite imagery. Yet, previous methods have been limited by 1) the spatial resolution of public satellite imagery, 2) classification schemes that operate at the pixel level, and 3) the need for multiple spectral bands. We advance the state-of-the-art by 1) using commercial imagery with panchromatic and …
Department of Computer Science, the University of Alabama（阿拉巴马大学计算机科学系）
Flood extent mapping plays a crucial role in disaster management and national water forecasting. In recent years, high-resolution optical imagery becomes increasingly available with the deployment of numerous small satellites and drones. However, analyzing such imagery data to extract flood extent poses unique challenges due to the rich noise and shadows, obstacles (e.g., tree canopies, clouds), and spectral confusion between pixel classes (flood, dry) due to spatial heterogeneity. Existing machine learning techniques often focus on spectral and spatial features from raster images without full…
An in-season early crop yield forecast before harvest can benefit the farmers to improve the production and enable various agencies to devise plans accordingly. We introduce a reliable and inexpensive method to predict crop yields from publicly available satellite imagery. The proposed method works directly on raw satellite imagery without the need to extract any hand-crafted features or perform dimensionality reduction on the images. The approach implicitly models the relevance of the different steps in the growing season and the various bands in the satellite imagery. We evaluate the propose…
