Deep Transfer Learning on Satellite Imagery Improves Air Quality Estimates in Developing Nations
Northeastern University（东北大学）；University Space Research Association（大学空间研究协会）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）；University of Massachusetts, Amherst（马萨诸塞大学阿默斯特分校）；University of British Columbia（不列颠哥伦比亚大学）；Imperial College London（帝国理工学院）
Nishant Yadav, Meytar Sorek-Hamer, Michael Von Pohle, Ata Akbari Asanjan, Adwait Sahasrabhojanee, Esra Suel, Raphael Arku, Violet Lingenfelter, Michael Brauer, … 展开作者
Nishant Yadav, Meytar Sorek-Hamer, Michael Von Pohle, Ata Akbari Asanjan, Adwait Sahasrabhojanee, Esra Suel, Raphael Arku, Violet Lingenfelter, Michael Brauer, Majid Ezzati, Nikunj Oza, Auroop R. Ganguly
Urban air pollution is a public health challenge in low- and middle-income countries (LMICs). However, LMICs lack adequate air quality (AQ) monitoring infrastructure. A persistent challenge has been our inability to estimate AQ accurately in LMIC cities, which hinders emergency preparedness and risk mitigation. Deep learning-based models that map satellite imagery to AQ can be built for high-income countries (HICs) with adequate ground data. Here we demonstrate that a scalable approach that adapts deep transfer learning on satellite imagery for AQ can extract meaningful estimates and insights in LMIC cities based on spatiotemporal patterns learned in HIC cities. The approach is demonstrated for Accra in Ghana, Africa, with AQ patterns learned from two US cities, specifically Los Angeles and New York.
Object detection and classification for aircraft are the most important tasks in the satellite image analysis. The success of modern detection and classification methods has been based on machine learning and deep learning. One of the key requirements for those learning processes is huge data to train. However, there is an insufficient portion of aircraft since the targets are on military action and oper- ation. Considering the characteristics of satellite imagery, this paper attempts to provide a framework of the simulated and unsupervised methodology without any additional su- pervision or p…
School of Computer Engineering, Iran University of Science and Technology（伊朗科技大学计算机工程学院）
Automatic airplane detection in aerial imagery has a variety of applications. Two of the significant challenges in this task are variations in the scale and direction of the airplanes. To solve these challenges, we present a rotation-and-scale invariant airplane proposal generator. We call this generator symmetric line segments (SLS) that is developed based on the symmetric and regular boundaries of airplanes from the top view. Then, the generated proposals are used to train a deep convolutional neural network for removing non-airplane proposals. Since each airplane can have multiple SLS propo…
Aircraft Detection in Satellite Imagery using Deep Learning Object Detectors
Department of Information Technology School of Computational and Physical AB Technologies（信息科技系 计算与物理AB技术学院）；Maulana Abul Kalam Azad University of Sciences Nagercoil, Kanyakumari, Technology, Kristu Jayanti (Deemed to be University) Tamil Nadu, India（马拉那阿布·卡拉姆·阿扎德科学大学纳杰科尔, 古里安, 技术, 克里斯图·贾亚尼大学（被认定为大学）泰米尔纳德邦, 印度）
Object detection models commonly deployed on uncrewed aerial systems (UAS) focus on identifying objects in the visible spectrum using Red-Green-Blue (RGB) imagery. However, there is growing interest in fusing RGB with thermal long wave infrared (LWIR) images to increase the performance of object detection machine learning (ML) models. Currently LWIR ML models have received less research attention, especially for both ground- and air-based platforms, leading to a lack of baseline performance metrics evaluating LWIR, RGB and LWIR-RGB fused object detection models. Therefore, this research contri…
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…
The earth observation industry provides satellite imagery with high spatial resolution and short revisit time. To allow efficient operational employment of these images, automating certain tasks has become necessary. In the defense domain, aircraft detection on satellite imagery is a valuable tool for analysts. Obtaining high performance detectors on such a task can only be achieved by leveraging deep learning and thus us-ing a large amount of labeled data. To obtain labels of a high enough quality, the knowledge of military experts is needed.We propose a hybrid clustering active learning meth…
Research Center For Modeling and Simulation (RCMS), NUST（国立科技大学建模与仿真研究中心）；RCMS, NUST（国立科技大学建模与仿真研究中心）
Object detection is one of the fundamental objectives in Applied Computer Vision. In some of the applications, object detection becomes very challenging such as in the case of satellite image processing. Satellite image processing has remained the focus of researchers in domains of Precision Agriculture, Climate Change, Disaster Management, etc. Therefore, object detection in satellite imagery is one of the most researched problems in this domain. This paper focuses on aircraft detection. in satellite imagery using deep learning techniques. In this paper, we used YOLO deep learning framework f…
Forest canopy height estimation from satellite RGB imagery using large-scale airborne LiDAR-derived training data and monocular depth estimation
Beijing Normal University（北京师范大学）；CSIRO Environment（英联邦科学与工业研究组织环境部）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Wuhan University（武汉大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）
There is an increasing demand for software that automatically detects and classifies mobile targets such as airplanes, cars, and ships in satellite imagery. Applications of such automated target recognition (ATR) software include economic forecasting, traffic planning, maritime law enforcement, and disaster response. This paper describes the extension of a convolutional neural network (CNN) for classification to a sliding window algorithm for detection. It is evaluated on mobile targets of the xView dataset, on which it achieves detection and classification accuracies higher than 95%.
RoughNet: Mapping Arctic Sea Ice Roughness Using Diffusion-Based Super-Resolution of Satellite Imagery
University College London（伦敦大学学院）；Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research（阿尔弗雷德·韦格纳极地与海洋研究所亥姆霍兹中心）；University of Victoria（维多利亚大学）
Detecting small objects over large areas remains a significant challenge in satellite imagery analytics. Among the challenges is the sheer number of pixels and geographical extent per image: a single DigitalGlobe satellite image encompasses over 64 km2 and over 250 million pixels. Another challenge is that objects of interest are often minuscule (~pixels in extent even for the highest resolution imagery), which complicates traditional computer vision techniques. To address these issues, we propose a pipeline (SIMRDWN) that evaluates satellite images of arbitrarily large size at native resoluti…
The Hong Kong Polytechnic University（香港理工大学）；TomTom Location Technology Germany GmbH（通腾定位科技德国有限公司）
Since the outbreak of COVID-19 policy makers have been relying upon non-pharmacological interventions to control the outbreak. With air pollution as a potential transmission vector there is need to include it in intervention strategies. We propose a U-net driven quantile regression model to predict $PM_{2.5}$ air pollution based on easily obtainable satellite imagery. We demonstrate that our approach can reconstruct $PM_{2.5}$ concentrations on ground-truth data and predict reasonable $PM_{2.5}$ values with their spatial distribution, even for locations where pollution data is unavailable. Suc…
SPyCer: Semi-Supervised Physics-Guided Contextual Attention for Near-Surface Air Temperature Estimation from Satellite Imagery
INSA CVL, Université d’Orléans, PRISME UR 4229（INSA CVL，奥尔良大学，PRISME UR 4229）；Université d’Orléans, INSA CVL, PRISME UR 4229（奥尔良大学，INSA CVL，PRISME UR 4229）；Université d’Orléans, CEDETE, UR 1210（奥尔良大学，CEDETE，UR 1210）
A Remote Approach to Cashew Orchard Detection: Leveraging Active Learning with Satellite Imagery in Guinea-Bissau
University of Porto（波尔图大学）；INESC-TEC（INESC-TEC机构）；University of Lisbon（里斯本大学）；CIBIO（CIBIO机构）；BIOPOLIS（BIOPOLIS机构）；InBIO Associated Laboratory（InBIO联合实验室）；CMUP（CMUP机构）
Chester F. Carlson Center for Imaging Science Rochester Institute of Technology（切斯特·F·卡尔森成像科学中心 罗切斯特理工学院）
Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on many computer vision tasks (e.g., object recognition, object detection, semantic segmentation) thanks to a large repository of annotated image data. Large labeled datasets for other sensor modalities, e.g., multispectral imagery (MSI), are not available due to the large cost and manpower required. In this paper, we adapt state-of-the-art DCNN frameworks in computer vision for semantic segmentation for MSI imagery. To overcome label scarcity for MSI data, we substitute real MSI for generated syn…
DFKI（德国人工智能研究中心）；TU Kaiserlautern（凯泽斯劳滕工业大学）；University of Oxford（牛津大学）；DLR（德国航空航天中心）；King’s College London（伦敦国王学院）；Czech Geological Survey（捷克地质调查局）；University of Warsaw（华沙大学）
Detecting and mapping informal settlements encompasses several of the United Nations sustainable development goals. This is because informal settlements are home to the most socially and economically vulnerable people on the planet. Thus, understanding where these settlements are is of paramount importance to both government and non-government organizations (NGOs), such as the United Nations Children's Fund (UNICEF), who can use this information to deliver effective social and economic aid. We propose two effective methods for detecting and mapping the locations of informal settlements. One us…
University of Maryland Baltimore County（马里兰大学巴尔的摩县分校）；Lehigh University（利哈伊大学）
For glaciologists, studying ice sheets from the polar regions is critical. With the advancement of deep learning techniques, we can now extract high-level information from the ice sheet data (e.g., estimating the ice layer thickness, predicting the ice accumulation for upcoming years, etc.). However, a vision-based conversational deep learning approach has not been explored yet, where scientists can get information by asking questions about images. In this paper, we have introduced the task of Visual Question Answering (VQA) on remote-sensed ice sheet imagery. To study, we have presented a uni…
Counting Trees from Satellite Imagery with Noisy Supervision
University of Copenhagen（哥本哈根大学）；University of Rwanda（卢旺达大学）；University of Chinese Academy of Sciences（中国科学院大学）；University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）；Stony Brook University（石溪大学）；LIGM, CNRS, Univ Gustave Eiffel, ENPC, IPP（LIGM，CNRS，古斯塔夫·埃菲尔大学，ENPC，IPP）
Change detection methods applied to monitoring key infrastructure like airport runways represent an important capability for disaster relief and urban planning. The present work identifies two generative adversarial networks (GAN) architectures that translate reversibly between plausible runway maps and satellite imagery. We illustrate the training capability using paired images (satellite-map) from the same point of view and using the Pix2Pix architecture or conditional GANs. In the absence of available pairs, we likewise show that CycleGAN architectures with four network heads (discriminator…
Detecting Brick Kiln Infrastructure at Scale: Graph, Foundation, and Remote Sensing Models for Satellite Imagery Data
Planetary Health Informatics (PHI) Lab, University of Oxford, Oxford, UK.（行星健康信息学实验室，牛津大学，英国）；Sustainable Environment (FUSE) Lab, University of Hong Kong, Hong Kong, China.（可持续环境（FUSE）实验室，香港大学，中国）；School of Computer and IT, Beaconhouse National University, Lahore, Pakistan（计算机与信息技术学院，贝肯豪斯国家大学，巴基斯坦）；Computer Science Department, Lahore University of Management Sciences, Lahore, Pakistan（管理科学大学计算机科学系，拉合尔，巴基斯坦）；Computer Science Department, University of Sarghoda, Sarghoda, Pakistan（计算机科学系，萨尔戈达大学，巴基斯坦）
Large datasets of sub-meter aerial imagery represented as orthophoto mosaics are widely available today, and these data sets may hold a great deal of untapped information. This imagery has a potential to locate several types of features; for example, forests, parking lots, airports, residential areas, or freeways in the imagery. However, the appearances of these things vary based on many things including the time that the image is captured, the sensor settings, processing done to rectify the image, and the geographical and cultural context of the region captured by the image. This thesis explo…
Puerto Rico suffered severe damage from the category 5 hurricane (Maria) in September 2017. Total monetary damages are estimated to be ~92 billion USD, the third most costly tropical cyclone in US history. The response to this damage has been tempered and slow moving, with recent estimates placing 45% of the population without power three months after the storm. Consequently, we developed a unique data-fusion mapping approach called the Urban Development Index (UDI) and new open source tool, Comet Time Series (CometTS), to analyze the recovery of electricity and infrastructure in Puerto Rico. …
Instance Segmentation XXL-CT Challenge of a Historic Airplane
Fraunhofer IIS（弗劳恩霍夫集成电路研究所）；Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering (IMTE)（弗劳恩霍夫个性化与基于细胞的医学工程研究所）；Institute of Medical Engineering, University of Lübeck（吕贝克大学医学工程研究所）
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…
Underwater automatic target recognition (UATR) has been a challenging research topic in ocean engineering. Although deep learning brings opportunities for target recognition on land and in the air, underwater target recognition techniques based on deep learning have lagged due to sensor performance and the size of trainable data. This letter proposed a framework for learning the visual representation of underwater acoustic imageries, which takes a transformer-based style transfer model as the main body. It could replace the low-level texture features of optical images with the visual features …
3D urban reconstruction of buildings from remotely sensed imagery has drawn significant attention during the past two decades. While aerial imagery and LiDAR provide higher resolution, satellite imagery is cheaper and more efficient to acquire for large scale need. However, the high, orbital altitude of satellite observation brings intrinsic challenges, like unpredictable atmospheric effect, multi view angles, significant radiometric differences due to the necessary multiple views, diverse land covers and urban structures in a scene, small base-height ratio or narrow field of view, all of whic…
ETH Zurich（苏黎世联邦理工学院）；Norwegian Institute of Bioeconomy Research（挪威生物经济研究所）；University of Zurich（苏黎世大学）
Monitoring and managing Earth's forests in an informed manner is an important requirement for addressing challenges like biodiversity loss and climate change. While traditional in situ or aerial campaigns for forest assessments provide accurate data for analysis at regional level, scaling them to entire countries and beyond with high temporal resolution is hardly possible. In this work, we propose a method based on deep ensembles that densely estimates forest structure variables at country-scale with 10-meter resolution, using freely available satellite imagery as input. Our method jointly tra…
Air Quality Prediction with A Meteorology-Guided Modality-Decoupled Spatio-Temporal Network
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Institution of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Huawei Cloud Enterprise Intelligence（华为云企业智能）
