Generating Physically-Consistent Satellite Imagery for Climate Visualizations
Massachusetts Institute of Technology（麻省理工学院）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；University of British Columbia（不列颠哥伦比亚大学）；Peaceful Society, Science and Innovation Foundation（和平协会科学与创新基金会）；University of Granada（格拉纳达大学）；Scripps Institute of Oceanography（斯克里普斯海洋研究所）；US Forest Service（美国林务局）；University of Oxford（牛津大学）；Riot Games（拳头游戏）；Pasteur Labs（巴斯德实验室）；Surgical Data Science Collective（外科数据科学集体）
Björn Lütjens, Brandon Leshchinskiy, Océane Boulais, Farrukh Chishtie, Natalia Díaz-Rodríguez, Margaux Masson-Forsythe, Ana Mata-Payerro, Christian Requena-Mesa… 展开作者
Björn Lütjens, Brandon Leshchinskiy, Océane Boulais, Farrukh Chishtie, Natalia Díaz-Rodríguez, Margaux Masson-Forsythe, Ana Mata-Payerro, Christian Requena-Mesa, Aruna Sankaranarayanan, Aaron Piña, Yarin Gal, Chedy Raïssi, Alexander Lavin, Dava Newman
备注：
arXiv admin note: text overlap with arXiv:2010.08103
Deep generative vision models are now able to synthesize realistic-looking satellite imagery. But, the possibility of hallucinations prevents their adoption for risk-sensitive applications, such as generating materials for communicating climate change. To demonstrate this issue, we train a generative adversarial network (pix2pixHD) to create synthetic satellite imagery of future flooding and reforestation events. We find that a pure deep learning-based model can generate photorealistic flood visualizations but hallucinates floods at locations that were not susceptible to flooding. To address this issue, we propose to condition and evaluate generative vision models on segmentation maps of physics-based flood models. We show that our physics-conditioned model outperforms the pure deep learning-based model and a handcrafted baseline. We evaluate the generalization capability of our method to different remote sensing data and different climate-related events (reforestation). We publish our code and dataset which includes the data for a third case study of melting Arctic sea ice and $>$30,000 labeled HD image triplets -- or the equivalent of 5.5 million images at 128x128 pixels -- for segmentation guided image-to-image translation in Earth observation. Code and data is available at \url{https://github.com/blutjens/eie-earth-public}.
Intuition Machines Inc（直觉机器公司）；Max Planck Institute for Evolutionary Anthropology（马克斯·普朗克进化人类学研究所）
Climate change is expected to reshuffle the settlement landscape: forcing people in affected areas to migrate, to change their lifeways, and continuing to affect demographic change throughout the world. Changes to the geographic distribution of population will have dramatic impacts on land use and land cover and thus constitute one of the major challenges of planning for climate change scenarios. In this paper, we explore a generative model framework for generating satellite imagery conditional on gridded population distributions. We make additions to the existing ALAE architecture, creating a…
Massachusetts Institute of Technology（麻省理工学院）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；Spatial Informatics Group（空间信息集团）；ENSTA Institut Polytechnique Paris（巴黎国立高等先进技术学院）；MIT Media Lab（麻省理工学院媒体实验室）；NASA Headquarters（美国国家航空航天局总部）；Latent Sciences（潜在科学公司）；University of Oxford（牛津大学）；Ubisoft（育碧）
As climate change increases the intensity of natural disasters, society needs better tools for adaptation. Floods, for example, are the most frequent natural disaster, but during hurricanes the area is largely covered by clouds and emergency managers must rely on nonintuitive flood visualizations for mission planning. To assist these emergency managers, we have created a deep learning pipeline that generates visual satellite images of current and future coastal flooding. We advanced a state-of-the-art GAN called pix2pixHD, such that it produces imagery that is physically-consistent with the ou…
Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；Descartes Labs, Inc（笛卡尔实验室公司）
In this work we demonstrate that generative adversarial networks (GANs) can be used to generate realistic pervasive changes in remote sensing imagery, even in an unpaired training setting. We investigate some transformation quality metrics based on deep embedding of the generated and real images which enable visualization and understanding of the training dynamics of the GAN, and may provide a useful measure in terms of quantifying how distinguishable the generated images are from real images. We also identify some artifacts introduced by the GAN in the generated images, which are likely to co…
Optical remote sensing imagery has been widely used in many fields due to its high resolution and stable geometric properties. However, remote sensing imagery is inevitably affected by climate, especially clouds. Removing the cloud in the high-resolution remote sensing satellite image is an indispensable pre-processing step before analyzing it. For the sake of large-scale training data, neural networks have been successful in many image processing tasks, but the use of neural networks to remove cloud in remote sensing imagery is still relatively small. We adopt generative adversarial network t…
Estimating forest carbon stocks from high-resolution remote sensing imagery by reducing domain shift with style transfer
School of Geography and Remote Sensing, Guangzhou University（广州大学地理与遥感学院）；Innovation Center for Remote Sensing Big Data Intelligent Applications, Guangzhou University（广州大学遥感大数据智能应用创新中心）
Mercedes-Benz Research and Development India Pvt. Ltd.（梅赛德斯-奔驰印度私人有限公司研发中心）；Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay（印度理工学院孟买分校资源工程研究中心）
Hyperspectral Imagery (and Remote Sensing in general) captured from UAVs or satellites are highly voluminous in nature due to the large spatial extent and wavelengths captured by them. Since analyzing these images requires a huge amount of computational time and power, various dimensionality reduction techniques have been used for feature reduction. Some popular techniques among these falter when applied to Hyperspectral Imagery due to the famed curse of dimensionality. In this paper, we propose a novel approach, LEt-SNE, which combines graph based algorithms like t-SNE and Laplacian Eigenmaps…
Institute of Data Science（数据科学研究所）；German Aerospace Center (DLR)（德国航空航天中心）；Max-Planck-Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；Friedrich-Schiller-Universität（弗里德里希-席勒大学）
Landscapes are meaningful ecological units that strongly depend on the environmental conditions. Such dependencies between landscapes and the environment have been noted since the beginning of Earth sciences and cast into conceptual models describing the interdependencies of climate, geology, vegetation and geomorphology. Here, we ask whether landscapes, as seen from space, can be statistically predicted from pertinent environmental conditions. To this end we adapted a deep learning generative model in order to establish the relationship between the environmental conditions and the view of lan…
This study presents a new visualization tool for classification of satellite imagery. Visualization of feature space allows exploration of patterns in the image data and insight into the classification process and related uncertainty. Visual Data Mining provides added value to image classifications as the user can be involved in the classification process providing increased confidence in and understanding of the results. In this study, we present a prototype visualization tool for visual data mining (VDM) of satellite imagery. The visualization tool is showcased in a classification study of h…
School of Geosciences, China University of Petroleum（中国石油大学地球科学学院）；College of Computer Science and Technology, China University of Petroleum（中国石油大学计算机科学与技术学院）
Tropical cyclones (TC) generally carry large amounts of water vapor and can cause large-scale extreme rainfall. Passive microwave rainfall (PMR) estimation of TC with high spatial and temporal resolution is crucial for disaster warning of TC, but remains a challenging problem due to the low temporal resolution of microwave sensors. This study attempts to solve this problem by directly forecasting PMR from satellite infrared (IR) images of TC. We develop a generative adversarial network (GAN) to convert IR images into PMR, and establish the mapping relationship between TC cloud-top bright tempe…
Synthetic Aperture Radar (SAR) imagery has diverse applications in land and marine surveillance. Unlike electro-optical (EO) systems, these systems are not affected by weather conditions and can be used in the day and night times. With the growing importance of SAR imagery, it would be desirable if models trained on widely available EO datasets can also be used for SAR images. In this work, we consider transfer learning to leverage deep features from a network trained on an EO ships dataset and generate predictions on SAR imagery. Furthermore, by exploring the network activations in the form o…
The present study is an attempt made to create a new tool for the analysis of Land Use Land Cover (LUCL) images in 3D visualization. This study mainly uses spatial data mining techniques on high resolution LULC satellite imagery. Visualization of feature space allows exploration of patterns in the image data and insight into the classification process and related uncertainty. Visual Data Mining provides added value to image classifications as the user can be involved in the classification process providing increased confidence in and understanding of the results. In this study, we present a pr…
DF4LCZ: A SAM-Empowered Data Fusion Framework for Scene-Level Local Climate Zone Classification
Future Network of Intelligence Institute (FNii), the Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）未来智能网络研究院（FNii））；School of Science and Engineering, the Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）
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）；Albert-Ludwigs-Universität Freiburg（弗赖堡大学）
The stability and ability of an ecosystem to withstand climate change is directly linked to its biodiversity. Dead trees are a key indicator of overall forest health, housing one-third of forest ecosystem biodiversity, and constitute 8%of the global carbon stocks. They are decomposed by several natural factors, e.g. climate, insects and fungi. Accurate detection and modeling of dead wood mass is paramount to understanding forest ecology, the carbon cycle and decomposers. We present a novel method to construct precise shape contours of dead trees from aerial photographs by combining established…
Renewable energy such as solar power is critical to fight the ever more serious climate change. China is the world leading installer of solar panel and numerous solar power plants were built. In this paper, we proposed a deep learning framework named SolarNet which is designed to perform semantic segmentation on large scale satellite imagery data to detect solar farms. SolarNet has successfully mapped 439 solar farms in China, covering near 2000 square kilometers, equivalent to the size of whole Shenzhen city or two and a half of New York city. To the best of our knowledge, it is the first tim…
Land use classification of low resolution spatial imagery is one of the most extensively researched fields in remote sensing. Despite significant advancements in satellite technology, high resolution imagery lacks global coverage and can be prohibitively expensive to procure for extended time periods. Accurately classifying land use change without high resolution imagery offers the potential to monitor vital aspects of global development agenda including climate smart agriculture, drought resistant crops, and sustainable land management. Utilizing a combination of capsule layers and long-short…
Zhejiang University（浙江大学）；University of Macau（澳门大学）
Displaying the large number of bands in a hyper- spectral image (HSI) on a trichromatic monitor is important for HSI processing and analysis system. The visualized image shall convey as much information as possible from the original HSI and meanwhile facilitate image interpretation. However, most existing methods display HSIs in false color, which contradicts with user experience and expectation. In this paper, we propose a visualization approach based on constrained manifold learning, whose goal is to learn a visualized image that not only preserves the manifold structure of the HSI but also …
The Alan Turing Institute（艾伦·图灵研究所）；University College London（伦敦大学学院）
Information on urban tree canopies is fundamental to mitigating climate change [1] as well as improving quality of life [2]. Urban tree planting initiatives face a lack of up-to-date data about the horizontal and vertical dimensions of the tree canopy in cities. We present a pipeline that utilizes LiDAR data as ground-truth and then trains a multi-task machine learning model to generate reliable estimates of tree cover and canopy height in urban areas using multi-source multi-spectral satellite imagery for the case study of Chicago.
SI Analytics；NVIDIA（英伟达）；National Institute of Meteorological Sciences (NIMS)（国家气象科学研究所）
Geostationary satellite imagery has applications in climate and weather forecasting, planning natural energy resources, and predicting extreme weather events. For precise and accurate prediction, higher spatial and temporal resolution of geostationary satellite imagery is important. Although recent geostationary satellite resolution has improved, the long-term analysis of climate applications is limited to using multiple satellites from the past to the present due to the different resolutions. To solve this problem, we proposed warp and refine network (WR-Net). WR-Net is divided into an optica…
Technical University of Munich（慕尼黑工业大学）；ETH Zurich（苏黎世联邦理工学院）；Massachusetts Institute of Technology（麻省理工学院）
Forest carbon offsets are increasingly popular and can play a significant role in financing climate mitigation, forest conservation, and reforestation. Measuring how much carbon is stored in forests is, however, still largely done via expensive, time-consuming, and sometimes unaccountable field measurements. To overcome these limitations, many verification bodies are leveraging machine learning (ML) algorithms to estimate forest carbon from satellite or aerial imagery. Aerial imagery allows for tree species or family classification, which improves the satellite imagery-based forest type classi…
ETH Zurich（苏黎世联邦理工学院）；University of Zurich（苏黎世大学）；Swiss Federal Institute of Aquatic Science and Technology（瑞士联邦水科学与技术研究所）
Depleting lake ice is a climate change indicator, just like sea-level rise or glacial retreat. Monitoring Lake Ice Phenology (LIP) is useful because long-term freezing and thawing patterns serve as sentinels to understand regional and global climate change. We report a study for the Oberengadin region of Switzerland, where several small- and medium-sized mountain lakes are located. We observe the LIP events, such as freeze-up, break-up and ice cover duration, across two decades (2000-2020) from optical satellite images. We analyse the time series of MODIS imagery by estimating spatially resolv…
Geographic variance in satellite imagery impacts the ability of machine learning models to generalise to new regions. In this paper, we model geographic generalisation in medium resolution Landsat-8 satellite imagery as a continuous domain adaptation problem, demonstrating how models generalise better with appropriate domain knowledge. We develop a dataset spatially distributed across the entire continental United States, providing macroscopic insight into the effects of geography on crop classification in multi-spectral and temporally distributed satellite imagery. Our method demonstrates imp…
Scattered trees outside of dense, closed-canopy forests are very important for carbon sequestration, supporting livelihoods, maintaining ecosystem integrity, and climate change adaptation and mitigation. In contrast to trees inside of closed-canopy forests, not much is known about the spatial extent and distribution of scattered trees at a global scale. Due to the cost of high-resolution satellite imagery, global monitoring systems rely on medium-resolution satellites to monitor land use. Here we present a globally consistent method to identify trees with canopy diameters greater than three me…
Urban trees help regulate temperature, reduce energy consumption, improve urban air quality, reduce wind speeds, and mitigating the urban heat island effect. Urban trees also play a key role in climate change mitigation and global warming by capturing and storing atmospheric carbon-dioxide which is the largest contributor to greenhouse gases. Automated tree detection and species classification using aerial imagery can be a powerful tool for sustainable forest and urban tree management. Hence, This study first offers a pipeline for generating labelled dataset of urban trees using Google Map's a…
SAMSelect: A Spectral Index Search for Marine Debris Visualization using Segment Anything
Laboratory of Geo-information Science and Remote Sensing, Wageningen University（地理信息科学与遥感实验室，瓦赫宁根大学）；Fundamental AI Lab at the University of Technology Nuremberg（技术大学纽伦堡的基础人工智能实验室）
本文提出 SAMSelect 算法，利用 Segment Anything Model 在小型标注数据上搜索最佳三通道波段或光谱指数组合，以增强 Sentinel-2 海洋垃圾目视解译，并在多地场景中验证其优于文献指数。
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…
AI-driven multi-source data fusion for algal bloom severity classification in small inland water bodies: Leveraging Sentinel-2, DEM, and NOAA climate data
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…
Netaji Subhas University of Technology（内塔吉·苏巴斯理工大学）；Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
The utility of aerial imagery (Satellite, Drones) has become an invaluable information source for cross-disciplinary applications, especially for crisis management. Most of the mapping and tracking efforts are manual which is resource-intensive and often lead to delivery delays. Deep Learning methods have boosted the capacity of relief efforts via recognition, detection, and are now being used for non-trivial applications. However the data commonly available is highly imbalanced (similar to other real-life applications) which severely hampers the neural network's capabilities, this reduces rob…
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…
University of Melbourne（墨尔本大学）；Monash University（莫纳什大学）；Cooperative Research Centre for Water Sensitive Cities（水敏感城市合作研究中心）；Arizona State University（亚利桑那州立大学）
Computer vision techniques enable automated detection of sky pixels in outdoor imagery. In urban climate, sky detection is an important first step in gathering information about urban morphology and sky view factors. However, obtaining accurate results remains challenging and becomes even more complex using imagery captured under a variety of lighting and weather conditions. To address this problem, we present a new sky pixel detection system demonstrated to produce accurate results using a wide range of outdoor imagery types. Images are processed using a selection of mean-shift segmentation, …
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 …
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…
School of Geosciences and Info Physics, Central South University（中南大学地球科学与信息物理学院）
Recently, deep convolutional neural network (DCNN) achieved increasingly remarkable success and rapidly developed in the field of natural image recognition. Compared with the natural image, the scale of remote sensing image is larger and the scene and the object it represents are more macroscopic. This study inquires whether remote sensing scene and natural scene recognitions differ and raises the following questions: What are the key factors in remote sensing scene recognition? Is the DCNN recognition mechanism centered on object recognition still applicable to the scenarios of remote sensing…
Methane Detection On Board Satellites from Unorthorectified Imagery
University of Oxford（牛津大学）；Delft University of Technology（代尔夫特理工大学）；Universitat de València（瓦伦西亚大学）；University of Surrey（萨里大学）；European Space Agency（欧洲空间局）
Indiana University（印第安纳大学）；University of Kansas（堪萨斯大学）；University of Colorado（科罗拉多大学）
Understanding the structure of Earth's polar ice sheets is important for modeling how global warming will impact polar ice and, in turn, the Earth's climate. Ground-penetrating radar is able to collect observations of the internal structure of snow and ice, but the process of manually labeling these observations is slow and laborious. Recent work has developed automatic techniques for finding the boundaries between the ice and the bedrock, but finding internal layers - the subtle boundaries that indicate where one year's ice accumulation ended and the next began - is much more challenging beca…
