Regular patterns of vegetation are considered widespread landscapes, although their global extent has never been estimated. Among them, spotted landscapes are of particular interest in the context of climate change. Indeed, regularly spaced vegetation spots in semi-arid shrublands result from extreme resource depletion and prefigure catastrophic shift of the ecosystem to a homogeneous desert, while termite mounds also producing spotted landscapes were shown to increase robustness to climate change. Yet, their identification at large scale calls for automatic methods, for instance using the popular deep learning framework, able to cope with a vast amount of remote sensing data, e.g., optical satellite imagery. In this paper, we tackle this problem and benchmark some state-of-the-art deep networks on several landscapes and geographical areas. Despite the promising results we obtained, we found that more research is needed to be able to map automatically these earth mounds from space.
RIKEN Center for Advance Intelligence Project（理化学研究所革新智能研究中心）；the University of Tokyo（东京大学）；Hiroshima University（广岛大学）；Chiba University（千叶大学）；Tokyo Institute of Technology（东京工业大学）；Tohoku University（东北大学）
Earth observation technologies, such as optical imaging and synthetic aperture radar (SAR), provide excellent means to monitor ever-growing urban environments continuously. Notably, in the case of large-scale disasters (e.g., tsunamis and earthquakes), in which a response is highly time-critical, images from both data modalities can complement each other to accurately convey the full damage condition in the disaster's aftermath. However, due to several factors, such as weather and satellite coverage, it is often uncertain which data modality will be the first available for rapid disaster respo…
Smart Transfer: Leveraging Vision Foundation Model for Rapid Building Damage Mapping with Post-Earthquake VHR Imagery
Department of Geography, National University of Singapore（新加坡国立大学地理系）；Urban Governance and Design Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）城市治理与设计学域）；School of Environment and Spatial Informatics, China University of Mining and Technology（中国矿业大学环境与空间信息学院）；Disaster Management Institute, Istanbul Technical University（伊斯坦布尔技术大学灾害管理研究所）；Department of Complexity Science and Engineering, University of Tokyo（东京大学复杂科学与工程系）；School of Automation, Southeast University（东南大学自动化学院）
JOANNEUM RESEARCH（约阿内姆研究公司）；Albert-Ludwigs-University Freiburg（弗赖堡阿尔伯特-路德维希大学）；University of Patras（帕特雷大学）
Purpose of review: This paper presents a review of the current state of the art in remote sensing based monitoring of forest disturbances and forest degradation from optical Earth Observation data. Part one comprises an overview of currently available optical remote sensing sensors, which can be used for forest disturbance and degradation mapping. Part two reviews the two main categories of existing approaches: classical image-to-image change detection and time series analysis. Recent findings: With the launch of the Sentinel-2a satellite and available Landsat imagery, time series analysis has…
Is sub-metre resolution necessary for cocoa mapping? A landscape-stratified evaluation of very high resolution imagery, decametric Earth Observation inputs, and operational products in Cote d'Ivoire
Sun Yat-sen University（中山大学）；UiT the Arctic University of Norway（挪威北极大学）；University of Cambridge（剑桥大学）
Global forest cover is critical to the provision of certain ecosystem services. With the advent of the google earth engine cloud platform, fine resolution global land cover mapping task could be accomplished in a matter of days instead of years. The amount of global forest cover (GFC) products has been steadily increasing in the last decades. However, it's hard for users to select suitable one due to great differences between these products, and the accuracy of these GFC products has not been verified on global scale. To provide guidelines for users and producers, it is urgent to produce a val…
Reducing methane emissions is essential for mitigating global warming. To attribute methane emissions to their sources, a comprehensive dataset of methane source infrastructure is necessary. Recent advancements with deep learning on remotely sensed imagery have the potential to identify the locations and characteristics of methane sources, but there is a substantial lack of publicly available data to enable machine learning researchers and practitioners to build automated mapping approaches. To help fill this gap, we construct a multi-sensor dataset called METER-ML containing 86,599 georeferen…
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences（中国科学院遥感与数字地球研究所）；Hainan Key Laboratory for Earth Observation（海南省地球观测重点实验室）；China University of Mining and Technology（中国矿业大学）
Heretofore, global burned area (BA) products are only available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery as well as the high-performance computing capacity of Google Earth Engine, we proposed an automated pipeline for generating 30-meter resolution global-scale annual bur…
Annual Earth-observation embeddings encode wildfire disturbance and support simplified burned area mapping
Conservation Research Institute, University of Cambridge（剑桥大学保护研究所）；Department of Plant Sciences, University of Cambridge（剑桥大学植物科学系）；dClimate Labs（dClimate实验室）；Department of Computer Science and Technology, University of Cambridge（剑桥大学计算机科学与技术系）；Department of Earth System Science, Stanford University（斯坦福大学地球系统科学系）
Nowadays, modern Earth Observation systems continuously collect massive amounts of satellite information. The unprecedented possibility to acquire high resolution Satellite Image Time Series (SITS) data (series of images with high revisit time period on the same geographical area) is opening new opportunities to monitor the different aspects of the Earth Surface but, at the same time, it is raising up new challenges in term of suitable methods to analyze and exploit such huge amount of rich and complex image data. One of the main task associated to SITS data analysis is related to land cover m…
EvaNet: Elevation-Guided Flood Extent Mapping on Earth Imagery (Extended Version)
Indiana University Bloomington（印第安纳大学伯明顿分校）；The University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；University of Florida（佛罗里达大学）；St. Cloud State University（圣克劳德州立大学）；Auburn University（奥本大学）
SHRUG-FM: Reliability-Aware Foundation Models for Earth Observation
Universitat de València（瓦伦西亚大学）；Wageningen University & Research（瓦赫宁根大学与研究所）；Delft University of Technology（代尔夫特理工大学）；European Space Agency（欧洲航天局）；Heidelberg University（海德堡大学）；Ghent University（根特大学）；University of Oxford（牛津大学）；Google Research（谷歌研究）
Deep convolutional neural networks (CNNs) have outperformed existing object recognition and detection algorithms. On the other hand satellite imagery captures scenes that are diverse. This paper describes a deep learning approach that analyzes a geo referenced satellite image and efficiently detects built structures in it. A Fully Convolution Network (FCN) is trained on low resolution Google earth satellite imagery in order to achieve end result. The detected built communities are then correlated with the vaccination activity that has furnished some useful statistics.
DUNIA: Pixel-Sized Embeddings via Cross-Modal Alignment for Earth Observation Applications
Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, France；Department for AI in Society, Science；Technology, Zuse Institute Berlin, Germany；Department of Information Systems, University of Münster, Germany；Department of Computer Science, CNRS, INRIA \& École Normale Supérieure, Paris 75230, France
提出 DUNIA 方法，通过图像与全波形 LiDAR 跨模态对比对齐学习像素级嵌入，在零样本和微调设置下的多项地球观测环境监测任务中表现优于专门监督模型并接近或超越 SOTA。
Anna University（安娜大学）；Akshya College Of Engineering And Technology（阿克沙亚工程与技术学院）
Maps are used to describe far-off places . It is an aid for navigation and military strategies. Mapping of the lands are important and the mapping work is based on (i). Natural resource management & development (ii). Information technology ,(iii). Environmental development ,(iv). Facility management and (v). e-governance. The Landuse / Landcover system espoused by almost all Organisations and scientists, engineers and remote sensing community who are involved in mapping of earth surface features, is a system which is derived from the united States Geological Survey (USGS) LULC classification s…
Instituto de Astrofísica de Canarias (IAC)（加那利群岛天体物理研究所）；Universidad de La Laguna（拉古纳大学）
Finding potential life harboring exo-Earths is one of the aims of exoplanetary science. Detecting signatures of life in exoplanets will likely first be accomplished by determining the bulk composition of the planetary atmosphere via reflected/transmitted spectroscopy. However, a complete understanding of the habitability conditions will surely require mapping the presence of liquid water, continents and/or clouds. Spin-orbit tomography is a technique that allows us to obtain maps of the surface of exoplanets around other stars using the light scattered by the planetary surface. We leverage the…
Lancaster University（兰卡斯特大学）；European Space Agency（欧洲空间局）
Earth observation is fundamental for a range of human activities including flood response as it offers vital information to decision makers. Semantic segmentation plays a key role in mapping the raw hyper-spectral data coming from the satellites into a human understandable form assigning class labels to each pixel. In this paper, we introduce a prototype-based interpretable deep semantic segmentation (IDSS) method, which is highly accurate as well as interpretable. Its parameters are in orders of magnitude less than the number of parameters used by deep networks such as U-Net and are clearly i…
Better Planet Laboratory, University of Colorado Boulder（科罗拉多大学博尔德分校更好星球实验室）；University of Colorado Boulder（科罗拉多大学博尔德分校）；Mortenson Center for Global Engineering and Resilience, University of Colorado Boulder（科罗拉多大学博尔德分校莫滕森全球工程与复原力中心）
University of Florence（佛罗伦萨大学）；Centre Tecnològic Telecomunicacions Catalunya（加泰罗尼亚电信技术中心）；University of Padua（帕多瓦大学）
In this letter, we use deep-learning convolution neural networks (CNNs) to assess the landslide mapping and classification performances on optical images (from Sentinel-2) and SAR images (from Sentinel-1). The training and test zones used to independently evaluate the performance of the CNNs on different datasets are located in the eastern Iburi subprefecture in Hokkaido, where, at 03.08 local time (JST) on September 6, 2018, an Mw 6.6 earthquake triggered about 8000 coseismic landslides. We analyzed the conditions before and after the earthquake exploiting multi-polarization SAR as well as op…
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 …
Earth Observation Center, German Aerospace Center (DLR)（德国航空航天中心地球观测中心）；School of Engineering and Design, Technical University of Munich（慕尼黑工业大学工程与设计学院）；Bristol Glaciology Centre, University of Bristol（布里斯托尔大学布里斯托尔冰川学中心）；Department of Water and Climate, Vrije Universiteit Brussel（布鲁塞尔自由大学水与气候系）；Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich（苏黎世联邦理工学院水力、水力学与冰川学实验室）；Laboratoire de Glaciologie, Université Libre de Bruxelles（布鲁塞尔自由大学冰川学实验室）
University of Kaiserslautern-Landau（凯泽斯劳滕-兰道大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；University of Yamanashi（山梨大学）
Climate change has increased the severity and frequency of weather disasters all around the world. Flood inundation mapping based on earth observation data can help in this context, by providing cheap and accurate maps depicting the area affected by a flood event to emergency-relief units in near-real-time. Building upon the recent development of the Sen1Floods11 dataset, which provides a limited amount of hand-labeled high-quality training data, this paper evaluates the potential of five traditional machine learning approaches such as gradient boosted decision trees, support vector machines o…
Uncertainty-Aware Hourly Air Temperature Mapping at 2 km Resolution via Physics-Guided Deep Learning
Spatial Sciences Institute（空间科学研究所）；Dornsife College of Letters, Arts and Sciences（文理学院）；University of Southern California（南加州大学）；Division of Biostatistics, Department of Population and Public Health Sciences（流行病学与公共卫生科学系生物统计学部）；Keck School of Medicine（凯克医学院）
With the development of Earth observation technology, very-high-resolution (VHR) image has become an important data source of change detection. Nowadays, deep learning methods have achieved conspicuous performance in the change detection of VHR images. Nonetheless, most of the existing change detection models based on deep learning require annotated training samples. In this paper, a novel unsupervised model called kernel principal component analysis (KPCA) convolution is proposed for extracting representative features from multi-temporal VHR images. Based on the KPCA convolution, an unsupervi…
