Human settlement extent (HSE) information is a valuable indicator of world-wide urbanization as well as the resulting human pressure on the natural environment. Therefore, mapping HSE is critical for various environmental issues at local, regional, and even global scales. This paper presents a deep-learning-based framework to automatically map HSE from multi-spectral Sentinel-2 data using regionally available geo-products as training labels. A straightforward, simple, yet effective fully convolutional network-based architecture, Sen2HSE, is implemented as an example for semantic segmentation within the framework. The framework is validated against both manually labelled checking points distributed evenly over the test areas, and the OpenStreetMap building layer. The HSE mapping results were extensively compared to several baseline products in order to thoroughly evaluate the effectiveness of the proposed HSE mapping framework. The HSE mapping power is consistently demonstrated over 10 representative areas across the world. We also present one regional-scale and one country-wide HSE mapping example from our framework to show the potential for upscaling. The results of this study contribute to the generalization of the applicability of CNN-based approaches for large-scale urban mapping to cases where no up-to-date and accurate ground truth is available, as well as the subsequent monitor of global urbanization.
European Commission, Joint Research Centre (JRC)（欧洲委员会联合研究中心）；Arhs Developments S.A（Arhs开发公司）；Engineering S.p.a（工程股份公司）
Spatially consistent and up-to-date maps of human settlements are crucial for addressing policies related to urbanization and sustainability, especially in the era of an increasingly urbanized world.The availability of open and free Sentinel-2 data of the Copernicus Earth Observation program offers a new opportunity for wall-to-wall mapping of human settlements at a global scale.This paper presents a deep-learning-based framework for a fully automated extraction of built-up areas at a spatial resolution of 10 m from a global composite of Sentinel-2 imagery.A multi-neuro modeling methodology bu…
The volume contains selected contributions from the Machine Learning Challenge "Discover the Mysteries of the Maya", presented at the Discovery Challenge Track of The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021). Remote sensing has greatly accelerated traditional archaeological landscape surveys in the forested regions of the ancient Maya. Typical exploration and discovery attempts, beside focusing on whole ancient cities, focus also on individual buildings and structures. Recently, there have been several successful a…
Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming. The spatio-temporal extent of lake ice cover, along with the timings of key phenological events such as freeze-up and break-up, provide important cues about the local and global climate. We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar (SAR) data with a deep neural network. In previous studies that used optical satellite imagery for lake ice monitoring, frequent cloud cover was a main limiting fact…
NOVA-LINCS；Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa（里斯本新大学科学与技术学院）
Mapping structures such as settlements, roads, individual houses and any other types of artificial structures is of great importance for the analysis of urban growth, masking, image alignment and, especially in the studied use case, the definition of Fuel Management Networks (FGC), which protect buildings from forest fires. Current cartography has a low generation frequency and their resolution may not be suitable for extracting small structures such as small settlements or roads, which may lack forest fire protection. In this paper, we use time series data, extracted from Sentinel-1 and 2 con…
Offshore oil and gas platform dynamics in the North Sea, Gulf of Mexico, and Persian Gulf: Exploiting the Sentinel-1 archive
German Remote Sensing Data Center, Earth Observation Center, EOC of the German Aerospace Center, DLR（德国遥感数据中心，地球观测中心，德国航空航天中心（DLR）地球观测中心）；Institute for Geography and Geology, Department of Remote Sensing, University of Würzburg（地理与地质研究所，遥感系，乌尔姆大学）
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…
Applications of digital agricultural services often require either farmers or their advisers to provide digital records of their field boundaries. Automatic extraction of field boundaries from satellite imagery would reduce the reliance on manual input of these records which is time consuming and error-prone, and would underpin the provision of remote products and services. The lack of current field boundary data sets seems to indicate low uptake of existing methods,presumably because of expensive image preprocessing requirements and local, often arbitrary, tuning. In this paper, we address th…
City University of Hong Kong（香港城市大学）；Wake Forest University（维克森林大学）；Dartmouth College（达特茅斯学院）
Artisanal and Small-scale Gold Mining (ASGM) is an important source of income for many households, but it can have large social and environmental effects, especially in rainforests of developing countries. The Sentinel-2 satellites collect multispectral images that can be used for the purpose of detecting changes in water extent and quality which indicates the locations of mining sites. This work focuses on the recognition of ASGM activities in Peruvian Amazon rainforests. We tested several semi-supervised classifiers based on Support Vector Machines (SVMs) to detect the changes of water bodie…
German Aerospace Center (DLR)（德国航空航天中心）；Google Switzerland（谷歌瑞士公司）；Google India（谷歌印度公司）；MindEarth
Human settlements are the cause and consequence of most environmental and societal changes on Earth; however, their location and extent is still under debate. We provide here a new 10m resolution (0.32 arc sec) global map of human settlements on Earth for the year 2015, namely the World Settlement Footprint 2015 (WSF2015). The raster dataset has been generated by means of an advanced classification system which, for the first time, jointly exploits open-and-free optical and radar satellite imagery. The WSF2015 has been validated against 900,000 samples labelled by crowdsourcing photointerpreta…
University of Helsinki（赫尔辛基大学）；Finnish Environment Institute（芬兰环境研究所）
Semantic segmentation by convolutional neural networks (CNN) has advanced the state of the art in pixel-level classification of remote sensing images. However, processing large images typically requires analyzing the image in small patches, and hence features that have large spatial extent still cause challenges in tasks such as cloud masking. To support a wider scale of spatial features while simultaneously reducing computational requirements for large satellite images, we propose an architecture of two cascaded CNN model components successively processing undersampled and full resolution ima…
Ecological Legacies of Pre-Columbian Settlements Evident in Palm Clusters of Neotropical Mountain Forests
Leiden Institute of Advanced Computer Science (LIACS), Leiden University（莱顿大学莱顿高级计算机科学研究所）；Computational Social Sciences and Humanities Laboratory, Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS)（巴塞罗那超级计算中心-国家超级计算中心计算社会科学与人文实验室）；Subdirección de Gestión del Patrimonio, Instituto Colombiano de Antropología e Historia（哥伦比亚人类学与历史研究所遗产管理副局）；Department of Humanities, CASEs group, Universitat Pompeu Fabra（庞培法布拉大学人文系CASEs研究组）；Facultad de Artes, Universidad Nacional de Colombia（哥伦比亚国立大学艺术学院）；Naturalis Biodiversity Center（荷兰自然生物多样性中心）；Division of Earth Observation and Geoinformatics (DIOTG), General Coordination of Earth Sciences (CG-CT), National Institute for Space Research (INPE)（巴西国家空间研究所地球科学总协调部地球观测与地理信息司）；Departmento de Antropología, Universidad del Magdalena（马格达莱纳大学人类学系）；ICREA（加泰罗尼亚研究及高级研究机构）；GAES, The University of the Witwatersrand（威特沃特斯兰德大学GAES）
Geographically-Weighted Weakly Supervised Bayesian High-Resolution Transformer for 200m Resolution Pan-Arctic Sea Ice Concentration Mapping and Uncertainty Estimation using Sentinel-1, RCM, and AMSR2 Data
Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary（地质工程系，Schulich 工程学院，卡尔加里大学）
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）
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…
Deep Learning for Slum Mapping in Remote Sensing Images: A Meta-analysis and Review
Centre of Excellence in Artificial Intelligence, Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校人工智能卓越中心）；Tata Consultancy Services Research（塔塔咨询服务公司研究院）
Leveraging AI multimodal geospatial foundation models for improved near-real-time flood mapping at a global scale
North Carolina State University（北卡罗来纳州立大学）；LabCorp（实验室集团）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Earth and Life Institute, UCLouvain（鲁汶天主教大学地球与生命研究所）；University of Helsinki（赫尔辛基大学）；Clark University（克拉克大学）；Thünen Institute of Farm Economics（蒂嫩农场经济研究所）
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 Perugia（佩鲁贾大学）；University of L’Aquila（拉奎拉大学）
Synthetic aperture radar (SAR) interferometry (InSAR) is performed using repeat-pass geometry. InSAR technique is used to estimate the topographic reconstruction of the earth surface. The main problem of the range-Doppler focusing technique is the nature of the two-dimensional SAR result, affected by the layover indetermination. In order to resolve this problem, a minimum of two sensor acquisitions, separated by a baseline and extended in the cross-slant-range, are needed. However, given its multi-temporal nature, these techniques are vulnerable to atmosphere and Earth environment parameters v…
