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2024 Academic Track of the Free and Open Source Software for Geospatial Europe, FOSS4G 2024

作者： Montero, David Mahecha, Miguel D. Aybar, César Mosig, Clemens Wieneke, Sebastian Leipzig University Germany Institute of Earth System Science and Remote Sensing Leipzig University Germany Halle-Jena-Leipzig Germany Germany Image Processing Laboratory Universitat de València Spain

The Sentinel-2 (S2) mission from the European Space Agency’s Copernicus program provides essential data for Earth surface analysis. Its Level-2A products deliver high-to-medium resolution (10–60 m) surface reflectance (SR) data through the MultiSpectral Instrument (MSI). To enhance the accuracy and comparability of SR data, adjustments simulating a nadir viewing perspective are essential. These corrections address the anisotropic nature of SR and the variability in sun and observation angles, ensuring consistent image comparisons over time and under different conditions. The c-factor method, a simple yet effective algorithm, adjusts observed S2 SR by using the MODIS BRDF model to achieve Nadir BRDF Adjusted Reflectance (NBAR). Despite the straightforward application of the c-factor to individual images, a cohesive Python framework for its application across multiple S2 images and Earth System Data Cubes (ESDCs) from cloud-stored data has been lacking. Here we introduce sen2nbar, a Python package crafted to convert S2 SR data to NBAR, supporting both individual images and ESDCs derived from cloud-stored data. This package simplifies the conversion of S2 SR data to NBAR via a single function, organized into modules for efficient process management. By facilitating NBAR conversion for both SAFE files and ESDCs from SpatioTemporal Asset Catalogs (STAC), sen2nbar is developed as a flexible tool that can handle diverse data format requirements. We anticipate that sen2nbar will considerably contribute to the standardization and harmonization of S2 data, offering a robust solution for a diverse range of users across various applications. sen2nbar is an open-source tool available at https://***/ESDS-Leipzig/sen2nbar. © Author(s) 2024.

关键词： Python

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A SCIL-based Transformer for PolSAR image Classification

A SCIL-based Transformer for PolSAR Image Classification

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IET International Radar Conference 2023, IRC 2023

作者： Xin, Xinyue Li, Ming Wu, Yan Zhang, Peng Xu, Dazhi Zheng, Jia The National Laboratory of Radar Signal Processing School of Electronic Engineering Xidian University Xi'an China The Remote Sensing Image Processing and Fusion Group School of Electronic Engineering Xidian University Xi'an China

ISBN: (纸本)9781839539954

Transformer has been applied for polarimetric synthetic aperture radar (PolSAR) image processing due to its ability to construct long-range dependency. However, Transformer lacks the learning of local spatial information within samples and correlation information among samples, which limits its performance in practical tasks. To address these issues, this paper proposes a novel Transformer structure based on spatial information learning and correlation information learning (SCIL-based Transformer) for PolSAR image classification. Firstly, a global-local spatial information learning (GL-SIL) module is constructed to fully extract the global-local spatial information within samples. Secondly, a correlation information learning (CIL) module is designed to achieve the learning of correlation information among samples by modeling the dependency across samples. Compared to the self-attention network in Transformer, the above two modules not only add the learning of local spatial information within samples and the correlation information among samples, but also greatly reduce the network complexity. Experiments on two real PolSAR datasets have shown that the proposed SCIL-based Transformer has better performance than other state-of-the-art algorithms. © The Institution of Engineering & Technology 2023.

关键词： Contrastive Learning

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Geomatics for Assessment of Outdoor Thermal Comfort Calculations and Predictions in Urban Areas 4

Geomatics for Assessment of Outdoor Thermal Comfort Calculat...

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4th International Conference on Innovative Research in applied Science, Engineering and Technology, IRASET 2024

作者： Ahsissene, Safae Zahrae, Rhziel Fatima Raissouni, Naoufal Krüger, Eduardo Sobrino, Jose National School Of Applied Sciences Of Tetuan Abdelmalek Essadi University Remote Sensing Systems And Telecommunications Research Lab Tetuan Morocco Federal Technological University Of Paraná Curitiba Brazil University Of Valencia Imaging Processing Laboratory Valencia Spain

ISBN: (纸本)9798350309508

Urban areas commonly have higher temperatures than rural areas, which can cause thermal discomfort for residents. Understanding the factors that contribute to temperature variations is critical to improving thermal comfort in urban areas. This study uses four outdoor thermal comfort databases of different Köpen Geiger classifications from ten cities over a three-decade period and aims to establish a correlation between LST and air temperature. Discomfort Index (DI) is then calculated using the equation derived from this relationship. The DI takes into consideration both temperature and humidity, which are important factors in determining thermal discomfort. The calculated DI is then compared to the Thermal Sensation Vote (TSV) in the databases in order to validate the accuracy of the suggested DI. Thermal comfort maps are then created for different Moroccan cities. The maps can provide an indication of areas where thermal discomfort is more likely to occur, based on the calculated DI. This can be used by urban planners and policymakers to make informed decisions about urban design and planning. © 2024 IEEE.

关键词： Thermal comfort

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On-Demand Earth System Data Cubes

On-Demand Earth System Data Cubes

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： David Montero César Aybar Chaonan Ji Guido Kraemer Maximilian Söchting Khalil Teber Miguel D. Mahecha Remote Sensing Centre for Earth System Research (RSC4Earth) Leipzig University Institute of Earth System Science & Remote Sensing Leipzig University German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Image Processing Laboratory Universitat de València Image and Signal Processing Group Leipzig University Department of Remote Sensing Helmholtz Centre for Environmental Research (UFZ)

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Advancements in Earth system science have seen a surge in diverse datasets. Earth System Data Cubes (ESDCs) have been introduced to efficiently handle this influx of high-dimensional data. ESDCs offer a structured, intuitive framework for data analysis, organising information within spatio-temporal grids. The structured nature of ESDCs unlocks significant opportunities for Artificial Intelligence (AI) applications. By providing well-organised data, ESDCs are ideally suited for a wide range of sophisticated AI-driven tasks. An automated framework for creating AI-focused ESDCs with minimal user input could significantly accelerate the generation of task-specific training data. Here we introduce cubo, an open-source Python tool designed for easy generation of AI-focused ESDCs. Utilising collections in SpatioTemporal Asset Catalogs (STAC) that are stored as Cloud Optimised GeoTIFFs (COGs), cubo efficiently creates ESDCs, requiring only central coordinates, spatial resolution, edge size, and time range.

关键词： Earth Instruments Training data Geoscience and remote sensing Spatiotemporal phenomena Sensors Task analysis

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Mesogeos: a multi-purpose dataset for data-driven wildfire modeling in the Mediterranean 23

Mesogeos: a multi-purpose dataset for data-driven wildfire m...

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Proceedings of the 37th International Conference on Neural Information processing Systems

作者： Spyros Kondylatos Ioannis Prapas Gustau Camps-Valls Ioannis Papoutsis Orion Lab Institute for Astronomy Astrophysics Space Applications and Remote Sensing National Observatory of Athens and Image Processing Laboratory (IPL) Universitat de València Image Processing Laboratory (IPL) Universitat de València Orion Lab Institute for Astronomy Astrophysics Space Applications and Remote Sensing National Observatory of Athens

We introduce Mesogeos, a large-scale multi-purpose dataset for wildfire modeling in the Mediterranean. Mesogeos integrates variables representing wildfire drivers (meteorology, vegetation, human activity) and historical records of wildfire ignitions and burned areas for 17 years (2006-2022). It is designed as a cloud-friendly spatio-temporal dataset, namely a datacube, harmonizing all variables in a grid of 1km x 1km x 1-day resolution. The datacube structure offers opportunities to assess machine learning (ML) usage in various wildfire modeling tasks. We extract two ML-ready datasets that establish distinct tracks to demonstrate this potential: (1) short-term wildfire danger forecasting and (2) final burned area estimation given the point of ignition. We define appropriate metrics and baselines to evaluate the performance of models in each track. By publishing the datacube, along with the code to create the ML datasets and models, we encourage the community to foster the implementation of additional tracks for mitigating the increasing threat of wildfires in the Mediterranean.

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The Spafleximp Project: Spanish Flex-S3 Mission Calibration and Validation Plan Implementation

The Spafleximp Project: Spanish Flex-S3 Mission Calibration ...

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IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： Pedro J. Gómez-Giráldez Marcos Jiménez Ma Pilar Cendrero-Mateo Shari Van Wittenberghe Juan José Peón Adrián Moncholí-Estornell Jesús Delegido Jose F. Moreno Ricardo Díaz-Delgado Remote Sensing & GIS Lab. Doñana Biological Station (LAST-EBD) CSIC Sevilla Spain National Institute of Aerospace Technology (INTA) Madrid Spain Laboratory for Earth Observation Image Processing Laboratory University of Valencia Paterna Spain

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The European Space Agency's FLuorescence EXplorer-Sentinel 3 (ESA FLEX-S3) mission, scheduled for launch in 2026, aims to remotely detect vegetation fluorescence at 300x300-meter pixel resolution. The ESA requires a national Calibration and Validation (Cal/Val) plan for FLEX-S3 products that addresses the selection of test sites, measurement protocols, and uncertainty budgets. Despite Spain's significant Cal/Val test sites, it lacks a permanent instrumented site in international networks. The SpaFLEXImp initiative aims to implement a FLEX Cal/Val plan, standardizing protocols, and establishing a coordinated network of sites. Led by the National Institute of Aerospace Technology (INTA), the project involves the University of Valencia and the Doñana Biological Station-CSIC. With a long experience in in situ, airborne, and spaceborne measurements, the teams will conduct specific Cal/Val campaigns in 3 sites, making Spain a European and international reference for terrestrial Cal/Val activities.

关键词： Protocols Uncertainty Instruments European Space Agency Ecosystems Europe Vegetation mapping

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Camera localization for augmented reality and indoor positioning:a vision-based 3D feature database approach

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International Journal of Digital Earth 2020年第6期13卷 727-741页

作者： Jing Li Chengyi Wang Xuejie Kang Qiang Zhao Key Laboratory of Digital Earth Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingPeople’s Republic of China Laboratory of Remote Sensing Image Processing Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijingPeople’s Republic of China University of Chinese Academy of Sciences BeijingPeople’s Republic of China

The recent fast development in computer vision and mobile sensor technology such as mobile LiDAR and RGB-D cameras is pushing the boundary of the technology to suit the need of real-life applications in the fields of Augmented Reality(AR),robotics,indoor GIS and *** localization is often a key and enabling technology among these *** this paper,we developed a novel camera localization workflow based on a highly accurate 3D prior map optimized by our RGBD SLAM method in conjunction with a deep learning routine trained using consecutive video frames labeled with high precision camera ***,an AR registration method tightly coupled with a game engine is proposed,which incorporates the proposed localization algorithm and aligns the real Kinetic camera with a virtual camera of the game engine to facilitate AR application development in an integrated *** experimental results show that the localization accuracy can achieve an average error of 35 cm based on a fine-tuned prior 3D feature database at 3 cm accuracy compared against the ground-truth 3D LiDAR *** influence of the localization accuracy on the visual effect of AR overlay is also demonstrated and the alignment of the real and virtual camera streamlines the implementation of AR fire emergency response demo in a Virtual Geographic Environment.

关键词： Augmented reality(AR) virtual geographic environment(VGE) indoor positioning indoor GIS LiDAR 3D reconstruction localization camera pose

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A SCIL-based transformer for PolSAR image classification

A SCIL-based transformer for PolSAR image classification

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IET International Radar Conference (IRC 2023)

作者： Xinyue Xin Ming Li Yan Wu Peng Zhang Dazhi Xu Jia Zheng National Laboratory of Radar Signal Processing School of Electronic Engineering Xidian University Xi'an People's Republic of China Remote Sensing Image Processing and Fusion Group School of Electronic Engineering Xidian University Xi'an People's Republic of China

ISBN: (数字)9781837240982

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Geomatics for Assessment of Outdoor Thermal Comfort Calculations and Predictions in Urban Areas

Geomatics for Assessment of Outdoor Thermal Comfort Calculat...

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Innovative Research in applied Science, Engineering and Technology (IRASET),Conference on

作者： Safae Ahsissene Rhziel Fatima Zahrae Naoufal Raissouni Eduardo Krüger Jose Sobrino Remote Sensing Systems and Telecommunications research lab National school of applied sciences of Tetuan Abdelmalek Essadi University Tetuan Morocco Federal Technological University of Paraná Curitiba Brazil Imaging Processing Laboratory University of Valencia Valencia Spain

ISBN: (数字)9798350309508

ISBN: (纸本)9798350309515

关键词： Temperature sensors Correlation Temperature Databases Urban areas Land surface temperature Indexes

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TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting

TeleViT: Teleconnection-driven Transformers Improve Subseaso...

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International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Ioannis Prapas Nikolaos Ioannis Bountos Spyros Kondylatos Dimitrios Michail Gustau Camps-Valls Ioannis Papoutsis Orion Lab Institute for Astronomy Astrophysics Space Applications and Remote Sensing (IAASARS) National Observatory of Athens Image Processing Laboratory (IPL) Universitat de València Department of Informatics and Telematics Harokopio University of Athens

Wildfires are increasingly exacerbated as a result of climate change, necessitating advanced proactive measures for effective mitigation. It is important to forecast wildfires weeks and months in advance to plan forest fuel management, resource procurement and allocation. To achieve such accurate long-term forecasts at a global scale, it is crucial to employ models that account for the Earth system’s inherent spatio-temporal interactions, such as memory effects and teleconnections. We propose a teleconnection-driven vision transformer (TeleViT), capable of treating the Earth as one interconnected system, integrating fine-grained local-scale inputs with global-scale inputs, such as climate indices and coarse-grained global variables. Through comprehensive experimentation, we demonstrate the superiority of TeleViT in accurately predicting global burned area patterns for various forecasting windows, up to four months in advance. The gain is especially pronounced in larger forecasting windows, demonstrating the improved ability of deep learning models that exploit teleconnections to capture Earth system dynamics. Code available at ***/Orion-Ai-Lab/TeleViT.

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