10 research outputs found

    The outcome of the 2021 IEEE GRSS Data Fusion Contest - Track MSD:Multitemporal semantic change detection

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    We present here the scientific outcomes of the 2021 Data Fusion Contest (DFC2021) organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. DFC2021 was dedicated to research on geospatial artificial intelligence (AI) for social good with a global objective of modeling the state and changes of artificial and natural environments from multimodal and multitemporal remotely sensed data toward sustainable developments. DFC2021 included two challenge tracks: 'Detection of settlements without electricity' and 'Multitemporal semantic change detection.' This article mainly focuses on the outcome of the multitemporal semantic change detection track. We describe in this article the DFC2021 dataset that remains available for further evaluation of corresponding approaches and report the results of the best-performing methods during the contest

    Foreword to the Special Issue on Computer Vision-Based Approaches for Earth Observation

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    The five papers in this special section focus on computer vision-based approaches for Earth observation. These papers followed a series of events promoting works at the interface between computer vision and remote sensing: the special sessions organized at the Living Planet Symposium1 and the Computer Vision and Pattern Recognition (CVPR) conference (the EarthVision2 and Computer Vision for Global Challenges3 workshops). These sessions aimed at fostering collaboration between the computer vision and earth observation communities to boost automated interpretation of remotely sensed data. They also aimed at raising awareness inside the computer vision community for this highly challenging and quickly evolving field of research with a big impact on human society, economy, industry, and the planet. Submissions were invited from all areas of computer vision and image analysis relevant for, or applied to environmental remote sensing and were not limited to the papers presented at the events above. The papers retained in this special issue reflect the high variety of automatic image analysis in remote sensing

    Report on the 2022 IEEE Geoscience and Remote Sensing Society Data Fusion Contest: Semisupervised learning

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    The Image Analysis and Data Fusion (IADF) Technical Committee (TC) of the IEEE Geoscience and Remote Sensing Society (GRSS) has been organizing the annual Data Fusion Contest (DFC) since 2006. The contest promotes the development of methods for extracting geospatial information from large-scale, multisensor, multimodal, and multitemporal data. It aims to propose new problem settings that are challenging to address with existing techniques and to establish new benchmarks for scientific challenges in remote sensing image analysis [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19]

    Deep learning and earth observation to support the sustainable development goals: Current approaches, open challenges, and future opportunities

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    The synergistic combination of deep learning (DL) models and Earth observation (EO) promises significant advances to support the Sustainable Development Goals (SDGs). New developments and a plethora of applications are already changing the way humanity will face the challenges of our planet. This article reviews current DL approaches for EO data, along with their applications toward monitoring and achieving the SDGs most impacted by the rapid development of DL in EO. We systematically review case studies to achieve zero hunger, create sustainable cities, deliver tenure security, mitigate and adapt to climate change, and preserve biodiversity. Important societal, economic, and environmental implications are covered. Exciting times are coming when algorithms and Earth data can help in our endeavor to address the climate crisis and support more sustainable development

    Cross-City Semantic Segmentation (C2Seg) in Multimodal Remote Sensing: Outcome of the 2023 IEEE WHISPERS C2Seg Challenge

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    Given the ever-growing availability of remote sensing data (e.g., Gaofen in China, Sentinel in the EU, and Landsat in the USA), multimodal remote sensing techniques have been garnering increasing attention and have made extraordinary progress in various Earth observation (EO)-related tasks. The data acquired by different platforms can provide diverse and complementary information. The joint exploitation of multimodal remote sensing has been proven effective in improving the existing methods of land-use/land-cover segmentation in urban environments. To boost technical breakthroughs and accelerate the development of EO applications across cities and regions, one important task is to build novel cross-city semantic segmentation models based on modern artificial intelligence technologies and emerging multimodal remote sensing data. This leads to the development of better semantic segmentation models with high transferability among different cities and regions. The Cross-City Semantic Segmentation contest is organized in conjunction with the 13th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)

    Large-Scale Fine-Grained Building Classification and Height Estimation for Semantic Urban Reconstruction: Outcome of the 2023 IEEE GRSS Data Fusion Contest

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    This article presents the scientific outcomes of the 2023 Data Fusion Contest (DFC23) organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The contest consists of two tracks investigating the fusion of optical and synthetic aperture radar data for: 1) fine-grained roof type classification and 2) height estimation. During the development phase, 1000 people registered for the contest, while at the end 55 and 35 teams competed during the test phase in the two tracks, respectively. This article presents the methods and results obtained by the first and second-ranked teams of each track. In Track 1, both winning teams leveraged pretraining, modern network architectures, model ensembles, and measures to cope with the imbalanced class distribution. The solutions to Track 2 are more diverse and are characterized by modern multitask learning approaches. The data of this contest is openly available to the community for further research, development, and refinement of machine learning methods
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