Image Analysis and Data Fusion

The TC is comprised of 3 working groups dedicated to distinct fields within the scope of image analysis and data fusion, namely WG-MIA (Machine/Deep Learning for Image Analysis), WG-ISP (Image and Signal Processing), and WG-BEN (Benchmarking).

The TC maintains this site as a platform to share ideas and inform the community regarding the recent advances of image analysis and data fusion and distributes an e-mail newsletter to all committee members on a regular basis regarding recent advancements, datasets, and opportunities. The IADF TC activities include:

• Organization of a special session held annually during the IGARSS meeting, gathering cutting edge contributions covering various issues related to analysis and fusion of multi-modal and multi-temporal earth observation data via artificial intelligence, machine/deep learning, computer vision, and image/signal processing.
• Organization of the Data Fusion Contest, a scientific challenge held annually since 2006. The Contest is open not only to IEEE members but to everyone, with the goal of promoting innovation and benchmarking in analyzing multi-source big earth observation data.
• Organization of EarthVision, a workshop on large scale computer vision for remote sensing imagery held in conjunction with one of the major computer vision conferences (e.g., CVPR). The workshop aims to foster collaboration between the computer vision and earth observation communities and to advance automated interpretation of remotely sensed data.
• Operation of the GRSS Data and Algorithm Standard Evaluation (DASE) website. The website provides data sets and algorithm evaluation standards to support research, development, and testing of algorithms for remote sensing data analysis (e.g., machine/deep learning, image/signal processing).

If you are interested in receiving the newsletter, please join the IADF TC.

Organization

The IADF Technical Committee encourages participation from all its members. The committee organization includes the Chair, two Co-Chairs, and three working groups led by working group leads.

IADF Technical Committee Chair

Prof. Claudio Persello University of Twente The Netherlands

IADF Technical Committee Co-Chair

Dr. Gemine Vivone National Research Council Italy

Dr. Saurabh Prasad University of Houston USA

Working Group Leads

WG on Machine/Deep Learning for Image Analysis (WG-MIA)

WG-MIA Lead

Dr. Dalton Lunga Oak Ridge National Laboratory USA

WG-MIA Co-Lead

Dr. Ujjwal Verma Manipal Institute of Technology India

Dr. Silvia Ullo University of Sannio Italy

Dr. Ronny Hänsch German Aerospace Center (DLR) Germany

Prof. Danfeng Hong Chinese Academy of Sciences China

WG on Image and Signal Processing (WG-ISP)

WG-ISP Lead

Dr. Gülşen Taşkın Istanbul Technical University (VITO) Turkey

WG-ISP Co-Lead

Dr. Stefan Auer German Aerospace Center (DLR) Germany

Dr. Loic Landrieu LASTIG, IGN/ENSG, UGE, France

Dr. Sicong Liu Tongji University China

Dr. Lexie Yang Oak Ridge National Laboratory USA

WG on Benchmarking (WG-BEN)

WG-BEN Lead

Prof. Xian Sun Chinese Academy of Sciences China

WG-BEN Co-Lead

Seyed Ali Ahmadi K. N. Toosi University of Technology Iran

Srija Chakraborty NASA Postdoctoral Program Fellow, NASA GSFC, USRA USA

Dr. Francescopaolo Sica Universität der Bundeswehr München Germany

Dr. Yonghao Xu Linköping University Sweden

News

First IEEE GRSS & IADF School on Computer Vision for Earth Observation will take place Oct 3 -7, 2022 covering Image Fusion, Explainable AI for the Earth Science, Big Geo-Data, Multi-source Image Analysis, Deep Learning for Spectral Unmixing, SAR Image Analysis, Learning with Zero/Few Labels taught by experts! The school will include lectures, theoretical sessions, hands-on sessions and future livestreams. Find out more: IADF-School

The EarthVision 2023 workshop will take place at the next CVPR. We have awesome keynote speakers and a very challenging contest! Don't miss to see the latest advancements on ComputerVision and AI / ML for Remote Sensing and Earth Observation: www.grss-ieee.org/events/earthvision-2023/

 

 

TC Newsletter

The committee distributes an e-mail newsletter to all committee members on a monthly basis regarding recent advancements, datasets, and opportunities. If you are interested in receiving the newsletter, please join the TC. If you want to let us know about upcoming conference/workshop/journal deadlines, new datasets or challenges, or vacant positions in remote sensing and earth observation, we would highly appreciate your input.

EOD: The Earth Observation Database

EOD provides an interactive and searchable catalog of public benchmark datasets for remote sensing and earth observation with the aim to support researchers in the fields of geoscience, remote sensing, and machine learning.

IADF School

The IADF School focuses on applying CV/ML methods to address challenges in remote sensing and contains a series of lectures on the existing methods utilized for analyzing satellite images, along with the challenges encountered.

 

Workshops and Special / Invited Sessions

Current

• EarthVision 2023
• IGARSS 2023: "Data Fusion: The AI Era"
• BMVC 2023: “Machine Vision for Earth Observation”

Past

• ICLR 2023: Machine Learning for Remote Sensing
• EarthVision 2017, 2019, 2020, 2021, 2022
• IGARSS 2022: "Data Fusion: The AI Era"
• IGARSS 2021: The main IADF session "Data Fusion: The AI Era"
• IGARSS 2021: The DFC21 session "IEEE GRSS Data Fusion Contest"
• IGARSS 2021: "Machine Learning Datasets in Remote Sensing" (WG-BEN)
• IGARSS 2021: "Multi-resolution and Multimodal Remote Sensing Image Processing and Interpretation" (WG-ISP)

Contests and Challenges

Current

• The IADF Flood Mapathon calls people all over the world to contribute to completing a map of flood zones. The task focuses on mapping flood inundation areas and important ground features given satellite images. It supports manual mapping, automatic mapping using AI technologies, and their combinations.

• Cross-city multimodal semantic segmentation challenge

Past

• IEEE Data Fusion Contest
• 2020 Gaofen Challenge on Automated High-Resolution Earth Observation Image Interpretation
• IEEE GRSS IADF Photo Contest 2023

Special Issues / Streams

Current

• Special Stream on "Explainable Machine Learning for Remote Sensing" 
• Special Stream on "Machine Learning in Remote Sensing towards the Sustainable Development Goals"

Past

• Special Stream on "Fusion of Multimodal Remote Sensing Data for Analysis and Interpretation”
• Special Stream on "Advanced Processing for Multimodal Optical Remote Sensing Imagery”
• Special issue on “Benchmarking in Remote Sensing Data Science”
• Special Issue on “2020 Gaofen Challenge on Automated High-Resolution Earth Observation Image Interpretation”

GRSS Webinars

• Sep 1, 2020: GRSS Image Analysis and Data Fusion TC & Sample Activity: Benchmarking ML4RS
• Dec 8, 2020: Mapping urban deprivation and socio-economic inequalities using earth observation and deep learning
• October 26th, 2022: Scaling Geospatial Artificial Intelligence for Disaster Response

 

ML in RS Tutorial

Despite the wide application of machine learning to analyze remotely sensed data, the complexity of these methods often hinders to use them to their full potential. The aim of this tutorial is threefold: First, to provide insights into the algorithmic principles behind state-of-the-art machine learning approaches. Second, to illustrate the benefits and limitations of machine learning with practical examples. Third, to inspire new ideas by discussing unusual applications from remote sensing and other domains. Coming next at IGARSS23.

 

Data and Algorithm Standard Evaluation (DASE)

The GRSS Data and Algorithm Standard Evaluation (DASE) website provides data sets and algorithm evaluation standards to support research, development, and testing of algorithms for remote sensing data analysis (e.g., machine/deep learning, image/signal processing).

 

 

Working Groups

To encourage the active participation of all TC members, the IADF organization comprises, in addition to the 3 Technical Committee Co-Chairs, 3 working groups (WGs). These working groups focus on Machine/Deep Learning for Image Analysis (MIA), Image and Signal Processing (ISP), and Benchmarking (BEN). Each WG will address a specific topic, will provide input and feedback to the TC chairs, organize topic-related events (such as workshops, contests, tutorials, invited sessions, etc.). Please find the corresponding WG and their thematic scope below. If you feel that certain research or applicational areas are within the scope of IADF but not well represented, feel free to propose additional WGs.

WG on Machine/Deep Learning for Image Analysis (WG-MIA)

The WG-MIA fosters theoretical and practical advancements in Machine Learning and Deep Learning (ML/DL) for the analysis of geospatial and remotely sensed images. Under the umbrella of the IADF TC, WG-MIA serves as a global network that promotes the development of ML/DL techniques and their application in the context of various geospatial domains. It aims at connecting engineers, scientists, teachers, and practitioners, promoting scientific/technical advancements and geospatial applications. To promote the societal impact of ML-based solutions for the analysis of geospatial data, we seek accountability, transparency, and explainability. We encourage the development of ethical, understandable, and trustworthy techniques. Current Activities: Organization of invited sessions at international conferences and special issues in international journals.

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WG on Image and Signal Processing (WG-ISP)

The WG-ISP promotes advances in signal and image processing relying upon the use of remotely sensed data. It serves as a global, multi-disciplinary, network for both data fusion and image analysis supporting activities about several specific topics under the umbrella of the GRSS IADF TC. It aims at connecting people, supporting educational initiatives for both students and professionals, and promoting advances in signal processing for remotely sensed data. The WG-ISP oversees different topics, such as pansharpening, super-resolution, data fusion, segmentation/clustering, denoising, despeckling, image enhancement, image restoration, and many others. Current Activities: Organization of invited sessions at international conferences, special issues in international journals, and challenges and contests using remotely sensed data.

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WG on Benchmarking (WG-BEN)

Datasets have always been important in methodical remote sensing. They have always been used as a backbone for the development and evaluation of new algorithms. In today’s era of big data and deep learning, datasets have become even more important than before: Large, well-curated, and annotated datasets are of crucial importance for the training and validation of state-of-the-art models for information extraction from increasingly versatile multi-sensor remote sensing data. In addition, due to the increasing number of new methods being proposed by scientists and engineers, the possibility to compare these methods in a fair and transparent manner has become more and more important. The WG-BEN addresses these challenges and provides input with respect to evaluation methods, datasets, benchmarks, competitions, and tools for the creation of reference data. Furthermore, we contribute to evaluation sites and databases. Current Activities: Organization of Invited Session on IGARSS, contribution to an online database for datasets (DASE 2.0), showcasing of selected public datasets in the monthly IADF Newsletter.

Best regards,

IADF Chairs

For any information about past Data Fusion Contests, released data, and the related terms and conditions, please email iadf_chairs@grss-ieee.org.

The 2023 IEEE GRSS Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS), the Aerospace Information Research Institute under the Chinese Academy of Sciences, the Universität der Bundeswehr München, and GEOVIS Earth Technology Co., Ltd. aims to push current research on building extraction, classification, and 3D reconstruction towards urban reconstruction with fine-grained semantic information of roof types.

• Data
• Contest Results

2022 IEEE GRSS Data Fusion Contest

The semi-supervised learning challenge of the 2022 IEEE GRSS Data Fusion Contest aims to promote research in automatic land cover classification from only partially annotated training data consisting of VHR RGB imagery.

• Data
• Contest Results

2021 IEEE GRSS Data Fusion Contest

The 2021 IEEE GRSS Data Fusion Contest aimed to promote research on geospatial AI for social good. The global objective was to build models for understanding the state and changes of artificial and natural environments from multimodal and multitemporal remote sensing data towards sustainable developments. The 2021 Data Fusion Contest consisted of two challenge tracks: Detection of settlements without electricity and Multitemporal semantic change detection.

• Information on Track DSE
• Information on Track MSD
• Contest Results

2020 IEEE GRSS Data Fusion Contest

The 2020 Data Fusion Contest aimed to promote research in large-scale land cover mapping from globally available multimodal satellite data. The task was to train a machine learning model for global land cover mapping based on weakly annotated samples. The Contest consisted of two challenge tracks: Track 1: Landcover classification with low-resolution labels, and Track 2: Landcover classification with low- and high-resolution labels.

• Data
• Contest Results

2019 IEEE GRSS Data Fusion Contest

The 2019 Data Fusion Contest aimed to promote research in semantic 3D reconstruction and stereo using machine intelligence and deep learning applied to satellite images. The global objective was to reconstruct both a 3D geometric model and a segmentation of semantic classes for an urban scene. Incidental satellite images, airborne lidar data, and semantic labels were provided to the community.

• Data
• Contest Results

2018 IEEE GRSS Data Fusion Contest

The 2018 Data Fusion Contest aimed to promote progress on fusion and analysis methodologies for multi-source remote sensing data. It consisted of a classification benchmark, the task to be performed being urban land use and land cover classification. The following advanced multi-source optical remote sensing data are provided to the community: multispectral LiDAR point cloud data (intensity rasters and digital surface models), hyperspectral data, and very high-resolution RGB imagery.

• Data
• Contest Results

2017 IEEE GRSS Data Fusion Contest

The 2017 IEEE GRSS Data Fusion Contest focused on global land use mapping using open data. Participants were provided with remote sensing (Landsat and Sentinel2) data and vector layers (Open Street Map), as well as a 17 classes ground reference at 100 x 100m resolution over five cities worldwide (Local climate zones, see Stewart and Oke, 2012): Berlin, Hong Kong, Paris, Rome, Sao Paulo. The task was to provide land use maps over four other cities: Amsterdam, Chicago, Madrid, and Xi’an. The maps were to be uploaded on an evaluation server. Please refer to the links below to know more about the challenge, download the data and submit your results (even now that the contest is over).

• Data
• Contest Results

2016 IEEE GRSS Data Fusion Contest

The 2016 IEEE GRSS Data Fusion Contest, organized by the IADF TC, was opened on January 3, 2016. The submission deadline was April 29, 2016. Participants submitted open topic manuscripts using the VHR and video-from-space data released for the competition. 25 teams worldwide participated to the Contest. Evaluation and ranking were conducted by the Award Committee.

• Data
• Contest Results

Paper: Mou, L.; Zhu, X.; Vakalopoulou, M.; Karantzalos, K.; Paragios, N.; Le Saux, B.; Moser, G. & Tuia, D., Multi-temporal very high resolution from space: Outcome of the 2016 IEEE GRSS Data Fusion Contest, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., in press.

2015 IEEE GRSS Data Fusion Contest

The 2015 Contest was focused on multiresolution and multisensor fusion at extremely high spatial resolution. A 5-cm resolution color RGB orthophoto and a LiDAR dataset, for which both the raw 3D point cloud with a density of 65 pts/m² and a digital surface model with a point spacing of 10 cm, were distributed to the community. These data were collected using an airborne platform over the harbor and urban area of Zeebruges, Belgium. The department of Communication, Information, Systems, and Sensors of the Belgian Royal Military Academy acquired and provided the dataset. Participants were supposed to submit original IGARSS-style full papers using these data for the generation of either 2D or 3D thematic mapping products at extremely high spatial resolution.

• Data
• Contest Results

Paper: M. Campos-Taberner, A. Romero-Soriano, C. Gatta, G. Camps-Valls, A. Lagrange, B. Le Saux, A. Beaupère, A. Boulch, A. Chan-Hon-Tong, S. Herbin, H. Randrianarivo, M. Ferecatu, M. Shimoni, G. Moser, and D. Tuia. Processing of extremely high-resolution LiDAR and RGB data: Outcome of the 2015 IEEE GRSS Data Fusion Contest. Part A: 2D contest. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 9(12):5547–5559, 2016.

Paper: A.-V. Vo, L. Truong-Hong, D.F. Laefer, D. Tiede, S. d’Oleire Oltmanns, A. Baraldi, M. Shimoni, G. Moser, and D. Tuia. Processing of extremely high-resolution LiDAR and RGB data: Outcome of the 2015 IEEE GRSS Data Fusion Contest. Part B: 3D contest. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., 9(12):5560–5575, 2016.

2014 IEEE GRSS Data Fusion Contest

The 2014 Contest involved two datasets acquired at different spectral ranges and spatial resolutions: a coarser-resolution long-wave infrared (LWIR, thermal infrared) hyperspectral data set and fine-resolution data acquired in the visible (VIS) wavelength range. The former was acquired by an 84-channel imager covering the wavelengths between 7.8 to 11.5 μm with approximately 1-meter spatial resolution. The latter is a series of color images acquired during separate flight-lines with approximately 20-cm spatial resolution. The two data sources cover an urban area near Thetford Mines in Québec, Canada, and were acquired and were provided for the Contest by Telops Inc. (Canada). A ground truth with 7 landcover classes is provided and the mapping is performed at the higher of the two data resolutions.

• Data
• Classification Contest Results
• Paper Contest Results

Paper: W. Liao, X. Huang, F. Van Coillie, S. Gautama, A. Pizurica, W. Philips, H. Liu, T. Zhu, M. Shimoni, G. Moser, D. Tuia. Processing of Multiresolution Thermal Hyperspectral and Digital Color Data: Outcome of the 2014 IEEE GRSS DataFusion Contest. IEEE J. Sel. Topics Appl. Earth Observ. and Remote Sensing, 8(6): 2984-2996, 2015.

2013 IEEE GRSS Data Fusion Contest

The 2013 Contest involved two datasets, a hyperspectral image and a LiDAR-derived Digital Surface Model (DSM), both at the same spatial resolution (2.5m). The hyperspectral imagery has 144 spectral bands in the 380 nm to 1050 nm region. The dataset was acquired over the University of Houston campus and the neighboring urban area. A ground reference with 15 land use classes is available.

• Data
• Contest Rules
• Classification Contest Results
• Paper Contest Results

Paper: Debes, C.; Merentitis, A.; Heremans, R.; Hahn, J.; Frangiadakis, N.; van Kasteren, T.; Liao, W.; Bellens, R.; Pizurica, A.; Gautama, S.; Philips, W.; Prasad, S.; Du, Q.; Pacifici, F.: Hyperspectral and LiDAR Data Fusion: Outcome of the 2013 GRSS Data Fusion Contest. IEEE J. Sel. Topics Appl. Earth Observ. and Remote Sensing, 7 (6) pp. 2405-2418.

2012 IEEE GRSS Data Fusion Contest

The 2012 Contest was designed to investigate the potential of multi-modal/multi-temporal fusion of very high spatial resolution imagery in various remote sensing applications [6]. Three different types of data sets (optical, SAR, and LiDAR) over downtown San Francisco were made available by DigitalGlobe, Astrium Services, and the United States Geological Survey (USGS), including QuickBird, WorldView-2, TerraSAR-X, and LiDAR imagery. The image scenes covered a number of large buildings, skyscrapers, commercial and industrial structures, a mixture of community parks and private housing, and highways and bridges. Following the success of the multi-angular Data Fusion Contest in 2011, each participant was again required to submit a paper describing in detail the problem addressed, the method used, and final results generated for review.
Paper: Berger, C.; Voltersen, M.; Eckardt, R.; Eberle, J.; Heyer, T.; Salepci, N.; Hese, S.; Schmullius, C.; Tao, J.; Auer, S.; Bamler, R.; Ewald, K.; Gartley, M.; Jacobson, J.; Buswell, A.; Du, Q.; Pacifici, F., “Multi-Modal and Multi-Temporal Data Fusion: Outcome of the 2012 GRSS Data Fusion Contest”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.6, no.3, pp.1324-1340, June 2013.

2011 IEEE GRSS Data Fusion Contest

A set of WorldView-2 multi-angular images was provided by DigitalGlobe for the 2011 Contest. This unique set was composed of five Ortho Ready Standard multi-angular acquisitions, including both 16 bit panchromatic and multispectral 8-band images. The data were collected over Rio de Janeiro (Brazil) in January 2010 within a three-minute time frame with satellite elevation angles of 44.7°, 56.0°, and 81.4° in the forward direction, and 59.8° and 44.6° in the backward direction. Since there were a large variety of possible applications, each participant was allowed to decide a research topic to work on, exploring the most creative use of optical multi-angular information. At the end of the Contest, each participant was required to submit a paper describing in detail the problem addressed, the method used, and the final result generated. The papers submitted were automatically formatted to hide the names and affiliations of the authors to ensure neutrality and impartiality of the reviewing process.
Paper: F. Pacifici, Q. Du, “Foreword to the Special Issue on Optical Multiangular Data Exploitation and Outcome of the 2011 GRSS Data Fusion Contest”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 5, no. 1, pp.3-7, February 2012.

2009-2010 IEEE GRSS Data Fusion Contest

In 2009-2010, the aim of the contest was to perform change detection using multi-temporal and multi-modal data. Two pairs of data sets were available over Gloucester, UK, before and after a flood event. The data set contained SPOT and ERS images (before and after the disaster). The optical and SAR images were provided by CNES. Similar to previous years’ Contests, the ground truth used to assess the results was not provided to the participants. Each set of results was tested and ranked a first-time using the Kappa coefficient. The best five results were used to perform decision fusion with majority voting. Then, re-ranking was carried out after evaluating the level of improvement with respect to the fusion results.
Paper: N. Longbotham, F. Pacifici, T. Glenn, A. Zare, M. Volpi, D. Tuia, E. Christophe, J. Michel, J. Inglada, J. Chanussot, Q. Du “Multi-modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009-2010 Data Fusion Contest”, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 5, no. 1, pp. 331-342, February 2012.

2008 IEEE GRSS Data Fusion Contest

The 2008 Contest was dedicated to the classification of very high spatial resolution (1.3 m) hyperspectral imagery. The task was again to obtain a classification map as accurate as possible with respect to the unknown (to the participants) ground reference. The data set was collected by the Reflective Optics System Imaging Spectrometer (ROSIS-03) optical sensor with 115 bands covering the 0.43-0.86 μm spectral range.

• Data

Paper: G. Licciardi, F. Pacifici, D. Tuia, S. Prasad, T. West, F. Giacco, J. Inglada, E. Christophe, J. Chanussot, P. Gamba, “Decision fusion for the classification of hyperspectral data: outcome of the 2008 GRS-S data fusion contest”, IEEE Transactions on Geoscience and Remote Sensing, vol. 47, no. 11, pp. 3857-3865, November 2009.

2007 IEEE GRSS Data Fusion Contest

In 2007, the Contest theme was urban mapping using synthetic aperture radar (SAR) and optical data, and 9 ERS amplitude data sets and 2 Landsat multi-spectral images were made available. The task was to obtain a classification map as accurate as possible with respect to the unknown (to the participants) ground reference, depicting land cover and land use patterns for the urban area under study.

• Data

Paper: F. Pacifici, F. Del Frate, W. J. Emery, P. Gamba, J. Chanussot, “Urban mapping using coarse SAR and optical data: outcome of the 2007 GRS-S data fusion contest”, IEEE Geoscience and Remote Sensing Letters, vol. 5, no. 3, pp. 331-335, July 2008.

2006 IEEE GRSS Data Fusion Contest

The focus of the 2006 Contest was on the fusion of multispectral and panchromatic images [1]. Six simulated Pleiades images were provided by the French National Space Agency (CNES). Each data set included a very high spatial resolution panchromatic image (0.80 m resolution) and its corresponding multi-spectral image (3.2 m resolution). A high spatial resolution multi-spectral image was available as ground reference, which was used by the organizing committee for evaluation but not distributed to the participants.
Paper: L. Alparone, L. Wald, J. Chanussot, C. Thomas, P. Gamba, L. M. Bruce, “Comparison of pansharpening algorithms: Outcome of the 2006 GRS-S data fusion contest”, IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 10, pp. 3012–3021, Oct. 2007.

Contact

The IADF TC is open for a wide range of people with different expertise and background and working in different application areas. We are happy if you:

• Provide feedback, suggestions, or ideas for future activities
• Propose input for next newsletter
• Propose the next Data Fusion Contest
• Propose a new IADF Working Group

 

You can engage with us by contacting the Committee Chairs by email, follow us on Twitter, join the LinkedIn IEEE GRSS Data Fusion Discussion Forum, or join the IADF TC!