Image Analysis and Data Fusion (IADF)
The Image Analysis and Data Fusion Technical Committee (IADF TC) of the Geoscience and Remote Sensing Society serves as a global, multi-disciplinary, network for geospatial image analysis (e.g., machine learning, deep learning, image and signal processing, and big data) and data fusion (e.g., multi-sensor, multi-scale, and multi-temporal data integration). It aims at connecting people and resources, educating students and professionals, and promoting theoretical advances and best practices in image analysis and data fusion.
- 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).
OrganizationThe 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
|Dr. Ronny Hänsch German Aerospace Center (DLR) Germany|
|Dr. Claudio Persello University of Twente The Netherlands|
|Dr. Gemine Vivone National Research Council Italy|
Working Group LeadsWG on Machine/Deep Learning for Image Analysis (WG-MIA) WG-MIA Lead
|Dr. Dalton Lunga Oak Ridge National Laboratory USA|
|Prof. Xian Sun Chinese Academy of Sciences China|
Ujjwal Verma Manipal Institute of Technology India
Saurabh Prasad University of Houston USA
Silvia Ullo University of Sannio Italy
|Gülşen Taşkın Istanbul Technical University (VITO) Turkey|
|Loic Landrieu LASTIG, IGN/ENSG, UGE, France|
|Zhou Zhang University of Wisconsin-Madison USA|
|Lexie Yang Oak Ridge National Laboratory USA|
|Prof. Michael Schmitt Munich University of Applied Sciences Germany|
|Seyed Ali Ahmadi K. N. Toosi University of Technology Iran|
Srija Chakraborty NASA Postdoctoral Program Fellow, NASA GSFC, USRA USA
Francescopaolo Sica German Aerospace Center Germany
Yonghao Xu Institute of Advanced Research in Artificial Intelligence (IARAI) Austria
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 EarthVision21 workshop will take place at the next CVPR. We already have awesome keynote speakers and challenging contests! Don't miss to submit your paper on ComputerVision and AI / ML for Remote Sensing and Earth Observation: grss-ieee.org/
It is not too late to join the IEEE GRSS Data Fusion Contest 2021: Geospatial AI for Social Good. Two tracks. Exciting tasks. Nice prizes! www.grss-ieee.
The IADF working group on Image and Signal Processing (ISP) opened the GRSL Special Stream “Fusion of Multimodal Remote Sensing Data for Analysis and Interpretation”. Editors: Yang Xu, Gemine Vivone, Wenzhi Liao, Ronny Hänsch. Submission: Feb. 1 - Apr. 30 www.classic.grss-ieee.org/specail-stream-on-fusion-of-multimodal-remote-sensing-...
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.
- Special issue on “Benchmarking in Remote Sensing Data Science”
- Special Issue on “2020 Gaofen Challenge on Automated High-Resolution Earth Observation Image Interpretation”
Special / Invited Sessions
- 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)
is a workshop on large-scale computer vision for remote sensing imagery held in conjunction with CVPR, one of the major computer vision conferences. The workshop aims to foster collaboration between the computer vision and earth observation communities and to advance automated interpretation of remotely sensed data.
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).
2020 Gaofen Challenge on Automated High-Resolution Earth Observation Image Interpretation
The 2020 Gaofen Challenge (en.sw.chreos.org/) is the most influential challenge on remote sensing in China and has been successfully held for four years with the support of China High-Resolution Major Scientific and Technological Projects. The challenge tracks including remote sensing image classification, object detection, semantic segmentation, etc. Thousands of remote sensing data have been published with sensors covering optics, SAR, multispectral, etc. The 2020 Gaofen Challenge is technically co-sponsored by IEEE GRSS and ISPRS. More than 700 teams from more than 20 countries (including China, Germany, France, Japan, Australia, Singapore, India, Sweden, etc.) have joined in this Challenge. The final phase was ended on 11th October, and the workshop was held on October 30, 2020.
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 IGARSS21.
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.
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.
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.
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.
Data Fusion Contest
The 2022 Data Fusion Contest was a great success and it's time to prepare for next year's contest. Following the last years, we would like to ask for proposals on the 2023 edition of the Data Fusion Contest.
If you are interested in co-organizing the contest, please send your proposal to email@example.com by August 14, 2022. A topic for the 2023 data fusion contest will be decided by the end of August. In your proposal, please clarify the following points.
- Novelty in comparison to the existing competitions and datasets
- Existence of data fusion aspects
Please clarify the following:
- Status and schedule: Are the data ready? Does it still need to be acquired? Are reference data available? Was any part of the data already published?
- Is the data under any kind of license?
- Will the data be available after the contest?
- Size of datasets: (tentative) number of images / scenes and (tentative) size in MB
- Example images
- Is there an opportunity to sponsor a prize for the top ranking teams (cash prize, cloud compute credits, etc.)
We are looking forward to receiving your proposals.
Ronny, Claudio, and Gemine
For any information about past Data Fusion Contests, released data, and the related terms and conditions, please email firstname.lastname@example.org.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
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 . 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.
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.
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 . 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.
Current membership (as of February 2021)
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