Advanced Multi-Sensor Optical Remote Sensing for Urban Land Use and Land Cover Classification: Outcome of the 2018 IEEE GRSS Data Fusion Contest

By Yonghao Xu, Bo Du, Liangpei Zhang, D. Cerra, Miguel Pato, E. Carmona, Saurabh Prasad, Naoto Yokoya, R. Hänsch, Bertrand Le Saux. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019

308 citations recorded by the source corpus.

This paper presents the scientific outcomes of the 2018 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2018 Contest addressed the problem of urban observation and monitoring with advanced multi-source optical remote sensing (multispectral LiDAR, hyperspectral imaging, and very high-resolution imagery). The competition was based on urban land use and land cover classification, aiming to distinguish between very diverse and detailed classes of urban objects, materials, and vegetation. Besides data fusion, it also quantified the respective assets of the novel sensors used to collect the data. Participants proposed elaborate approaches rooted in remote-sensing, and also in machine learning and computer vision, to make the most of the available data. Winning approaches combine convolutional neural networks with subtle earth-observation data scientist expertise.

Subfields: Atmospheric Science, Media Technology

View Paper: https://www.semanticscholar.org/paper/bf7fee3272a65fb74dfa908a6d5d6276f89e044f

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Citations in the corpus

Deep Learning for Classification of Hyperspectral Data: A Comparative Review (2019, unknown): “This dataset is part of the 2018 Data Fusion Contest release, along with very-high-resolution imagery and multispectral LiDAR [12], [13].”
More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification (2020, unknown): “Lately, the winners in 2018 IEEE Data Fusion Contest (DFC) reported their SS results via a fused fully convolutional network (FCN) conducted on MS-LiDAR and HS data [41].”
Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox (2020, background): “IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE MONTH 2020 6 Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society (GRSS) and the University of Houston for the 2018 data fusion contest [133], [134].”
HeatPrompt: Zero-Shot Vision-Language Modeling of Urban Heat Demand from Satellite Images (2026, unknown): “However, converting pixel data into quantitative energy metrics continues to present significant challenges [4]–[6].”