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

TLDR

The 2018 Data Fusion Contest, organized by the IEEE Geoscience and Remote Sensing Society, focused on urban land use and land cover classification to distinguish diverse urban objects, materials, and vegetation. Contest participants leveraged advanced multi‑source optical remote sensing—including multispectral LiDAR, hyperspectral imaging, and very high‑resolution imagery—alongside data fusion, sensor asset quantification, and machine‑learning and computer‑vision techniques to maximize the available data. Winning solutions combined convolutional neural networks with expert earth‑observation knowledge to achieve superior classification performance.

Abstract

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.