Abstract
Earth observation, aiming at monitoring the state of planet Earth using remote sensing data, is critical for improving our daily lives and living environment. With an increasing number of satellites in orbit, more and more datasets with diverse sensors and research domains are published to facilitate the research of the remote sensing community. In this paper, for the first time, we present a comprehensive review of more than 400 publicly published datasets, including applications like, land use/cover, change/disaster monitoring, scene understanding, agriculture, climate change and weather forecasting. We systemically analyze these Earth observation datasets from five aspects, including the volume, bibliometric analysis, research domains and the correlation between datasets. Based on the dataset attributes, we propose to measure, rank and select datasets to build a new benchmark for model evaluation. Furthermore, a new platform for Earth observation, termed EarthNets, is released towards a fair and consistent evaluation of deep learning methods on remote sensing data. EarthNets supports standard dataset libraries and cutting-edge deep learning models to bridge the gap between remote sensing and the machine learning community. Based on the EarthNets platform, extensive deep learning methods are evaluated on the new benchmark. The insightful results are beneficial to future research. The platform, dataset collections are publicly available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2210.04936
