We then present a large-scale scene classification dataset that contains one million aerial images termed Million-AID. Specifically, we first revisit aerial image interpretation by a literature review. In this paper, we address these issues in ASP, with perspectives from tile-level scene classification to pixel-wise semantic labeling. However, the former scheme often produces results with tile-wise boundaries, while the latter one needs to handle the complex modeling process from pixels to semantics, which often requires large-scale and well-annotated image samples with pixel-wise semantic labels. With the popularization of data-driven methods, the past decades have witnessed promising progress on ASP by approaching the problem with the schemes of tile-level scene classification or segmentation-based image analysis, when using high-resolution aerial images. Given an aerial image, aerial scene parsing (ASP) targets to interpret the semantic structure of the image content, e.g., by assigning a semantic label to every pixel of the image. The results show that by utilizing the proposed CSPT for task-aware model training, almost all downstream tasks in RSD can outperform the previous method of supervised pretraining-then-fine-tuning and even surpass the state-of-the-art (SOTA) performance without any expensive labeling consumption and careful model design. Finally, extensive experiments are carried out on twelve datasets in RSD involving three types of downstream tasks (e.g., scene classification, object detection and land cover classification) and two types of imaging data (e.g., optical and SAR). The proposed CSPT also can release the huge potential of unlabeled data for task-aware model training. Thus, in this paper, considering the self-supervised pretraining and powerful vision transformer (ViT) architecture, a concise and effective knowledge transfer learning strategy called ConSecutive PreTraining (CSPT) is proposed based on the idea of not stopping pretraining in natural language processing (NLP), which can gradually bridge the domain gap and transfer knowledge from the nature scene domain to the RSD. Moreover, pretraining models on large-scale nature scene datasets by supervised learning and then directly fine-tuning on diverse downstream tasks seems to be a crude method, which is easily affected by inevitable labeling noise, severe domain gaps and task-aware discrepancies. Unfortunately, due to different categories of imaging data and stiff challenges of data annotation, there is not a large enough and uniform remote sensing dataset to support large-scale pretraining in RSD. It has reached the status of consensus solution for task-aware model training in remote sensing domain (RSD). Ĭurrently, under supervised learning, a model pretrained by a large-scale nature scene dataset and then fine-tuned on a few specific task labeling data is the paradigm that has dominated the knowledge transfer learning. The platform, dataset collections are publicly available at. The insightful results are beneficial to future research. Based on the EarthNets platform, extensive deep learning methods are evaluated on the new benchmark. EarthNets supports standard dataset libraries and cutting-edge deep learning models to bridge the gap between remote sensing and the machine learning community. 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. Based on the dataset attributes, we propose to measure, rank and select datasets to build a new benchmark for model evaluation. We systemically analyze these Earth observation datasets from five aspects, including the volume, bibliometric analysis, research domains and the correlation between datasets. 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. 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. Earth observation, aiming at monitoring the state of planet Earth using remote sensing data, is critical for improving our daily lives and living environment.
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