Abstract
This article presents the scientific outcomes of the 2020 Data Fusion Contest (DFC2020) organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2020 Contest addressed the problem of automatic global land-cover mapping with weak supervision, i.e., estimating high-resolution semantic maps while only low-resolution reference data are available during training. Two separate competitions were organized to assess two different scenarios: 1) high-resolution labels are not available at all; and 2) a small amount of high-resolution labels are available additionally to low-resolution reference data. In this article, we describe the DFC2020 dataset that remains available for further evaluation of corresponding approaches and report the results of the best-performing methods during the contest.
Highlights
H IGH-RESOLUTION global land-cover maps and their automatic updating allow us to understand the state and changes of the Earth’s surface, yielding fundamental information for tackling global challenges such as climate change, natural disasters, and environmental conservation
Fortyseven percent of the registrations were from China as similar to the previous editions, and students were the majority, indicating that DFC2020 was widely used for educational purposes
The similar number of submissions in each track illustrates that both scenarios, i.e., having no or only a small amount of high-resolution labels, are of similar interest to the research community
Summary
H IGH-RESOLUTION global land-cover maps and their automatic updating allow us to understand the state and changes of the Earth’s surface, yielding fundamental information for tackling global challenges such as climate change, natural disasters, and environmental conservation. Open satellite data, such as the ones provided by the Sentinel and Landsat missions, as well as small satellite constellations, have made it possible to obtain large-scale multimodal Earth observation data at high spatial and temporal resolutions covering the entire globe. Machine and deep learning methods are effective for large-scale automated mapping, the high cost of labeled training data collection is a barrier to high-resolution high-accuracy global mapping. The task of achieving high-resolution and accurate land-cover classification from such low-resolution and noisy labels is a fundamental challenge, which can potentially lead to a paradigm shift in global mapping and facilitate the use of Earth observation data for the sustainable development goals [1]
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