The advances in remote sensing technologies have boosted applications for Earth observation. These technologies provide multiple observations or views with different levels of information. They might contain static or temporary views with different levels of resolution, in addition to having different types and amounts of noise due to sensor calibration or deterioration. A great variety of deep learning models have been applied to fuse the information from these multiple views, known as deep multi-view or multi-modal fusion learning. However, the approaches in the literature vary greatly since different terminology is used to refer to similar concepts or different illustrations are given to similar techniques. This article gathers works on multi-view fusion for Earth observation by focusing on the common practices and approaches used in the literature. We summarize and structure insights from several different publications concentrating on unifying points and ideas. In this manuscript, we provide a harmonized terminology while at the same time mentioning the various alternative terms that are used in literature. The topics covered by the works reviewed focus on supervised learning with the use of neural network models. We hope this review, with a long list of recent references, can support future research and lead to a unified advance in the area.

Multi-sensor data has become a foundation of Earth Observation (EO) research, offering models with enhanced accuracy via optimal fusion strategies. However, the unavailability of sensor data at the regional or country scale during inference can significantly undermine model performance. The literature explores diverse approaches to increasing model robustness to missing sensor scenarios, i.e., to reducing the decline in accuracy caused by missing data at inference time. Nevertheless, most of them have suboptimal behavior when a single-sensor is available for prediction. To address this challenge, we propose a novel method for multi-sensor modeling, Decision-level Sensor Dropout with mutual distillation (DSensD+). This employs a decision-level fusion, ignoring predictions from missing sensors and incorporating the Sensor Dropout (SensD) technique. Unlike works that use the SensD at the input or feature level, we use it at the decision level. Moreover, we include a mutual distillation strategy to improve the robustness. From a practical viewpoint, the additional components in the DSensD+ method are incorporated only for the training phase. During inference, it operates as a standard decision-level fusion model that ignores missing sensors.We validate our method on three EO datasets, spanning binary, multi-class, and multi-label classification tasks for crop- and tree-mapping related applications. Notably, DSensD+ outperforms several state-of-the-art methods, achieving consistent improvements across moderate (single-sensor missing) and extreme (single-sensor available) conditions, as well as with full-sensor data. These results demonstrate the robustness of DSensD+ and highlight the effectiveness of our method for the missing sensor problem, advancing the field of multi-sensor modeling in EO.