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In satellite imagery, clouds obstruct the ground information, directly impacting various downstream applications. Thus, cloud annotation/cloud detection serves as the initial preprocessing step in remote sensing image analysis. Recently, deep learning methods have significantly improved in the field of cloud detection, but training these methods necessitates abundant annotated data, which requires experts with professional domain knowledge. Moreover, the influx of remote sensing data from new satellites has further led to an increase in the cost of cloud annotation. To address the dependence on labeled datasets and professional domain knowledge, this paper proposes an automatic cloud annotation method for satellite remote sensing images, CloudAUE. Unlike traditional approaches, CloudAUE does not rely on labeled training datasets and can be operated by users without domain expertise. To handle the irregular shapes of clouds, CloudAUE firstly employs a convex hull algorithm for selecting cloud and non-cloud regions by polygons. When selecting convex hulls, the cloud region is first selected, and points at the edges of the cloud region are sequentially selected as polygon vertices to form a polygon that includes the cloud region. Then, the same selection is performed on non-cloud regions. Subsequently, the fast KD-Tree algorithm is used for pixel classification. Finally, an uncertainty method is proposed to evaluate the quality of annotation. When the confidence value of the image exceeds a preset threshold, the annotation process terminates and achieves satisfactory results. When the value falls below the threshold, the image needs to undergo a subsequent round of annotation. Through experiments on two labeled datasets, HRC and Landsat 8, CloudAUE demonstrates comparable or superior accuracy to deep learning algorithms, and requires only one to two annotations to obtain ideal results. An unlabeled self-built Google Earth dataset is utilized to validate the effectiveness and generalizability of CloudAUE. To show the extension capabilities in various fields, CloudAUE also achieves desirable results on a forest fire dataset. Finally, some suggestions are provided to improve annotation performance and reduce the number of annotations.

Parkinson’s disease (PD) is a well-known neurodegenerative disorder characterized by the degeneration of dopaminergic neurons, and oxidative stress and neuroinflammation are also associated with the pathogenesis of PD. Mitochonic acid 5 (MA-5), an analogue of indole-3-acetic acid, exerts key protective roles in inhibiting apoptosis, oxidative stress and neuroinflammation in multiple diseases. However, whether MA-5 can be beneficial for PD remains unclear. Hence, the aim of this study was to investigate the neuroprotective role of MA-5 in PD. In the current study, MPTP-challenged mice were treated as the in vivo model, and the effect of MA-5 on the motor function, neuronal survival, oxidative stress, neuroinflammation and the underlying mechanisms involved with AMPK and autophagy were determined. We revealed that MA-5 obviously up-regulated the phosphorylation of AMPK and promoted the autophagy (indicated by the increased LC3II/LC3I, parkin, pink and decreased p62) in substantia nigra (SN), ameliorated the motor deficits, up-regulated the expression of TH, suppressed the inflammation (indicated by the decreased protein levels of interleukin (IL)-1b, IL-6, tumour necrosis factor a) in SN in MPTP-induced mice. However, these patterns were reversed after the treatment of Compound C, an inhibitor of AMPK; also, after the application of CSA, an inhibitor of autophagy, MA-5 cannot play against the neurotoxicity of MPTP in mice. These combined results suggest that MA-5 can protect against MPTP-induced neurotoxicity to ameliorate the impaired motor function, which may be modulated via activation of AMPK-induced autophagy.