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. 展开 关键词: MACHINE learning REMOTE sensing DEEP learning IMAGE ...
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 ...
After the cloud and snow dataset completes the learning of the shallow feature extraction module, it enters the L2 to L5 layers of the lightweight feature mapping attention network for deep feature learning. In these layers of modules, we changed the original two-layer 3×33×3 convolution bloc...
Agricultural Pattern Analysis, 21k aerial farmland images (RGB-NIR, USA, 2019 season, 512x512px chips), label masks for 6 field anomaly patterns (Cloud shadow, Double plant, Planter skip, Standing Water, Waterway and Weed cluster). Paper:Chiu et al. 2020 ...
digitalsreeni-image-annotator-> A python based GUI to annotate images and save annotations as COCO style JSON format. Cloud hosted tools & services Several open source tools are also available on the cloud, including CVAT, label-studio & Diffgram. In general cloud solutions will provide a lot ...
Cloud detection in high-resolution satellite images is a critical step for many remote sensing applications, but also a challenge, as such images have limited spectral bands. The contribution of this paper is twofold: We present a dataset called CloudPeru as well as a methodology for cloud ...
A dataset for predicting cloud cover over Europe Article Open access 27 February 2024 Mapping of 10-km daily diffuse solar radiation across China from reanalysis data and a Machine-Learning method Article Open access 11 July 2024 Exploring super-resolution spatial downscaling of several meteorolo...
Multiband processing template for the Add Rasters to Mosaic Dataset tool. This Multiband template will combine both surface reflectance bands and the QA band into one raster, allowing you to define and apply the cloud mask. I’ll start with importing modules and defining the Image Analyst ...
Criteria to select the imagery were: 1) less 20% cloud cover, 2) calm sea state (i.e. no white caps and low swell), and 3) where it was known that only one species would be present at the time of image acquisition. The percentage of cloud coverage was assessed by the satellite im...
The cloud structures are firstly identified and tracked in satellite remote sensed images, after which heterogeneous cloud features and properties are extracted and integrated to form a unified dataset. The C4.5 decision tree algorithm and dependency network analysis are then employed to discover useful...