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 ...
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 ...
On cloud labeling days at two institutes, 67 scientists screened 10,000 satellite images on a crowd-sourcing platform and classified almost 50,000 mesoscale cloud clusters. This dataset is then used as a training dataset for deep learning algorithms that make it possible to automate the pattern ...
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 of infrastructure and storage for you, as well as integration with outsourced annotators. ...
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 ...
In contrast, we cast the problem of cloud removal as a conditional image synthesis challenge, and we propose a trainable spatiotemporal generator network (STGAN) to remove clouds. We train our model on a new large-scale spatiotemporal dataset that we construct, containing 97640 image pairs ...
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...
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...
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 ...
cloud_optimized_geotif hereused in the 3D modelling notebookhere. Package of utilitiesto assist working with the SpaceNet dataset. For more Worldview imagery see Kaggle DSTL competition. Sentinel As part of theEU Copernicus program, multiple Sentinel satellites are capturing imagery -> seewikipedia....