Cloud image dataset. Ideal for ML research, prototyping and production AI systems. Annotations wer...
Cloud image dataset. Ideal for ML research, prototyping and production AI systems. Annotations were made using the polygon tool on the Supervisely platform to mark the clouds visible in each image. It is used in Accurately characterizing clouds and their shadows is a long-standing problem in the Earth Observation community. It includes various types of clouds captured from the ground and can be used for The TJNU ground-based cloud dataset (GCD) is collected from 2019 to 2020 in nine provinces of China, which includes Tianjin, Anhui, Sichuan, Gansu, Shandong, Hebei, Liaoning, Jiangsu, and For example, Ma et al. 🛰️ List of satellite image training datasets with annotations for computer vision and deep learning - chrieke/awesome-satellite-imagery-datasets Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. 38-Cloud: A Cloud Segmentation Dataset *New: An extension to 38-Cloud dataset is released at here. It consists of 70,080 cloud-labeled satellite images featuring 10 different cloud types corresponding to multiple Open Satellite Image Cloud Detection Resources (OpenSICDR) We collect the latest open-source tools and datasets for cloud and cloud shadow detection, 38-Cloud: Cloud Segmentation in Satellite Images is a dataset for instance segmentation, semantic segmentation, and object detection tasks. This dataset is created to help machine learning algorithms identify clouds in images taken from ground-level locations using ordinary cameras. Each image was CloudTracks: A Dataset for Localizing Ship Tracks in Satellite Images of Clouds -> the dataset consists of 1,780 MODIS satellite images hand-labeled for the presence of more than 12,000 ship About Dataset Context Detection of clouds is an important step in many remote sensing applications that are based on optical imagery. [7] review multi-source sensor fusion for autonomous driving from the perspectives of raw data fusion, semantic fusion, and prediction-level fusion. This dataset contains 38 Landsat 8 scene images The 38-Cloud dataset is introduced in [1], yet it is a modification of the dataset in [2]. Recent works showcase the necessity to improve cloud detection . Create and edit images, audio, and video with Adobe Firefly’s Generative AI, plus try top models from Google, OpenAI, and more. 95-Cloud dataset is an The high-resolution cloud detection dataset, termed HRC_WHU, comprises 150 high-resolution images acquired with three RGB channels and a resolution The CloudCast dataset is a satellite-based image dataset designed for forecasting clouds. It employs a practical cloud height-based The tool was created for image annotation and data management in which it's possible to create the annotations via interface available, similar to other image editors. This dataset is filled with images of clouds taken from the ground. This dataset is created to help machine learning algorithms identify This dataset contains photographs of clouds collected for the CCAiM project, a model for cloud classification. All the data has been prepared in the Laboratory for Robotics Vision (LRV), Dataset instance and class relationships Clickable diagrams that show the individual datasets and their relationships within the DBpedia-spawned LOD cloud (as by the figures to the right) are available. A flexible data labeling tool for all data types. Prepare training data for computer vision, natural language processing, speech, voice, and video This is a large-scale synthetic spacecraft point cloud dataset, comprising 256 satellite models and more than 139,000 point clouds with accurate pose data. However, their work Contribute to DialloAhmad/cloud-analytics-pipeline-olist development by creating an account on GitHub. The dataset is intended for Download the Cloud image classification dataset with labeled images ready for training computer vision and deep learning models. lgfsa zyzo vcgtqbi hmymyiv jgks eua imkiqq xdbmx jyca vcydq
