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iSAID: Large-scale Dataset for Object Detection in Aerial Images(IIAI & Wuhan University, Dec 2019) 15 categories from plane to bridge, 188k instances, object instances and segmentation masks (MS COCO format), Google Earth & JL-1 image chips, Faster-RCNN baseline model (MXNet),devkit, Aca...
For this, we use the U-Net model of deep learning of image segmentation. The Kaggle dataset of the DSTL competition is used to segment them according to their classes and count their numbers. We measured the performance of models in terms of the Jaccard index, dice coefficient, accuracy, ...
The proposed approach is evaluated using a dataset of satellite images of water bodies available on Kaggle shown in Table 1, which comprises Sentinel-2 satellite imagery capturing various water bodies. Each image in this dataset is accompanied by a black-and-white mask, where black represents non...
Dataset The dataset used in this project contains 5631 images, divided into four classes: Cloudy, Desert, Green Area, and Water. Each class has approximately 1300 images. This data was sourced from Kaggle's Satellite Image Classification Dataset. Results Acknowledgments This project was developed as...
The Large-Scale Cloud image Dataset for Meteorology Research (LSCIDMR), made up of ten classes and thousands of high-resolution images, was used to evaluate the proposed SnapResNet152 model. Achieving an overall accuracy of 97.25%, the system outperformed some existing deep learning-based ...
In the experiments, the dataset is divided (on a random sampling basis) into training, validation as well as test sets with partitions of 7:2:1. (a) (b) (c) (d) Figure 4. An annotation example: Input image (a), building foot- prints (b,...
* [MagicBathyNet](https://www.magicbathy.eu/magicbathynet.html) -> a new multimodal benchmark dataset made up of image patches of Sentinel-2, SPOT-6 and aerial imagery, bathymetry in raster format and seabed classes annotations110
I believe there was a problem with this dataset, which led to many complaints that the competition was ruined. Kaggle - Draper - place images in order of time https://www.kaggle.com/c/draper-satellite-image-chronology/data Rating - hard. Not many useful kernels. ...
CNNs tile a fixed number of pixels and then pass a smaller 3D kernel over these tiles; the height and width of the kernel is the number of image rows and columns, as specified by the user, and the depth of the kernel is the number of spectral bands in the image. The neural network...