Omkar2118/datasets huggingface dataset:
multi-view-3DGPR
Full name: multi-view-3DGPR

If you want to known more about this dataset and new method, please read our paper [link](https://arxiv.org/abs/2308.04779).

If you find our paper or dataset useful, please give us a citation.


@article{zhou2023multi,
  title={Multi-View Fusion and Distillation for Subgrade Distresses Detection based on 3D-GPR},
  author={Zhou, Chunpeng and Ning, Kangjie and Wang, Haishuai and Yu, Zhi and Zhou, Sheng and Bu, Jiajun},
  journal={arXiv preprint arXiv:2308.04779},
  year={2023}
}






The dataset is available at now: 

Link：https://pan.baidu.com/s/14uZ6F0NbQxgwfaWTERQX2Q 
Passwd：2023

Introducing paper: Multi-View Fusion and Distillation for Subgrade Distresses Detection based on 3D-GPR
Weather
Full name: Max-Planck-Institut Weather Dataset for Long-term Time Series Forecasting

Weather is recorded every 10 minutes for the 2020 whole year, which contains 21 meteorological indicators, such as air temperature, humidity, etc. The dataset in CSV format can be downloaded at https://drive.google.com/file/d/1Tc7GeVN7DLEl-RAs-JVwG9yFMf--S8dy/view?usp=share_link.
Electricity Consuming Load
Full name: UCI Electricity Consuming Load

This data set contains electricity consumption of 370 points/clients.
SICKLE
Full name: Satellite Imagery for Cropping annotated with Keyparameter LabEls

The availability of well-curated datasets has driven the success of Machine Learning (ML) models. Despite greater access to earth observation data in agriculture, there is a scarcity of curated and labelled datasets, which limits the potential of its use in training ML models for remote sensing (RS) in agriculture. To this end, we introduce a first-of-its-kind dataset called SICKLE, which constitutes a time-series of multi-resolution imagery from 3 distinct satellites: Landsat-8, Sentinel-1 and Sentinel-2. Our dataset constitutes multi-spectral, thermal and microwave sensors during January 2018 - March 2021 period. We construct each temporal sequence by considering the cropping practices followed by farmers primarily engaged in paddy cultivation in the Cauvery Delta region of Tamil Nadu, India; and annotate the corresponding imagery with key cropping parameters at multiple resolutions (i.e. 3m, 10m and 30m). Our dataset comprises 2, 370 season-wise samples from 388 unique plots, having an average size of 0.38 acres, for classifying 21 crop types across 4 districts in the Delta, which amounts to approximately 209, 000 satellite images. Out of the 2, 370 samples, 351 paddy samples from 145 plots are annotated with multiple crop parameters; such as the variety of paddy, its growing season and productivity in terms of per-acre yields. Ours is also one among the first studies that consider the growing season activities pertinent to crop phenology (spans sowing, transplanting and harvesting dates) as parameters of interest. We benchmark SICKLE on three tasks: crop type, crop phenology (sowing, transplanting, harvesting), and yield prediction.

Introducing paper: SICKLE: A Multi-Sensor Satellite Imagery Dataset Annotated with Multiple Key Cropping Parameters
VGS
Full name: VideoGazeSpeech

Gaze following attracts much attention recently while existing databases commonly lack audio information. In this work, we collect the first gaze following dataset containing audios, the VideoGazeSpeech Dataset. The dataset is used to evaluate our method and also encourage future research in multi-modal gaze following. 
Our dataset comprises a total of $35,231$ frames of $29$ videos. Each video in the dataset has an average duration of approximately $20$ seconds and is recorded at a frame rate of $25$ frames per second (fps). The resolution of each video is $1280 \times 720$ pixels, and the entire dataset occupies a storage space of $7.2$ GB.