Omkar2118/datasets huggingface dataset:
CiNAT-Birds-2021
Full name: Cross-View iNaturalist Birds 2021

CiNAT Birds 2021 (Cross-View iNaturalist-2021 Birds) dataset contains ground-level images of bird species along with satellite images associated with the geolocation of the ground-level images. In total, there are 413,959 pairs for training and 14,831 pairs for validation and testing. The ground-level images are of varying sizes while the satellite images are of size 256x256. Additionally, the dataset comes with rich metadata for each image - geolocation, date, observer id, taxonomy.

Introducing paper: BirdSAT: Cross-View Contrastive Masked Autoencoders for Bird Species Classification and Mapping
Option Smile Volatility and Implied Probabilities Analysis
Full name: Option Smile Volatility and Implied Probabilities Analysis

This study’s sample consists of seven corporations (Black Rock, Google, Meta, JP Morgan, Walgreens, Netflix, and Pepsico) analyzed across seven quarters beginning in 2021. The data includes the implied volatility level (annualized) for the day before, the day of, and the day following the earnings report. This information was obtained from the Bloomberg Terminal dataset BVOL. The data we read from the terminal is based on Bloomberg’s algorithm for calculating the implied volatility for different strikes. The value is the same for both calls and puts, which makes comparisons and calculations more straightforward. The dataset contains a mixture of high-growth, high-risk technology corporations that saw strong market tailwinds during the previous year and steady, high-dividend-paying equities. For a more comprehensive conclusion, we analyze the implied volatility levels across three expirations to determine the influence of each expiration. The shortest maturity spans from 1 to 4 days, while the longest extends from 19 to 22 days. We got the announcement time directly from Bloomberg. The sample comprises blue-chip equities to ensure a highly liquid sample.
LNDST

The Landsat collection contains 400x400 RGB pictures captured by the Landsat 8 satellite. Each image might show areas of water and other areas that are just the background. Your task is to design a classifier that identifies each pixel as either 0 (background) or 1 (water), using integer values. For training, you'll receive the RGB images in jpg format along with a corresponding mask. In this mask, a '0' indicates the background, and a '1' indicates water. However, for testing, you'll only receive the RGB image without the mask.