Omkar2118/datasets huggingface dataset:
How Do I Make a Flight Reservation with Qatar Airways?
Full name: How Do I Make a Flight Reservation with Qatar Airways?

To make a Qatar Airways reservation, you can book online through their website or mobile app, call their customer service phone number at +1=(888)+800~9117(US) or +44=(203)+900~0080(UK), or visit an airport ticket counter. For online bookings, visit qatarairways.com and select your trip type, departure and destination, dates, and passenger information. You can also call +1=(888)+800~9117(US) or +44=(203)+900~0080(UK) for assistance with your booking. 
Booking Options:
Online: Visit the Qatar Airways website or use the mobile app to search for flights, select your preferred itinerary, and complete the booking process. 
Phone: Call Qatar Airways customer service at +1=(888)+800~9117(US) or +44=(203)+900~0080(UK) for assistance with booking or any inquiries.
Turin3D

---
license: mit
language:
- en
tags:
- urbanMapping
- 3d
- pointCloud
- LiDAR
pretty_name: Turin3D
size_categories:
- 10M<n<100M
---
# Turin 3D Dataset

## Description

The Turin 3D dataset is a collection of LiDAR point cloud data acquired within the city of Turin, Italy on January 2022 and collected in LAS 1.4 format. It's designed for use in 3D semantic segmentation tasks.
This dataset offers a detailed 3D representation of the urban environment, enabling the development and evaluation of semantic segmentation models for urban scenes.

## Purpose

This dataset is intended for researchers and practitioners interested in studying 3D semantic segmentation of urban environments using LiDAR data. It can be used for:

* Development and evaluation of 3D semantic segmentation algorithms.
* Analysis and understanding of urban scenes.
* Applications in autonomous vehicles, robotics, and urban planning.

## Dataset Content
It contains almost 70M points divided among 57 blocks covering around 25k m^2 each. Data is compressed with gzip, you can extract it using
```
cat dataset.tar.*.gz.part > dataset.tar.gz
tar -xvzf dataset.tar.gz
```

### Class Taxonomy

The dataset utilizes a taxonomy of 6 semantic classes:

0.  Undefined
1.  Soil
2.  Terrain
3.  Vegetation
4.  Building
5.  Street element
6.  Water

### Dataset Splits

* **Train:** Provided with soft labels (class probabilities).
* **Validation:** Precise annotations (hard labels) for model evaluation.
* **Test:** Precise annotations (hard labels) for final model evaluation.


### Annotation

* **Train:** Annotations generated automatically using deep learning models, providing soft labels.
* **Validation and Test:** Manual annotations.


## Dataset Card Authors

Luca Barco, Giacomo Blanco, Gaetano Chiriaco, Fabrizio Dominici

## Dataset Card Contact

luca.barco@linksfoundation.com, giacomo.blanco@linksfoundation.com, gaetano.chiriaco@linksfoundation.com, fabrizio.dominici@linksfoundation.com

Introducing paper: Turin3D: Evaluating Adaptation Strategies under Label Scarcity in Urban LiDAR Segmentation with Semi-Supervised Techniques