Hyperspectral (RGB + Thermal) drone images of Karlsruhe, Germany - Raw images for the Thermal Bridges on Building Rooftops (TBBR) dataset

Overview:

This repository contains the raw images for the dataset of Thermal Bridges on Building Rooftops (TBBR dataset).

This dataset contains 5696 drone images (2848 RGB and 2848 thermal) of building rooftops, recorded with a normal (RGB) and a FLIR-XT2 (thermal) camera on a DJI M600 drone. They show six large building blocks of around 20 buildings per block recorded in the city centre of the German city Karlsruhe east of the market square. Because of a high overlap rate of the images, the same buildings are on average recorded from different angles in different images about 20 times.

All images were recorded during a drone flight on March 19, 2019 from 7 a.m. to 8 a.m. At this time, temperatures were between 3.78 ° C and 4.97 ° C, humidity between 80% and 98%. There was no rain on the day of the flight, but there was 2.3mm/m² 48 hours beforehand. For recording the thermographic images an emissivity of 1.0 was set. The global radiation during this period was between 38.59 W / m² and 120.86 W / m². No direct sunlight can be seen visually on any of the recordings.

Usage:

Each zip archive file represents one of the six drone flight paths. The archives contain JPG files of size 4000x3000 pixels (RGB) and 640x512 (Thermal), separated into individual directories for RGB and Thermal:

├── Flug_100/
│   ├── RGB/
│   │   ├── DJI_0004.jpg
│   │   ├── DJI_0006.jpg
│   │   └── ...
│   └── Thermal/
│       ├── DJI_0003_R.JPG
│       ├── DJI_0005_R.JPG
│       └── ...
├── Flug_101/
│   ├── RGB/
│   │   ├── DJI_0001.jpg
│   │   ├── DJI_0003.jpg
│   │   └── ...
│   └── Thermal/
│       ├── DJI_0000_R.JPG
│       ├── DJI_0002_R.JPG
│       └── ...
└── ...

File Numbering/Naming Scheme:

The pairs of RGB + Thermal images follow the simple numbering scheme of: RGB = Thermal + 1.
For example, DJI_0003_R.jpg and DJI_0004.JPG are the matching Thermal and RGB images, respectively, that can be merged to form a single hyperspectral drone image.

To perform the merging, we recommend using the merge_image_layers.py script provided by the associated TBBRDet package (see the scripts/alignment/ directory).