Imagery dataset for rooftop detection and classification

The dataset consists of 3617 GeoTIFF images, clipped by a buffer of 2 m around the roof outline with a mask around it, stored in four different folders by roof type: flat, gable, complex and bug. The bugs category includes all images which do not represent buildings, such as construction sites, unclear images, small parts of roofs or simply impossible to recognize with a human eye shapes.
The orthophoto used for preparing the dataset is in TIFF format. It was obtained in 2020 through aerial photography with an ultra-wide range digital camera (UltraCam Eаgle Mark 3). The orthophoto has the following characteristics:

• Height of flight above the terrain: 2850-3200 m;
• Longitudinal overlap: 60%;
• Transverse overlap: 30%;
• Aerial imaged area – 1961 sq. km, of which 1342 sq. km is the territory of the Metropolitan Municipality Sofia. For this project, the study area of district Lozents is 9.2 sq. km;
• Resolution: 10 cm/pix for the urban area;
• Bands: RGBA numberer of tiles (georeferenced JPG files): 39.

Other applications of the dataset, in addition to rooftop detection and classification, are as follows::

• roof recognition model, distinguishing roofs from other urban objects such as streets, trees, cars, etc.; 
• roof segmentation model;
• recognition and classification of roof elements (chimneys, skylights, dormers, terrace, antennas, solar panels etc.);
• recognition and classification of roof materials (tiles, metal, asphalt, wood, vinyl, etc.);
• roof solar potential analysis (the characteristics of the roof regarding the requirements for installation of solar panels).