Aerial Survey

Aerial survey is a fundamental source of data for geospatial urban data generation and environmental analysis. According to the sensor mounted on the airplane and used for data acquisition, products with different geometric and spectral characteristics are generated for interdisciplinary applications.

A typical product are orthophotos which are created from many individual, overlapping and oriented aerial images with photogrammetric methods. The aerial images are rectified on a digital elevation model and combined into a homogenous mosaic by means of appropriate corrections and colour matching between them. Orthophotos describe the urban cover in two dimensions with very high spatial resolution, up to 2 cm. The demand for high-resolution has increased strongly in recent years. They facilitate work in administration, are integrated into GIS systems and many different follow-up products can be created, like cartographic maps. Aerial images can be also used for 3D surface modelling and the generation of very realistic 3D textured meshes.

Thermal imaging cameras measure the infrared radiation emitted by a surface and allow the creation of mosaics describing the object's temperature. Especially in the context of advancing global warming, thermography is becoming increasingly important in urban environments. With advancing climate change, the probability of longer hot days and periods is increasing. In this context, the urban environment heats up more strongly and so-called heat islands can form in the summer months. With the help of large-scale thermal scanner flights, such heat hot spots can be identified and the mitigation effect of water bodies can be observed. The knowledge gained from this provides a basis for planning further climate change adaptation strategies. In winter time, thermal flights can give indications on heat losses from buildings or underground infrastructures, and allow the monitoring of water temperature, for example industrial cooling water.

Hyperspectral aerial sensors enable the acquisition of images with hundreds of spectral bands in the visible and non-visible spectral ranges. By analysing the spectral signatures of the objects and applying machine learning algorithms, information is extracted for geological applications (i.e. mineral mapping and monitoring of polluted sites), urban green mapping (i.e. tree species, identification of invasive species, health status analysis and quantification of biophysical metrics), urban material classification (i.e. roof and ground materials, presence of pollutant like asbestos), water quality analysis and so on. A side product from the material classification, is represented by the urban ecological indices, that quantify properties like run-off, imperviousness, green cover, and so on, at pixel level but also at district level.