LCA stands for Land Cover Analysis and is used to provide an up to date, digital overview of what covers the surface of our earth split out into 10 key land cover classes. The LCA is often the first step in many environmental monitoring or site selection processes.

The complexity of modern urban environments has led to the introduction of 3D Land Information Systems (LISs), which tend to replace traditional 2D LIS architectures for the purposes of urban planning and regeneration, land administration, real estate management and civil development. Both the need for 3D visualization of the geometry of buildings in various time instances through the years and the need for acquisition of 3D models in various levels of detail (LoDs), which not only fulfill the requirements of the various users but also they speed up the visualization process, are obvious. Thus, additional dimensions, that is, for time and scale, need to be supported by a modern LIS. This service introduces a 5D modelling pipeline that may be adopted by a multi-purpose LIS for the selective creation of 3D models of an urban area in various time instances and at various LoDs, enriched with cadastral and other spatial data. The methodology is based on automatic change detection algorithms for spatial-temporal analysis of the spatial changes that took place in subsequent time periods, using image orientation, dense image matching and structure from motion algorithms, the procedure requires photogrammetric stereo plotting, implements procedural modelling and relies on the availability of overlapping aerial and terrestrial imagery, ground control points and cadastral information.

With AutoMap, the handling of satellite images has never been easier! We simplify the production of maps based on earth observation data and handle the application of machine learning algorithms for land surface classifications by providing a solid data processing pipeline in a fully managed cloud infrastructure. Bring your own samples or use our default solution, while we take care of the thousands of images that fit your request, all the clouds in your area of interest and every aspect of process optimization and secure data storage. Don't worry about machine learning concepts, intelligent training data collection, hyperparameter optimization or informative feature spaces - That's our job as well! Seeing the world from above is now as demanding as ordering a pizza!

NEO monitors solar panels throughout the Netherlands using deep learning methods. Solar panels are detected using various resolutions of satellite and aerial imagery. The panels are digitalized by the algorithm and related to 3D-roofs based on the AHN (Dutch national height model) to calculate zenith and azimuth angles. This data, combined with data such as solar power, allows NEO to estimate the power output for each object. Solar panels are also provided with parcel and address information and statistical reports, so customers can easily apply the data and relate it to internal registrations.