Land Registration and Territories
We automate the detection of changes in buildings and land registry omissions
Tracasa Global, a leader in the development of innovative geospatial solutions, works on the design and creation of smart applications featuring geographic information, such as orthophotos and satellite images. In this area, our company, with its own R&D team, has solutions based on artificial intelligence for the detection of land registry omissions and the monitoring of changes in both urban and rural environments.
Orthophotos and comparisons with the Land Registry
The first of the solutions consists of a Deep Learning model focused on the detection of changes in constructions through the segmentation of PNOA (National Aerial Orthophotography Plan) images and the optimized comparison of results with respect to the information at the Land Registry.
This model makes it possible to automatically locate both new constructions and changes to existing ones. The workflow includes steps such as building segmentation, automatic and smart comparisons of building masks against the land registry, and classification of the different types of changes detected.
PNOA orthophotos
Land Registry Information
Segmentation of elements
Detection of new buildings
Satellite images: SENX4 super-resolution
Along the same lines, but for situations where it is necessary to act on broader monitoring areas, or with greater periodicity, another solution developed by Tracasa Global applies a Deep Learning algorithm for the automatic segmentation of buildings and roads, through a combined analysis of images from the Sentinel-1 and Sentinel-2 satellites.
This method is enriched by our SENX4 super-resolution algorithm, which quadruples the spatial resolution of Sentinel-2 images, with free access and acquisitions every 5 days. SENX4 improves the resolution of satellite images, going from 10 to 2.5 meters and guaranteeing the conservation of radiometry. In this way, the result of the execution of these algorithms makes it possible to identify areas with a high probability of changes on the ground.