Building Footprint Extraction

The advanced deep learning technique presented in this paper has been developed for the automatic extraction of building footprints from high-resolution aerial/satellite imagery, with image resolutions ranging from 15 cm to 25 cm. The data of building footprints is an essential geospatial resource to develop detailed basemaps, update the databases in GIS, and carry out various spatial analyses and decision-making processes.

Building footprints are very useful information for urban planning, infrastructure development, valuation, insurance, taxation, disaster management, environmental monitoring, change detection, and many other purposes. They give an insight into the location, shapes, sizes, and other characteristics of the buildings, which helps to track the changes of the built environment.

Manual extraction of building footprints usually involves experienced GIS specialists who perform the digitization of individual buildings from the aerial/satellite imagery. This method is quite time- and labor-intensive, especially in the case of huge geographical areas, and may be quite expensive. It may also cause some inconsistencies among operators and make it difficult to keep up-to-date building databases with newly acquired imagery.

This approach enables deep learning models to offer an efficient solution through automatic identification and segmentation of building footprints based on the use of computer vision technology. Through learning complex spatial, spectral, and structural features of buildings, the model can distinguish building footprints from roads, vegetation, shadows, and other surrounding objects.

Through the automation of the building footprint extraction process, the model offers the potential to save time and effort while ensuring that building footprints are produced consistently and accurately. This automated extraction process will be particularly useful when creating building footprints of thousands or even millions of buildings during large-scale geospatial projects.

Building footprint datasets produced by the model can be incorporated into GIS software, spatial databases, maps, and geospatial analyses. They can be used in applications ranging from monitoring urban growth to planning infrastructure development to population analysis.

Through its potential to process high-resolution imagery and automate the extraction process that is usually done manually, this deep learning model provides an efficient solution to the mapping of buildings and the production of geospatial data.