The purpose of this study is to close this gap by investigating questions on the practical applicability of BFP extraction. Despite the comprehensive work performed, it is still unclear how robust and generalizable state-of-the-art methods are to different regions, cities, settlement structures, and densities. A rich body of work for BFP extraction has been presented in the literature, and promising research results have been reported on benchmarking datasets.

In addition, effects, such as occlusions, shadows, and bad lighting, have to also be considered and compensated. Building footprint extraction is challenging because buildings can have different sizes, shapes, and appearances both in the same region and in different regions of the world. BFP extraction is an essential task in remote sensing and represents the foundation for many higher-level analysis tasks, such as disaster management, monitoring of city development, etc. īuilding footprint (BFP) extraction focuses on the precise pixel-wise segmentation of buildings from aerial photographs such as satellite images. The landscape is characterised by being a coastal region situated in the temperate climate zone that receives on average about 800 mm rain per year. The soil is predominantly clay in the northern part and moist, loamy sand or sand in the southern part (known as the “polders”) of the study area (GDI-Flanders, 2001). It covers mainly grasing fields and intensive, agricultural croplands. The study area consisted of a selection of 58 grasing fields (parcels) from 29 farms east of the historic town of Bruges in Belgium (Fig. Successful results are expect- ed to enhance the spatial resolution of epidemiological studies, resulting in an improved capacity to forecast areas under potential liver fluke threat. The objective was to develop an automated object-based methodology to delineate SWBs and enable the identification, characterisation and classification of vector habitats. The aim of this paper is to describe an automated process using VHR satellite imagery to detect SWBs at a fine spatial scale in a case study in Flanders (Bruges region) using a WorldView-2 image acquired in May 2013. of easy incorporation in a vector-based geographical information systems (GIS) environment (Hay and Castilla, 2006 Kim et al., 2009).

Google maps earth view satellite