Recent Advances on 2D and 3D Change Detection in Urban Environments from Remote Sensing Data - Computational Approaches for Urban Environments - page 232

required for detection and monitoring. Along with the different specifications of the
currently available remote sensing data, this is, actually, the main reason why these
categories seem to form different groups in the literature including data, methods,
and validation practices. In particular, the biggest share are holding the efforts which
focus either on land cover/land use or either on building change detection.
On the one hand, the opening of the United States Geological Survey's Landsat
data archive (Woodcock et al. 2008 ; Wulder et al. 2012 ) and the newly launched
Landsat Data Continuity Mission (LDCM) enabled the easy access to a record
of historical data and related studies on monitoring mainly land-cover/land-use
changes, updating land national cover maps, and detecting the spatiotemporal
dynamics, the evolution of land-use change, and landscape patterns. With this
increased data availability and the increasing open data policies both in the USA
and EU, similar studies can correspond to the current demand for improving the
capacity to mass process big data and enable the efficient spatiotemporal modeling
and monitoring.
On the other hand, a significant amount of research was focused on local
scales and building change detection. Novel promising automated algorithms were
developed which allow one to automatically detect, capture, analyze, and model effi-
ciently single buildings in dynamic urban scenes. Mainly model-based approaches,
like parametric, structural, statistical, procedural, and grammar-based ones, have
been design to detect, both in 2D and in 3D, buildings and spatiotemporal changes.
Google Earth, Virtual Earth, and other government applications and databases must
be/remain updated, and therefore, the motivation on automated algorithms instead
of costly manual digitization procedures is, still, high.
Apart from the requirements regarding the multiple properties of the desired
product and detection target, the change detection procedure is affected by a
number of parameters including spatial, spectral, thematic, and temporal constraints;
radiometric, atmospheric, and geometric properties; and soil moisture conditions.
Therefore, a sophisticated methodology should be able to address in a preprocessing
step all the various constrains and conditions that will enable an effective and
accurate core spatiotemporal analysis. In the following two subsections, certain
important aspects regarding the multiple properties of the remote sensing data are
detailed along with a brief description on the required preprocessing steps.

10.3 Remote Sensing Data

During the last decades important technological advances in optics, photonics,
electronics, and nanotechnology allowed the development of frame and push-
broom sensor with high spatial and spectral resolution. New satellite mission
have been scheduled continuously and gradually remote sensing data of higher
quality from either passive or active sensors will be available. However, today data
with high spatial and spectral resolution is either for military or commercial use.
In Table 10.2 , a summary of the currently available satellite remote sensing sensors,