Recent Advances on 2D and 3D Change Detection in Urban Environments from Remote Sensing Data - Computational Approaches for Urban Environments - page 229

10.1 Introduction

Understanding and modeling in detail the dynamic 3D urban scenes can enable
effectively urban environment sustainability. In particular, the efficient spatiotem-
poral urban monitoring in large scale is critical in various engineering, civilian,
and military applications such as urban and rural planning, mapping, and updating
geographic information systems, housing value, population estimation, surveillance,
transportation, archeology, architecture, augmented reality, 3D visualization, virtual
tourism, location-based services, navigation, wireless telecommunications, disaster
management, and noise, heat, and exhaust spreading simulations. All these subjects
are actively discussed in the geography, geoscience, and computer vision scien-
tific communities both in academia and industry. Organizations like Google and
Microsoft are trying and seeking to include extensively up-to-date 2D and 3D urban
models in their products (Microsoft Virtual Earth and Google Earth).
The prohibitively high cost of generating manually such 2D and 3D dynamic
models/maps explains the urgent need towards automatic approaches, especially
when one considers modeling and monitoring time-varying events within the
complex urban areas. In addition, there is an emergence for algorithms that provide
generic solutions through the automated and concurrent processing of all available
data like panchromatic, multispectral, hyperspectral, radar, and digital elevation
data. However, processing multimodal data is not straightforward (He et al. 2011b ;
Longbotham et al. 2012 ;Bergeretal. 2013 ) and requires novel, sophisticated
algorithms that on the one hand can accept as an input multiple data from different
sensors, data with different dimensions, and data with different geometric, spatial,
and spectral properties and on the other hand can automatically register and process
them.
Furthermore, despite the important research activity during the last decades,
there are, still, important challenges towards the development of automated and
accurate change detection algorithms (Lu et al. 2011c ; Longbotham et al. 2012 ;
Hussain et al. 2013 ). It has been generally agreed and is verified by the quantitative
evaluation of recent research efforts that there isn't, still, any specific single, generic,
automated methodology that is appropriate for all applications and/or all the case
studies. The maximum accuracy of the 2010 multimodal change detection contest
was just over 70 % (Longbotham et al. 2012 ). This is in accordance and closely
related with Wilkinson's earlier report on the minor improvement during the last
decade on the performance of classification algorithms (Wilkinson 2005 ). Even the
latest machine learning techniques haven't contributed much on the remote sensing
data classification problem. Standard approaches usually result in similar levels of
accuracy with the newer more advanced ones. Therefore, several aspects of the
change detection process, towards the efficient 2D and 3D updating of geospatial
databases, possess emerging challenges.
The aforementioned need for more intensive research and development is,
furthermore, boosted by the available and increasing petabyte archives of geospatial
(big) data. Along with the increasing volume and reliability of real-time sensor