Recent Advances on 2D and 3D Change Detection in Urban Environments from Remote Sensing Data - Computational Approaches for Urban Environments - page 251

are based on post-classification comparison procedures, building changes can be
extracted by comparing multitemporal building detection maps and reconstructed
urban/city models.
Based on the current status and state of the art, the validation outcomes of
relevant studies, and the special challenges of each detection component separately,
the present study highlights certain issues and insights that may be applicable for
future research and development.

10.8.1 Need to Design Novel Multimodal

Computational Frameworks

In accordance with recent reports (Longbotham et al. 2012 ; Zhang 2012 ;Berger
et al. 2013 ), this survey highlights that the fusion of multimodal, multitemporal
data is considered to be the ultimate solution for optimized information extraction.
Currently, there is a lack in single, generic frameworks that can in full synergy
process and exploit all available geospatial data. This is a rather crucial issue since
the effective and accurate detection and modeling requires rich spatial, spectral, and
temporal (remote or not) observations over the structured environment acquired (i)
from various sensors, including frame and push-broom cameras and multispectral,
hyperspectral, thermal, and radar sensors, and (ii) from various platforms, including
satellite, airborne, UAV, and ground systems. This is not a trivial task and a lot of
research and development is, thus, required.

10.8.2 Need for Efficient Unsupervised Techniques Able

to Identify “From-To” Change Trajectories

Unsupervised and supervised approaches are holding the same share of research
interest. In particular, the unsupervised ones in many cases achieve the same
overall detection accuracy levels as the supervised ones do ( e.g., Longbotham
et al. 2012 ). This is a really promising fact given the possible capability of (near)
real-time response to urgent and timely crucial change detection tasks, without
training samples available. In dense time series and big geospatial data analysis,
this seems, also, the only possible direction. However, most applications require
end products which report on the detailed land-cover/land-use “from-to” change
trajectories instead of a binary “change or not” map (Lu et al. 2011c ; Bruzzone and
Bovolo 2013 ). The need for incorporating spatial context and relationships into the
detection procedure and introduce automated algorithms able to detect changes with
a semantic meaning is underlined from the present study.