Enhanced change detection index for disaster response, recovery assessment and monitoring of accessibility and open spaces (camp sites)

The availability of Very High Resolution (VHR) optical sensors and a growing image archive that is frequently updated, allows the use of change detection in post-disaster recovery and monitoring for robust and rapid results. The proposed semi-automated GIS object-based method uses readily available pre-disaster GIS data and adds existing knowledge into the processing to enhance change detection. It also allows targeting specific types of changes pertaining to similar man-made objects. This change detection method is based on pre/post normalized index, gradient of intensity, texture and edge similarity filters within the object and a set of training data. Once the change is quantified, based on training data, the method can be used automatically to detect change in order to observe recovery over time in large areas. Analysis over time can also contribute to obtaining a full picture of the recovery and development after disaster, thereby giving managers a better understanding of productive management practices.EU FP

RAPID MAPPING FOR BUILT HERITAGE AT RISK USING LOW-COST AND COTS SENSORS. A TEST IN THE DUOMO VECCHIO OF SAN SEVERINO MARCHE

In the last years, the researchers in the field of Geomatics have focused their attention in the experimentation and validation of new methodologies and techniques, stressing especially the potential of low-cost and COTS (Commercial Off The Shelf) solutions and sensors. In particular, these tools have been used with purposes of rapid mapping in different contexts (ranging from the construction industry, environmental monitoring, mining activities, etc.). The Built Heritage, due to its intrinsic nature of endangered artefact, can largely benefit from the technological and methodological innovations in this research field. The contribute presented in this paper will highlight these main topics: the rapid mapping of the Built Heritage (in particular the one subjected to different types of risk) using low-cost and COTS solutions. Different sensors and techniques were chosen to be evaluated on a specific test site: the Duomo Vecchio of San Severino Marche (MC - Italy), that was partially affected by the earthquake swarm that hit the area of Central Italy starting from the 24th of August 2016. One of the main aims of this work is to demonstrate how low-cost and COTS sensors can contribute to the documentation of the Built Heritage for its safeguard, for damage assessment in case of disastrous events and operations of restoration and preservation

SCDNET: A novel convolutional network for semantic change detection in high resolution optical remote sensing imagery

Abstract With the continuing improvement of remote-sensing (RS) sensors, it is crucial to monitor Earth surface changes at fine scale and in great detail. Thus, semantic change detection (SCD), which is capable of locating and identifying "from-to" change information simultaneously, is gaining growing attention in RS community. However, due to the limitation of large-scale SCD datasets, most existing SCD methods are focused on scene-level changes, where semantic change maps are generated with only coarse boundary or scarce category information. To address this issue, we propose a novel convolutional network for large-scale SCD (SCDNet). It is based on a Siamese UNet architecture, which consists of two encoders and two decoders with shared weights. First, multi-temporal images are given as input to the encoders to extract multi-scale deep representations. A multi-scale atrous convolution (MAC) unit is inserted at the end of the encoders to enlarge the receptive field as well as capturing multi-scale information. Then, difference feature maps are generated for each scale, which are combined with feature maps from the encoders to serve as inputs for the decoders. Attention mechanism and deep supervision strategy are further introduced to improve network performance. Finally, we utilize softmax layer to produce a semantic change map for each time image. Extensive experiments are carried out on two large-scale high-resolution SCD datasets, which demonstrates the effectiveness and superiority of the proposed method