THE WORK TO RECOVER AND REBUILD FOLLOWING an earthquake requires reliable information on the condition of structures in the affected areas. In developed areas, efforts to gather this information can be time-consuming and prone to errors, often resulting in incomplete or inaccurate information.

A new, software-based methodology to recognize collapsed buildings utilizes classification of satellite images combined with height variation information. The methodology was demonstrated in a full-scale, real-life scenario by a team led by Prof. Valerio Baiocchi of the University of Rome. According to Baiocchi, the team’s work was intended to demonstrate the methodology on actual data available for the entire city of L’Aquila in the Abruzzo region of central Italy, in an actual and complete simulation of quick damage assessment in a real emergency. The team utilized satellite imagery of the city of L’Aquila, which experienced a magnitude 6.3 earthquake on April 6, 2009. The work demonstrated a robust classification of collapsed structures that was completed quickly and with good confidence

Baiocchi, Valerio

Waldemar, Krebs

English

THE WORK TO RECOVER AND REBUILD FOLLOWING an earthquake requires reliable information on the condition of structures in the affected areas. In developed areas, efforts to gather this information can be time-consuming and prone to errors, often resulting in incomplete or inaccurate information.
A new, software-based methodology to recognize collapsed buildings utilizes classification of satellite images combined with height variation information. The methodology was demonstrated in a full-scale, real-life scenario by a team led by Prof. Valerio Baiocchi of the University of Rome. According to Baiocchi, the team’s work was intended to demonstrate the methodology on actual data available for the entire city of L’Aquila in the Abruzzo region of central Italy, in an actual and complete simulation of quick damage assessment in a real emergency. The team utilized satellite imagery of the city of L’Aquila, which experienced a magnitude 6.3 earthquake on April 6, 2009. The work demonstrated a robust classification of collapsed structures that was completed quickly and with good confidence

Valerio, Baiocchi

KREBS, WALDEMAR

Archivio della ricerca- Università di Roma La Sapienza

[ Winter 2015 / Vol. 30 / No. 1 ]Satellite view of North America, courtesy of NASAWe’ll see weather before it becomes weather...   Our efforts are about saving lives (by providing   better severe weather forecasting).” – Alan Hall, CEO, Tempus Global Data p. 26GEOGLAM    GEO’s Global Ag Monitoring InitiativeWe need to realize that the coastal zone is borrowed from the world’s   oceans and they are about to take it back.”– Hans-Peter Plag, PhD, Mitigation & Adaptation Research Institute, Old Dominion University  p. 16Weather Data CommercializationQ&A with Alan Hall, CEO Tempus Global Data p. 26The Certainty of  Sea Level Riseby Hans-Peter Plag, PhD Old Dominion University p. 13Earthquakes: Identifying Collapsed Buildingsby Valerio Baiocchi, PhD Sapienza University of Roma, Italy p. 22p. 13W W W . A P O G E O S P A T I A L . C O M  |  W I N T E R  2 0 1 5  A P O G E O  5  16 2213Columns9  EDITOR & PUBLISHER LETTER  Inspiration in Many Formsby Ray Williamson, PhD, Editor  and Myrna James Yoo, Publisher10  SECURE WORLD FOUNDATION FORUM  Report from ASPRS Pecora SymposiumVALUE OF LANDSAT, GOOGLE EARTH ENGINEby Ray Williamson, PhD13  ON THE EDGE  The Slowly  Developing Disaster:SEA LEVEL RISEBy Hans-Peter Plag, PhDFeatures16 The GEOGLAM InitiativeAGRICULTURAL MONITORING  WITH EARTH OBSERVATIONSby Christopher Justice, PhD, University of Maryland and Michel Deshayes, PhD, Group on Earth Observations22 EarthquakesIDENTIFYING COLLAPSED BUILDINGSby Valerio Baiocchi, PhD, Sapienza University of Roma and Waldemar Krebs, PhD, Trimble26  APOGEO SPATIAL EXECUTIVE INTERVIEW  Commercializing Weather DataFINANCIAL AND SOCIETAL BENEFITSQ&A with Alan Hall, CEO, Tempus Global Data[ Winter 2015 / Vol. 30 / No. 1 ] 6  |  W W W . A P O G E O S P A T I A L . C O M  |  W I N T E R  2 0 1 5  A P O G E OFormerly  [ Winter 2015 / Vol. 30 / No. 1 ]MISSIONApogeo Spatial communicates the power of geospatial tools and technologies  in managing the world’s environment and scarce resources, so that the  global population has the security of water, food, and energy.PARTNERSHIPS    Apogeo Spatial has a  partnership with  Secure World Foundation  (www.swfound.org).Apogeo is affiliated with the Alliance  for Earth Observations, a program of  The Institute for Global Environmental  Strategies (www.strategies.org).STAFF Publisher/Managing Editor MYRNA JAMES YOO   myrna@apogeospatial.com Editor RAY A. WILLIAMSON, PhD Contributing Writers LEONARD DAVID   MATTEO LUCCIO Creative Director ANDREW STERNARD   www.andrewsternard.comEDITORIAL ADVISORY BOARD MARK BRENDER DigitalGlobe Foundation ANITA BURKE The Catalyst Institute NANCY COLLETON Institute for Global    Environmental Strategies WILLIAM B. GAIL, PhD Global Weather Corporation ANNE HALE MIGLARESE PLANETiQ HANS-PETER PLAG , PhD Old Dominion University   Mitigation and Adaptation Research Institute KEVIN POMFRET, ESQ. Centre for Spatial Law and PolicyEDITORIAL CONTRIBUTIONSApogeo welcomes contributions for feature articles. We publish articles on the remote sensing industry, including applications, technology, and business. Please see Editorial Guidelines on www.apogeospatial.com, and email proposals to editor@apogeospatial.com.SUBSCRIPTIONSTo subscribe, please go to www.locationalliance.net/subscribe.  If you cannot go online, you may write to the address below.Apogeo (ISSN 0896-7091) Copyright © 2015Blueline Publishing LLC, P.O. Box 11519, Denver, CO 80211All rights reserved. No material may be reproduced or transmitted in any form or by any means without written permission from the publisher. While every precaution is taken to ensure accuracy, the publishers and their partners cannot accept responsibility for the accuracy of information or for any opinions or views presented in Apogeo.Although trademark and copyright symbols are not used in this publication, they are honored. Apogeo is printed on 20% recycled (10% post-consumer waste) paper.  All inks used contain a percentage of soy base. Our printer meets or  exceeds all federal Resource Conservation Recovery Act (RCRA) Standards.COVER IMAGENorth America from SpaceTHIS IMAGE OF NORTH AMERICA from NASA is reminiscent of the 1968 Apollo 8 Mission photo of the Earth from space, called Earth Rise, though this one is not the entire Earth. The further out the view, the more pro-found is the fact of seeing no boundaries and political borders.Earth imagery has come a long way, with many people using both commercial and gov-ernment imagery. Landsat 8 is a great victory for the academic and scientific communities with their precedent-setting free data (see Secure World Foundation Forum on page 10), and WorldView-3 is pushing the envelope for companies and government customers.The Group on Earth Observations’ Global Agricultural Monitoring initiative (GEOGLAM) is an excellent example of an organization using both free and commercial imagery for their important and far-reaching work, on page 16.  2 2  |  W W W . A P O G E O S P A T I A L . C O M  |  W I N T E R  2 0 1 5  A P O G E OIdentifying Collapsed Buildings THE WORK TO RECOVER AND REBUILD FOLLOWING an earthquake requires reliable information on the condition of structures in the affected areas. In developed areas, efforts to gather this information can be time-consuming and prone to errors, often resulting in incomplete or inaccurate information.A new, software-based methodology to recognize collapsed buildings utilizes classification of satellite images combined with height variation informa-tion. The methodology was demonstrated in a full-scale, real-life scenario by a team led by Prof. Valerio Baiocchi of the University of Rome. According to Baiocchi, the team’s work was intended to demon-strate the methodology on actual data available for the entire city of L’Aquila in the Abruzzo region of central Italy, in an actual and complete simulation of quick damage assessment in a real emergency. The team utilized satellite imagery of the city of L’Aquila, which experienced a magnitude 6.3 earthquake on April 6, 2009. The work demonstrated a robust clas-sification of collapsed structures that was completed quickly and with good confidence. FIGURE 1. Classification of vegetated areas through NDVI. FIGURE 2. Classification results (object-oriented approach) on some subsets (red for buildings, gray for streets, green for vegetated areas, and black for shadows).DR. VALERIO BAIOCCHI / DOCTOR OF PHILOSOPHY / GEODESY AND SURVEY SCIENCES  AND PROFESSOR AT SAPIENZA / UNIVERSITY OF ROMA  DR. WALDEMAR KREBS / ECOGNITION ACCOUNT MANAGER AT TRIMBLE / MUNICH, GERMANY WWW.ECOGNITION.COM1W W W . A P O G E O S P A T I A L . C O M  |  W I N T E R  2 0 1 5  A P O G E O  2 3  2 2 4  |  W W W . A P O G E O S P A T I A L . C O M  |  W I N T E R  2 0 1 5  A P O G E OCOMPARING PIXEL-BASED AND  OBJECT-ORIENTED CLASSIFICATIONSThe first part of the process entails classifica-tion of remotely-sensed objects. Data were obtained from monoscopic, very high-resolution imag-ery taken by the WorldView-2 satellite in August 2010. Height information came from a digital sur-face model developed from imagery acquired by the EROS-B satellite in April 2009, just a few days after the main earthquake. The pansharpened images were orthorectified using 22 ground control points established by GNSS and a digital elevation model from existing Abruzzo regional cartography (CartaTecnicaRegionale, or CTR). The study region was then broken into nine 2.3 km x 2.3 km subsets.Classification was performed using two approaches: traditional pixel-based analyses and an object-based approach using Trimble eCogni-tion Developer 8.0 image analysis software. The approaches used identical class hierarchies comprised of buildings, green areas, roads and shadows.The data were first processed using the object-based solution, which is based on the idea that the important information necessary to interpret an image is not represented only in a single pixel, but in meaningful image objects and their mutual rela-tionships. Rather than classifying single pixels, the object approach uses a bottom-up, region-grow-ing technique to create groups of pixels (known as segments) that possess a certain level of homoge-neity in scale, shape and color.Initial processing used NDVI (Normalized Difference Vegetation Index) to assign objects to the vegetation class. A similar process was used to reduce risk that shadowed parts of buildings might be eliminated from processing. See Figure 1.Other hierarchal classes were handled using nearest-neighbor classification techniques. The classification required 30 to 60 minutes per subset, which can be scaled by parallel processing. The pro-duced vector files of building polygons were overlaid on the rectified satellite images.A second round of classification utilized pixel-based approaches in NDVI and nearest-neighbor  FIGURE 3. The vector file of buildings obtained after the image classification. FIGURE 4. The same file overlapping to a satellite image.As it is well known, during the Haiti event it was not possible to detect quickly all the collapsed buildings from aerial and satellite images. Local authorities needed to ask for the help of volunteers for a manual identification of collapsed buildings. For this reason, our automated methodology was utilized exactly  as in a real emergency situation.43W W W . A P O G E O S P A T I A L . C O M  |  W I N T E R  2 0 1 5  A P O G E O  2 5  processing. Both sets of results were compared to the CTR cartography provided by the Abruzzo regional administration. The comparison was done using the visible surface area of building roofs. See Figure 2.The eCognition process recognized 93 percent of the known building areas, while the pixel-based pro-cess could recognize 64 percent. This result demon-strated that the object-oriented technique provided information that is more accurate and robust than the pixel-based approach.When the vector file produced by eCognition was overlaid onto the satellite images, it was possible to quickly identify areas where buildings had collapsed in the quake. Because of this result, only the object-based classifications were used in the subsequent steps of the process.Next, digital surface models were developed using pre- and post-quake imagery from EROS-B. Comparing these models exposed height differences of buildings before and after the earthquake, and a color-coded model was developed to highlight sig-nificant differences. These data could then be com-bined with the existing cartography.Using building polygons identified by eCognition, locations where the height differences might be the result of a building collapse (as opposed to changes in height in areas where no buildings existed) could be identified. The data enabled users to readily identify all buildings in the study area that had collapsed as a result of the earthquake. See Figures 3 and 4.CONCLUSIONSThe project demonstrated that the use of a high-resolution, object-oriented classification image combined with digital surface models can produce accurate, robust information on changes in build-ings in post-seismic scenarios. The object-oriented approach produced results superior to pixel-based methods. The process may be further improved through optimization and improved specialization of the classification rule sets.To be effective, organizations must main-tain accurate pre-event data. Similarly, the meth-ods require the availability of high-performance hardware and software to quickly execute classifica-tion and DSM extraction in immediate post-quake events. The methodology could allow a first damage assessment to be performed quickly in a completely automated way through the use of remotely sensed data, thereby offering ready support for the emer-gency response activities carried out by humanitar-ian agencies with little or no error.Baiocchi summarized the importance of the team’s work: “Quick assessment of damage, par-ticularly in earthquakes like the one that occurred in Haiti in 2010, can help to save potential victims buried under debris. Rescue operations need to know the location of collapsed buildings within hours to aid in rescue of trapped persons. As it is well known, during the Haiti event it was not possi-ble to detect quickly all the collapsed buildings from aerial and satellite images. Local authorities needed to ask for the help of volunteers all over the world for a manual identification of collapsed buildings on the images. For this reason, our automated meth-odology (obviously faster than manual recognition) was utilized exactly as in a real emergency situation. By assessing the performance using real data, it has been confirmed that it can be used for future emer-gency situation.” Acknowledgements:This paper is taken from work produced by Valerio Baiocchi, “Sapienza” University of Rome; Raffaella Brigante, University of Perugia; Donatella Dominici, University of L’Aquila; Maria Vittoria Milone and Martina Mormile, “Sapienza” University of Rome; and Fabio Radicioni, University of Perugia. The full research is published in European Journal of Remote Sensing – 2014, 47: 413-435. The paper may be freely viewed at http://server-geolab.agr.unifi.it/public/completed/2014_EuJRS_47_413_435_Baiocchi.pdf 

Identifying collapsed buildings

https://iris.uniroma1.it/bitstream/11573/771394/1/Baiocchi_Identifying-collapsed-buildings_2015.pdf

Identifying collapsed buildings

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Archivio della ricerca- Università di Roma La Sapienza