David Dubois

Fast and Efficient Evaluation of Building Damage From Very High Resolution Optical Satellite Images

In this paper, we present a novel combination of object features to both match buildings from predisaster images to shapes in a postdisaster image and assess damage on those buildings. These features include scale profile ratios extracted from a tree of shapes representation of the original image as well as texture features. A supervised classifier is used to classify building damage into three representative classes tied to the European Macroseismic Scale (EMS-98). The method is compared to visual inspection results as well as other automated methods. Results clearly show the benefits of our method for fast crisis mapping applications with few human inputs required.

Scale profile as feature for quick satellite image object-based classification

With the increasing precision of recent spaceborne sensors, remotely sensed images have become exceedingly large. These images are being used more and more often in the preparation of emergency maps when a disaster occurs. Visual interpretation of these images is long and automatic pixel-based methods require a lot of memory, processing power and time. In this paper, we propose to use a fast level-set image transformation in order to obtain a hierarchical representation of images objects. A scale profile is then extracted and included as a relevant feature for land-use classification in urban areas. The main contribution of this paper is the analysis of the scale profile for remote sensing applications. The data set from the earthquake that occurred on 12 January 2012 in Haiti is used.

Meeting remote sensing requirements for faster disaster response

Natural and man-made disasters make the headline many times a year. Crisis images depicting extent of event and intensity of damage are frequently displayed to emphasize the impact of the disaster on the local population. These maps are more and more often generated from raw images acquired by spaceborne sensors. Very high space resolution images are huge and require very careful analysis by numerous teams of trained photo-interpreter in order to provide meaningful maps in a timely manner. This paper describes the acquisition process and image analysis steps currently required to create crisis maps for events for which the International Charter “Space & Major Disasters” is activated. We answer the question “how can image processing time be reduced for faster disaster mapping?” by proposing a semi-automated building detection process.

Object- versus pixel-based building detection for disaster response

Recent disasters have shown that there is a growing interest for remotely sensed data to support decision makers and emergency teams in the field. Fast and accurate detection of buildings and sustained damage is of great importance. Current methods rely on numerous photo-interpreters to visually analyze the data. Multiple pixel-based methods exist to classify pixels as being part of a building or not but results vary widely and precision is often poor with very high resolution images. This paper proposes an object-based solution to building detection and compares it to a traditional approach. Object-based classification clearly provides adequate results in much less time and thus is ideal for disaster response.

Software development to speed up disaster management with remote sensing

When a disaster occurs, earth observation specialists must analyze a considerable amount of images as quickly as possible to help rescue teams or to make on-the-fly decisions. As there is currently no remote sensing software that is specific for natural disasters, we propose in this work to determine the basic requirements for such software and develop a test framework for such application. Our approach includes a state of the art review on current earth observation software as well as disaster response needs. We also list requirements based on our review and propose a solution for providing software assistance to photointerpreters. Finally our test framework is presented.

Automated building damage classification for the case of the 2010 Haiti earthquake

Building damage evaluation is an important part of disaster response and recovery phases of the emergency management cycle. Earth observation data acquired by remote sensing satellites can provide useful information for damage mapping. Unfortunately, very high spatial resolution images are quite large and require analysis by multiple expert photo-interpreters in order to extract meaningful information in short time. This can lead to human errors caused by fatigue and varying degrees of image understanding from one analyst to the next. In this paper, we propose an object-based supervised learning scheme using geometric, scale and textural object features to detect the level of damage on buildings affected by an earthquake. The method is applied to the case of the 2010 Haiti earthquake.

Toward a GUI remote sensing environment built over OTB

The need for fast and reliable remote sensing algorithms is continuously growing. Remote sensing libraries are scarce and sometimes are difficult to use. The Orfeo Toolbox (OTB) is one such library that stands apart with its robust development scheme and programming concepts. Not all remote sensing researchers have a strong programming background. Programmers need to find ways of giving easier access to desired algorithms without the need of cumbersome programming overheard. This paper compares the actual parameter management class of OTB with a proposed dynamic graphical interface to replace it. The programming concepts used will first be presented. This work will lead to creating a visual programming environment for OTB.