No abstract

This paper uses GeoEye-1 imagery and airborne lidar (Light Detection and Ranging) data to map buildings and their rubble in Port-au-Prince caused by the Haiti earthquake on 12 January 2010. This is achieved by performing an objectbased one-class-at-a-time land cover classification of the image and lidar data using spectral, textural and height information. Classification accuracy is about 87 percent overall, and approximately 80 percent for buildings and rubble. Comparison of manually-selected 200 actual damaged buildings within an area of two sq. km in the city center shows an accuracy of over 90 percent for building and rubble mapping. 3D building models for approximately 55,000 buildings covering an area of 30 sq. km over Port-au-Prince were generated. It is found that most of the damage is to the concrete and masonry structures in the well planned areas of the city and very little damage to the shelters and the temporary type houses with metal sheet roofs. The study demonstrates that fusing optical imagery and lidar data can effectively map the nature, severity, extent and damage patterns caused by earthquakes in densely populated urban areas like Port-au-Prince.

ABSTRACT:Transportation agencies require up-to-date, reliable, and feasibly acquired information on road geometry and features within proximity to the roads as input for evaluating and prioritizing new or improvement road projects. The information needed for a robust evaluation of road projects includes road centerline, width, and extent together with the average grade, cross-sections, and obstructions near the travelled way. Remote sensing is equipped with a large collection of data and well-established tools for acquiring the information and extracting aforementioned various road features at various levels and scopes. Even with many remote sensing data and methods available for road extraction, transportation operation requires more than the centerlines. Acquiring information that is spatially coherent at the operational level for the entire road system is challenging and needs multiple data sources to be integrated. In the presented study, we established a framework that used data from multiple sources, including one-foot resolution color infrared orthophotos, airborne LiDAR point clouds, and existing spatially non-accurate ancillary road networks. We were able to extract 90.25% of a total of 23.6 miles of road networks together with estimated road width, average grade along the road, and cross sections at specified intervals. Also, we have extracted buildings and vegetation within a predetermined proximity to the extracted road extent. 90.6% of 107 existing buildings were correctly identified with 31% false detection rate.

Abstract. Systematic errors may result from the adoption of an incomplete functional model that is not able to properly incorporate all the effects involved in the image formation process. These errors very likely appear as systematic residual patterns in image observations and produce deformations of the photogrammetric model in object space. The Brown/Beyer model of self-calibration is often adopted in underwater photogrammetry, although it does not take into account the refraction introduced by the passage of the optical ray through different media, i.e. air and water. This reduces the potential accuracy of photogrammetry underwater. In this work, we investigate through simulations the depth-dependent systematic errors introduced by unmodelled refraction effects when both flat and dome ports are used. The importance of camera geometry to reduce the deformation in the object space is analyzed and mitigation measures to reduce the systematic patterns in image observations are investigated. It is shown how, for flat ports, the use of a stochastic approach, consisting in radial weighting of image observations, improves the accuracy in object space up to 50%. Iterative look-up table corrections are instead adopted to reduce the evident systematic residual patterns in the case of dome ports.