A framework for automatic feature extraction from airborne light detection and ranging data

Recent advances in airborne Light Detection and Ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. Airborne LIDAR systems usually return a 3-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. This technology is becoming a primary method for extracting information of different kinds of geometrical objects, such as high-resolution digital terrain models (DTMs), buildings and trees, etc. In the past decade, LIDAR gets more and more interest from researchers in the field of remote sensing and GIS. Compared to the traditional data sources, such as aerial photography and satellite images, LIDAR measurements are not influenced by sun shadow and relief displacement. However, voluminous data pose a new challenge for automated extraction the geometrical information from LIDAR measurements because many raster image processing techniques cannot be directly applied to irregularly spaced LIDAR points. ^ In this dissertation, a framework is proposed to filter out information about different kinds of geometrical objects, such as terrain and buildings from LIDAR automatically. They are essential to numerous applications such as flood modeling, landslide prediction and hurricane animation. The framework consists of several intuitive algorithms. Firstly, a progressive morphological filter was developed to detect non-ground LIDAR measurements. By gradually increasing the window size and elevation difference threshold of the filter, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Then, building measurements are identified from no-ground measurements using a region growing algorithm based on the plane-fitting technique. Raw footprints for segmented building measurements are derived by connecting boundary points and are further simplified and adjusted by several proposed operations to remove noise, which is caused by irregularly spaced LIDAR measurements. To reconstruct 3D building models, the raw 2D topology of each building is first extracted and then further adjusted. Since the adjusting operations for simple building models do not work well on 2D topology, 2D snake algorithm is proposed to adjust 2D topology. The 2D snake algorithm consists of newly defined energy functions for topology adjusting and a linear algorithm to find the minimal energy value of 2D snake problems. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. The results demonstrated that the proposed framework achieves a very good performance. ^

Re-imagining Yerevan in the post-Soviet era: Urban symbolism and narratives of the nation in the landscape of Armenia's capital

The urban landscape of Yerevan has experienced tremendous changes since the collapse of the Soviet Union and Armenia’s independence in 1991. Domestic and foreign investments have poured into Yerevan’s building sector, converting many downtown neighborhoods into sleek modern districts that now cater to foreign investors, tourists, and the newly rich Armenian nationals. Large portions of the city’s green parks and other public spaces have been commercialized for private and exclusive use, creating zones that are accessible only to the affluent. In this dissertation I explore the rapidly transforming landscape of Yerevan and its connections to the development of contemporary Armenian national identity. This research was guided by principles of ethnographic inquiry, and I employed diverse methods, including document and archival research, structured and semi-structured interviews and content analysis of news media. I also used geographic information systems (GIS) and satellite images to represent and visualize the stark transformations of spaces in Yerevan. Informed by and contributing to three literatures—on the relationship between landscape and identity formation, on the construction of national identity, and on Soviet and post-Soviet cities—this dissertation investigates how messages about contemporary Armenian national identity are being expressed via the transforming landscape of Armenia’s national capital. In it I describe the ways in which abrupt transformations have resulted in the physical and symbolic eviction of residents, introducing fierce public debates about belonging and exclusion within the changing urban context. I demonstrate that the new additions to Yerevan’s landscape and the symbolic messages that they carry are hotly contested by many long-time residents, who struggle for inclusion of their opinions and interests in the process of re-imagining their national capital. This dissertation illustrates many of the trends that are apparent in post-Soviet and post-Socialist space, while at the same time exposing some unique characteristics of the Armenian case.

Land-cover detection and landscape structure analysis in the Pachitea Basin, Peruvian Amazon

Classification procedures, including atmospheric correction satellite images as well as classification performance utilizing calibration and validation at different levels, have been investigated in the context of a coarse land-cover classification scheme for the Pachitea Basin. Two different correction methods were tested against no correction in terms of reflectance correction towards a common response for pseudo-invariant features (PIF). The accuracy of classifications derived from each of the three methods was then assessed in a discriminant analysis using crossvalidation at pixel, polygon, region, and image levels. Results indicate that only regression adjusted images using PIFs show no significant difference between images in any of the bands. A comparison of classifications at different levels suggests though that at pixel, polygon, and region levels the accuracy of the classifications do not significantly differ between corrected and uncorrected images. Spatial patterns of land-cover were analyzed in terms of colonization history, infrastructure, suitability of the land, and landownership. The actual use of the land is driven mainly by the ability to access the land and markets as is obvious in the distribution of land cover as a function of distance to rivers and roads. When considering all rivers and roads a threshold distance at which disproportional agro-pastoral land cover switches from over represented to under represented is at about 1km. Best land use suggestions seem not to affect the choice of land use. Differences in abundance of land cover between watersheds are more prevailing than differences between colonist and indigenous groups.