Deformable Part Models for Automatically Georeferencing Historical Map Images

Libraries are digitizing their collections of maps from all eras, generating increasingly large online collections of historical cartographic resources. Aligning such maps to a modern geographic coordinate system greatly increases their utility. This work presents a method for such automatic georeferencing, matching raster image content to GIS vector coordinate data. Given an approximate initial alignment that has already been projected from a spherical geographic coordinate system to a Cartesian map coordinate system, a probabilistic shape-matching scheme determines an optimized match between the GIS contours and ink in the binarized map image. Using an evaluation set of 20 historical maps from states and regions of the U.S., the method reduces average alignment RMSE by 12%

Details of Deformable Part Models for Automatically Georeferencing Historical Map Images

Libraries are digitizing their collections of maps from all eras, generating increasingly large online collections of historical cartographic resources. Aligning such maps to a modern geographic coordinate system greatly increases their utility. This work presents a method for such automatic georeferencing, matching raster image content to GIS vector coordinate data. Given an approximate initial alignment that has already been projected from a spherical geographic coordinate system to a Cartesian map coordinate system, a probabilistic shape-matching scheme determines an optimized match between the GIS contours and ink in the binarized map image. Us- ing an evaluation set of 20 historical maps from states and regions of the U.S., the method reduces average alignment RMSE by 12%

Structure-From-Motion Photogrammetry of Antarctic Historical Aerial Photographs in Conjunction with Ground Control Derived from Satellite Data

A longer temporal scale of Antarctic observations is vital to better understanding glacier dynamics and improving ice sheet model projections. One underutilized data source that expands the temporal scale is aerial photography, specifically imagery collected prior to 1990. However, processing Antarctic historical aerial imagery using modern photogrammetry software is difficult, as it requires precise information about the data collection process and extensive in situ ground control is required. Often, the necessary orientation metadata for older aerial imagery is lost and in situ data collection in regions like Antarctica is extremely difficult to obtain, limiting the use of traditional photogrammetric methods. Here, we test an alternative methodology to generate elevations from historical Antarctic aerial imagery. Instead of relying on pre-existing ground control, we use structure-from-motion photogrammetry techniques to process the imagery with manually derived ground control from high-resolution satellite imagery. This case study is based on vertical aerial image sets collected over Byrd Glacier, East Antarctica in December 1978 and January 1979. Our results are the oldest, highest resolution digital elevation models (DEMs) ever generated for an Antarctic glacier. We use these DEMs to estimate glacier dynamics and show that surface elevation of Byrd Glacier has been constant for the past ∼40 years

Fire Environment Analysis at Army Garrison Camp Williams in Relation to Fire Behavior Potential for Gauging Fuel Modification Needs

Large fires (400 ha +) occur about every seven to ten years in the vegetation types located at US Army Garrison Camp Williams (AGCW) practice range located near South Jordan, Utah. In 2010 and 2012, wildfires burned beyond the Camp’s boundaries into the wildland-urban interface. The political and public reaction to these fire escapes was intense. Researchers at Utah State University were asked to organize a system of fuel treatments that could be developed to prevent future escapes. The first step of evaluation was to spatially predict fuel model types derived from a random forests classification approach. Fuel types were mapped according to fire behavior fuel models with an overall validation of 72.3% at 0.5 m resolution. Next, using a combination of empirical and semi-empirical based methods, potential fire behavior was analyzed for the dominant vegetation types at AGCW on a climatological basis. Results suggest the need for removal of woody vegetation within 20 m of firebreaks and a minimum firebreak width of 8 m in grassland fuels. In Utah juniper (Juniperus osteosperma (Torr.) Little), results suggest canopy coverage of 25% or less while in Gambel oak (Quercus gambelii Nutt.) stands along the northern boundary of the installation, a fuelbreak width of 60 m for secondary breaks and 90 m for primary breaks is recommended

Merging digital surface models sourced from multi-satellite imagery and their consequent application in automating 3D building modelling

Recently, especially within the last two decades, the demand for DSMs (Digital Surface Models) and 3D city models has increased dramatically. This has arisen due to the emergence of new applications beyond construction or analysis and consequently to a focus on accuracy and the cost. This thesis addresses two linked subjects: first improving the quality of the DSM by merging different source DSMs using a Bayesian approach; and second, extracting building footprints using approaches, including Bayesian approaches, and producing 3D models. Regarding the first topic, a probabilistic model has been generated based on the Bayesian approach in order to merge different source DSMs from different sensors. The Bayesian approach is specified to be ideal in the case when the data is limited and this can consequently be compensated by introducing the a priori. The implemented prior is based on the hypothesis that the building roof outlines are specified to be smooth, for that reason local entropy has been implemented in order to infer the a priori data. In addition to the a priori estimation, the quality of the DSMs is obtained by using field checkpoints from differential GNSS. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the Maximum Likelihood model which showed similar quantitative statistical results and better qualitative results. Perhaps it is worth mentioning that, although the DSMs used in the merging have been produced using satellite images, the model can be applied on any type of DSM. The second topic is building footprint extraction based on using satellite imagery. An efficient flow-line for automatic building footprint extraction and 3D model construction, from both stereo panchromatic and multispectral satellite imagery was developed. This flow-line has been applied in an area of different building types, with both hipped and sloped roofs. The flow line consisted of multi stages. First, data preparation, digital orthoimagery and DSMs are created from WorldView-1. Pleiades imagery is used to create a vegetation mask. The orthoimagery then undergoes binary classification into ‘foreground’ (including buildings, shadows, open-water, roads and trees) and ‘background’ (including grass, bare soil, and clay). From the foreground class, shadows and open water are removed after creating a shadow mask by thresholding the same orthoimagery. Likewise roads have been removed, for the time being, after interactively creating a mask using the orthoimagery. NDVI processing of the Pleiades imagery has been used to create a mask for removing the trees. An ‘edge map’ is produced using Canny edge detection to define the exact building boundary outlines, from enhanced orthoimagery. A normalised digital surface model (nDSM) is produced from the original DSM using smoothing and subtracting techniques. Second, start Building Detection and Extraction. Buildings can be detected, in part, in the nDSM as isolated relatively elevated ‘blobs’. These nDSM ‘blobs’ are uniquely labelled to identify rudimentary buildings. Each ‘blob’ is paired with its corresponding ‘foreground’ area from the orthoimagery. Each ‘foreground’ area is used as an initial building boundary, which is then vectorised and simplified. Some unnecessary details in the ‘edge map’, particularly on the roofs of the buildings can be removed using mathematical morphology. Some building edges are not detected in the ‘edge map’ due to low contrast in some parts of the orthoimagery. The ‘edge map’ is subsequently further improved also using mathematical morphology, leading to the ‘modified edge map’. Finally, A Bayesian approach is used to find the most probable coordinates of the building footprints, based on the ‘modified edge map’. The proposal that is made for the footprint a priori data is based on the creating a PDF which assumes that the probable footprint angle at the corner is 90o and along the edge is 180o, with a less probable value given to the other angles such as 45o and 135o. The 3D model is constructed by extracting the elevation of the buildings from the DSM and combining it with the regularized building boundary. Validation, both quantitatively and qualitatively has shown that the developed process and associated algorithms have successfully been able to extract building footprints and create 3D models

CHARACTERIZING FOREST STANDS USING UNMANNED AERIAL SYSTEMS (UAS) DIGITAL PHOTOGRAMMETRY: ADVANCEMENTS AND CHALLENGES IN MONITORING LOCAL SCALE FOREST COMPOSITION, STRUCTURE, AND HEALTH

Present-day forests provide a wide variety of ecosystem services to the communities that rely on them. At the same time, these environments face routine and substantial disturbances that direct the need for site-specific, timely, and accurate monitoring/management (i.e., precision forestry). Unmanned Aerial Systems (UAS or UAV) and their associated technologies offer a promising tool for conducting such precision forestry. Now, even with only natural color, uncalibrated, UAS imagery, software workflows involving Structure from Motion (SfM) (i.e., digital photogrammetry) modelling and segmentation can be used to characterize the features of individual trees or forest communities. In this research, we tested the effectiveness of UAS-SfM for mapping local scale forest composition, structure, and health. Our first study showed that digital (automated) methods for classifying forest composition that utilized UAS imagery produced a higher overall accuracy than those involving other high-spatial-resolution imagery (7.44% - 16.04%). The second study demonstrated that natural color sensors could provide a highly efficient estimate of individual tree diameter at breast height (dbh) (± 13.15 cm) as well as forest stand basal area, tree density, and stand density. In the final study, we join a growing number of researchers examining precision applications in forest health monitoring. Here, we demonstrate that UAS, equipped with both natural color and multispectral sensors, are more capable of distinguishing forest health classes than freely available high-resolution airborne imagery. For five health classes, these UAS data produced a 14.93% higher overall accuracy in comparison to the airborne imagery. Together, these three chapters present a wholistic approach to enhancing and enriching precision forest management, which remains a critical requirement for effectively managing diverse forested landscapes

Generation of Horizontally Curved Driving Lines for Autonomous Vehicles Using Mobile Laser Scanning Data

The development of autonomous vehicle desiderates tremendous advances in three-dimensional (3D) high-definition roadmaps. These roadmaps are capable of providing 3D positioning information with 10-to-20 cm accuracy. With the assistance of 3D high-definition roadmaps, the intractable autonomous driving problem is transformed into a solvable localization issue. The Mobile Laser Scanning (MLS) systems can collect accurate, high-density 3D point clouds in road environments for generating 3D high-definition roadmaps. However, few studies have been concentrated on the driving line generation from 3D MLS point clouds for highly autonomous driving, particularly for accident-prone horizontal curves with the problems of ambiguous traffic situations and unclear visual clues. This thesis attempts to develop an effective method for semi-automated generation of horizontally curved driving lines using MLS data. The framework of research methodology proposed in this thesis consists of three steps, including road surface extraction, road marking extraction, and driving line generation. Firstly, the points covering road surface are extracted using curb-based road surface extraction algorithms depending on both the elevation and slope differences. Then, road markings are identified and extracted according to a sequence of algorithms consisting of geo-referenced intensity image generation, multi-threshold road marking extraction, and statistical outlier removal. Finally, the conditional Euclidean clustering algorithm is employed followed by the nonlinear least-squares curve-fitting algorithm for generating horizontally curved driving lines. A total of six test datasets obtained in Xiamen, China by a RIEGL VMX-450 system were used to evaluate the performance and efficiency of the proposed methodology. The experimental results demonstrate that the proposed road marking extraction algorithms can achieve 90.89% in recall, 93.04% in precision and 91.95% in F1-score, respectively. Moreover, the unmanned aerial vehicle (UAV) imagery with 4 cm was used for validation of the proposed driving line generation algorithms. The validation results demonstrate that the horizontally curved driving lines can be effectively generated within 15 cm-level localization accuracy using MLS point clouds. Finally, a comparative study was conducted both visually and quantitatively to indicate the accuracy and reliability of the generated driving lines