Lifting GIS Maps into Strong Geometric Context for Scene Understanding

Contextual information can have a substantial impact on the performance of visual tasks such as semantic segmentation, object detection, and geometric estimation. Data stored in Geographic Information Systems (GIS) offers a rich source of contextual information that has been largely untapped by computer vision. We propose to leverage such information for scene understanding by combining GIS resources with large sets of unorganized photographs using Structure from Motion (SfM) techniques. We present a pipeline to quickly generate strong 3D geometric priors from 2D GIS data using SfM models aligned with minimal user input. Given an image resectioned against this model, we generate robust predictions of depth, surface normals, and semantic labels. We show that the precision of the predicted geometry is substantially more accurate other single-image depth estimation methods. We then demonstrate the utility of these contextual constraints for re-scoring pedestrian detections, and use these GIS contextual features alongside object detection score maps to improve a CRF-based semantic segmentation framework, boosting accuracy over baseline models

Radiography of the Past - Three Dimensional, Virtual Reconstruction of a Roman Town in Lusitania

The European project, "RADIO-PAST" was launched in 2009 within the Marie Curie framework "Industry-Academia Partnerships and Pathways". The project aims to join resources and different skills to tackle each possible aspect connected with "non-destructive" approaches to understand and reconstruct complex archaeological sites. The consortium of 7 partners has chosen an "open laboratory for research and experimentation" in and around the abandoned Roman site of Ammaia in central Portugal, but some research activities are carried out by the partner institutions in different areas of the Mediterranean and continental Europe. This paper describes the various methods and procedures which were used to undertake the three dimensional reconstruction of this Roman urban site in Lusitania.7reasons Medien GmbH, 1200 Vienna, Bäuerlegasse 3-4, Austria, Department of Archaeology, Ghent University, 9000 Gent, Sint-Pietersnieuwstraat 35, University of Évora CIDEHUS, 7002-554 Évora, Palácio do Vimioso Apartado 94, Portugal, Department of Humanistics, University of Cassino, 03043 Cassino (FR), Via Marconi 10, Italy

Cluster-Wise Ratio Tests for Fast Camera Localization

Feature point matching for camera localization suffers from scalability problems. Even when feature descriptors associated with 3D scene points are locally unique, as coverage grows, similar or repeated features become increasingly common. As a result, the standard distance ratio-test used to identify reliable image feature points is overly restrictive and rejects many good candidate matches. We propose a simple coarse-to-fine strategy that uses conservative approximations to robust local ratio-tests that can be computed efficiently using global approximate k-nearest neighbor search. We treat these forward matches as votes in camera pose space and use them to prioritize back-matching within candidate camera pose clusters, exploiting feature co-visibility captured by clustering the 3D model camera pose graph. This approach achieves state-of-the-art camera localization results on a variety of popular benchmarks, outperforming several methods that use more complicated data structures and that make more restrictive assumptions on camera pose. We also carry out diagnostic analyses on a difficult test dataset containing globally repetitive structure that suggest our approach successfully adapts to the challenges of large-scale image localization

Geospatial Information Research: State of the Art, Case Studies and Future Perspectives

Geospatial information science (GI science) is concerned with the development and application of geodetic and information science methods for modeling, acquiring, sharing, managing, exploring, analyzing, synthesizing, visualizing, and evaluating data on spatio-temporal phenomena related to the Earth. As an interdisciplinary scientific discipline, it focuses on developing and adapting information technologies to understand processes on the Earth and human-place interactions, to detect and predict trends and patterns in the observed data, and to support decision making. The authors – members of DGK, the Geoinformatics division, as part of the Committee on Geodesy of the Bavarian Academy of Sciences and Humanities, representing geodetic research and university teaching in Germany – have prepared this paper as a means to point out future research questions and directions in geospatial information science. For the different facets of geospatial information science, the state of art is presented and underlined with mostly own case studies. The paper thus illustrates which contributions the German GI community makes and which research perspectives arise in geospatial information science. The paper further demonstrates that GI science, with its expertise in data acquisition and interpretation, information modeling and management, integration, decision support, visualization, and dissemination, can help solve many of the grand challenges facing society today and in the future

Mapping and monitoring forest remnants : a multiscale analysis of spatio-temporal data

KEYWORDS : Landsat, time series, machine learning, semideciduous Atlantic forest, Brazil, wavelet transforms, classification, change detectionForests play a major role in important global matters such as carbon cycle, climate change, and biodiversity. Besides, forests also influence soil and water dynamics with major consequences for ecological relations and decision-making. One basic requirement to quantify and model these processes is the availability of accurate maps of forest cover. Data acquisition and analysis at appropriate scales is the keystone to achieve the mapping accuracy needed for development and reliable use of ecological models.The current and upcoming production of high-resolution data sets plus the ever-increasing time series that have been collected since the seventieth must be effectively explored. Missing values and distortions further complicate the analysis of this data set. Thus, integration and proper analysis is of utmost importance for environmental research. New conceptual models in environmental sciences, like the perception of multiple scales, require the development of effective implementation techniques.This thesis presents new methodologies to map and monitor forests on large, highly fragmented areas with complex land use patterns. The use of temporal information is extensively explored to distinguish natural forests from other land cover types that are spectrally similar. In chapter 4, novel schemes based on multiscale wavelet analysis are introduced, which enabled an effective preprocessing of long time series of Landsat data and improved its applicability on environmental assessment.In chapter 5, the produced time series as well as other information on spectral and spatial characteristics were used to classify forested areas in an experiment relating a number of combinations of attribute features. Feature sets were defined based on expert knowledge and on data mining techniques to be input to traditional and machine learning algorithms for pattern recognition, viz . maximum likelihood, univariate and multivariate decision trees, and neural networks. The results showed that maximum likelihood classification using temporal texture descriptors as extracted with wavelet transforms was most accurate to classify the semideciduous Atlantic forest in the study area.In chapter 6, a multiscale approach to digital change detection was developed to deal with multisensor and noisy remotely sensed images. Changes were extracted according to size classes minimising the effects of geometric and radiometric misregistration.Finally, in chapter 7, an automated procedure for GIS updating based on feature extraction, segmentation and classification was developed to monitor the remnants of semideciduos Atlantic forest. The procedure showed significant improvements over post classification comparison and direct multidate classification based on artificial neural networks.</p

Offshore marine visualization

In 85 B.C. a Greek philosopher called Posidonius set sail to answer an age-old question: how deep is the ocean? By lowering a large rock tied to a very long length of rope he determined that the ocean was 2km deep. These line and sinker methods were used until the 1920s when oceanographers developed the first echo sounders that could measure the water's depth by reflecting sound waves off the seafloor. The subsequent increase in sonar depth soundings resulted in oceanologists finally being able to view the alien underwater landscape. Paper printouts and records dominated the industry for decades until the mid 1980s when new digital sonar systems enabled computers to process and render the captured data streams.In the last five years, the offshore industry has been particularly slow to take advantage of the significant advancements made in computer and graphics technologies. Contemporary marine visualization systems still use outdated 2D representations of vessels positioned on digital charts and the potential for using 3D computer graphics for interacting with multidimensional marine data has not been fully investigated.This thesis is concerned with the issues surrounding the visualization of offshore activities and data using interactive 3D computer graphics. It describes the development of a novel 3D marine visualization system and subsequent study of marine visualization techniques through a number of offshore case studies that typify the marine industry. The results of this research demonstrate that presenting the offshore engineer or office based manager with a more intuitive and natural 3D computer generated viewing environment enables complex offshore tasks, activities and procedures to be more readily monitored and understood. The marine visualizations presented in this thesis take advantage of recent advancements in computer graphics technology and our extraordinary ability to interpret 3D data. These visual enhancements have improved offshore staffs' spatial and temporal understanding of marine data resulting in improved planning, decision making and real-time situation awareness of complex offshore data and activities