R3^3SGM: Real-time Raster-Respecting Semi-Global Matching for Power-Constrained Systems

Stereo depth estimation is used for many computer vision applications. Though many popular methods strive solely for depth quality, for real-time mobile applications (e.g. prosthetic glasses or micro-UAVs), speed and power efficiency are equally, if not more, important. Many real-world systems rely on Semi-Global Matching (SGM) to achieve a good accuracy vs. speed balance, but power efficiency is hard to achieve with conventional hardware, making the use of embedded devices such as FPGAs attractive for low-power applications. However, the full SGM algorithm is ill-suited to deployment on FPGAs, and so most FPGA variants of it are partial, at the expense of accuracy. In a non-FPGA context, the accuracy of SGM has been improved by More Global Matching (MGM), which also helps tackle the streaking artifacts that afflict SGM. In this paper, we propose a novel, resource-efficient method that is inspired by MGM's techniques for improving depth quality, but which can be implemented to run in real time on a low-power FPGA. Through evaluation on multiple datasets (KITTI and Middlebury), we show that in comparison to other real-time capable stereo approaches, we can achieve a state-of-the-art balance between accuracy, power efficiency and speed, making our approach highly desirable for use in real-time systems with limited power.Comment: Accepted in FPT 2018 as Oral presentation, 8 pages, 6 figures, 4 table

Infiltration Route Analysis Using Thermal Observation Devices (TOD) and Optimization Techniques in a GIS Environment

Infiltration-route analysis is a military application of geospatial information system (GIS) technology. In order to find susceptible routes, optimal-path-searching algorithms are applied to minimize the cost function, which is the summed result of detection probability. The cost function was determined according to the thermal observation device (TOD) detection probability, the viewshed analysis results, and two feature layers extracted from the vector product interim terrain data. The detection probability is computed and recorded for an individual cell (50 m × 50 m), and the optimal infiltration routes are determined with A* algorithm by minimizing the summed costs on the routes from a start point to an end point. In the present study, in order to simulate the dynamic nature of a real-world problem, one thousand cost surfaces in the GIS environment were generated with randomly located TODs and randomly selected infiltration start points. Accordingly, one thousand sets of vulnerable routes for infiltration purposes could be found, which could be accumulated and presented as an infiltration vulnerability map. This application can be further utilized for both optimal infiltration routing and surveillance network design. Indeed, dynamic simulation in the GIS environment is considered to be a powerful and practical solution for optimization problems. A similar approach can be applied to the dynamic optimal routing for civil infrastructure, which requires consideration of terrain-related constraints and cost functions

A Supervised Approach to Delineate Built-Up Areas for Monitoring and Analysis of Settlements

Monitoring urban growth and measuring urban sprawl is essential for improving urban planning and development. In this paper, we introduce a supervised approach for the delineation of urban areas using commonly available topographic data and commercial GIS software. The method uses a supervised parameter optimization approach along with buffer-based quality measuring method. The approach was developed, tested and evaluated in terms of possible usage in monitoring built-up areas in spatial science at a very fine-grained level. Results show that built-up area boundaries can be delineated automatically with higher quality compared to the settlement boundaries actually used. The approach has been applied to 166 settlement bodies in Germany. The study shows a very efficient way of extracting settlement boundaries from topographic data and maps and contributes to the quantification and monitoring of urban sprawl. Moreover, the findings from this study can potentially guide policy makers and urban planners from other countries

The Importance of Landscape Position Information and Elevation Uncertainty for Barrier Island Habitat Mapping and Modeling

Barrier islands provide important ecosystem services, including storm protection and erosion control to the mainland, habitat for fish and wildlife, and tourism. As a result, natural resource managers are concerned with monitoring changes to these islands and modeling future states of these environments. Landscape position, such as elevation and distance from shore, influences habitat coverage on barrier islands by regulating exposure to abiotic factors, including waves, tides, and salt spray. Geographers commonly use aerial topographic lidar data for extracting landscape position information. However, researchers rarely consider lidar elevation uncertainty when using automated processes for extracting elevation-dependent habitats from lidar data. Through three case studies on Dauphin Island, Alabama, I highlighted how landscape position and treatment of lidar elevation uncertainty can enhance habitat mapping and modeling for barrier islands. First, I explored how Monte Carlo simulations increased the accuracy of automated extraction of intertidal areas. I found that the treatment of lidar elevation uncertainty led to an 80% increase in the areal coverage of intertidal wetlands when extracted from automated processes. Next, I extended this approach into a habitat mapping framework that integrates several barrier island mapping methods. These included the use of landscape position information for automated dune extraction and the use of Monte Carlo simulations for the treatment of elevation uncertainty for elevation-dependent habitats. I found that the accuracy of dune extraction results was enhanced when Monte Carlo simulations and visual interpretation were applied. Lastly, I applied machine learning algorithms, including K-nearest neighbor, support vector machine, and random forest, to predict habitats using landscape position information extracted from topobathymetric data. I used the habitat map to assess the accuracy of the prediction model and I assessed the ability of the model to generalize by hindcasting habitats using historical data. The habitat model had a deterministic overall accuracy of nearly 70% and a fuzzy overall accuracy of over 80%. The hindcast model had a deterministic overall accuracy of nearly 80% and the fuzzy overall accuracy was over 90%. Collectively, these approaches should allow geographers to better use geospatial data for providing critical information to natural resource managers for barrier islands

An Interactive WebGIS Integrating Environmental Susceptibility Mapping in a Self-Burning Waste Pile Using a Multi-Criteria Decision Analysis Approach

Mining activities promote resulting wastes, so coal mines are prone to release contaminants to the environment, namely to the soil and water. Therefore, the analysis of this type of risk is crucial in waste pile management. The Sao Pedro da Cova (Porto, Portugal) coal waste pile has been studied in recent years, with several data acquired from 2019-2021 under a research project using distinct methodologies. These results are now combined in a multi-approach method to estimate the environmental impacts of the waste pile and identify the contamination. With the integration of all the data in a Geographical Information System (GIS) environment, and to fulfill a scientific gap, this study aims: (i) to create a susceptibility map of contamination in the areas surrounding the self-burning coal waste pile in Sao Pedro da Cova, using Analytical Hierarchy Process (AHP) and Fuzzy AHP approaches; and (ii) to develop a webGIS application incorporating all the information acquired that can be useful for the residents of Sao Pedro da Cova and also to the decision-making public entities and researchers. The results obtained show that the contamination susceptibility is higher surrounding the abandoned mine, particularly along the waste piles and the corresponding runoff areas, which can be especially sensitive