Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping

This work investigates the use of semantic information to link ground level occupancy maps and aerial images. A ground level semantic map, which shows open ground and indicates the probability of cells being occupied by walls of buildings, is obtained by a mobile robot equipped with an omnidirectional camera, GPS and a laser range finder. This semantic information is used for local and global segmentation of an aerial image. The result is a map where the semantic information has been extended beyond the range of the robot sensors and predicts where the mobile robot can find buildings and potentially driveable ground

Incident and Traffic-Bottleneck Detection Algorithm in High-Resolution Remote Sensing Imagery

One  of  the  most  important  methods  to  solve  traffic  congestion  is  to detect the incident state of a roadway. This paper describes the development of a method  for  road  traffic  monitoring  aimed  at  the  acquisition  and  analysis  of remote  sensing  imagery.  We  propose  a  strategy  for  road  extraction,  vehicle detection  and incident detection  from remote sensing imagery using techniques based on neural networks, Radon transform  for angle detection and traffic-flow measurements.  Traffic-bottleneck  detection  is  another  method  that  is  proposed for recognizing incidents in both offline and real-time mode. Traffic flows and incidents are extracted from aerial images of bottleneck zones. The results show that the proposed approach has a reasonable detection performance compared to other methods. The best performance of the learning system was a detection rate of 87% and a false alarm rate of less than 18% on 45 aerial images of roadways. The performance of the traffic-bottleneck detection  method had a detection rate of 87.5%

An integrated study of earth resources in the state of California using remote sensing techniques

The University of California has been conducting an investigation which seeks to determine the usefulness of modern remote sensing techniques for studying various components of California's earth resources complex. Most of the work has concentrated on California's water resources, but with some attention being given to other earth resources as well and to the interplay between them and California's water resources

Drones in the Desert: Augmenting HMA and Socio-Economic Activities in Chad

Funded by the Belgian Directorate-General for Development and led by Humanity & Inclusion (HI) under the auspices of the National Mine Action Centre, Haut Commissariat National au Déminage (HCND) in Chad, the Odyssey2025 Project explores ways to accelerate land release for the local population with the combined use of small consumer drones, new survey methods, and mobile data collection. Project partners include Mobility Robotics, Dynergie, InZentive, and Third Element Aviation. A practical, field-driven approach is being undertaken together with partners in the PRODECO project, Mines Advisory Group (MAG), and Fondation Suisse de Déminage (FSD)

Smart Cities: Inverse Design of 3D Urban Procedural Models with Traffic and Weather Simulation

Urbanization, the demographic transition from rural to urban, has changed how we envision and share the world. From just one-fourth of the population living in cities one hundred years ago, now more than half of the population does, and this ratio is expected to grow in the near future. Creating more sustainable, accessible, safe, and enjoyable cities has become an imperative

DETERMINING WHERE INDIVIDUAL VEHICLES SHOULD NOT DRIVE IN SEMIARID TERRAIN IN VIRGINIA CITY, NV

This thesis explored elements involved in determining and mapping where a vehicle should not drive off-road in semiarid areas. Obstacles are anything which slows or obstructs progress (Meyer et al., 1977) or limits the space available for maneuvering (Spenko et al., 2006). This study identified the major factors relevant in determining which terrain features should be considered obstacles when off-road driving and thus should be avoided. These are elements relating to the vehicle itself and how it is driven as well as terrain factors of slope, vegetation, water, and soil. Identification of these in the terrain was done using inferential methods of Terrain Pattern Recognition (TPR), analyzing of remotely sensing data, and Digital Elevation Map (DEM) data analysis. Analysis was further refined using other reference information about the area. Other factors such as weather, driving angle, and environmental impact are discussed. This information was applied to a section of Virginia City, Nevada as a case-study. Analysis and mapping was done purposely without field work prior to mapping to determine what could be assessed using only remote means. Not all findings from the literature review could be implemented in this trafficability study. Some methods and trafficability knowledge could not be implemented and were omitted due to data being unavailable, un-acquirable, or being too coarsely mapped to be useful. Examples of these are Lidar mapping of the area, soil profiling of the terrain, and assessment of plant species present in the area for driven-over traction and tire punctures. The Virginia City section was analyzed and mapped utilizing hyperspectral remotely sensed image data, remote-sensor-derived DEM data was used in a Geographical Information Systems (GIS). Stereo-paired air photos of the study site were used in TPR. Other information on flora, historical weather, and a previous soil survey map were used in a Geographical Information System (GIS). Field validation was used to check findings.The case study's trafficability assessment demonstrated methodologies of terrain analysis which successfully classified many materials present and identified major areas where a vehicle should not drive. The methods used were: Manual TPR of the stereo-paired air photo using a stereo photo viewer to conduct drainage-tracing and slope analysis of the DEM was done using automated methods in ArcMap. The SpecTIR hyperspectral data was analyzed using the manual Environment for Visualizing Images (ENVI) software hourglass procedure. Visual analysis of the hyperspectral data and air photos along with known soil and vegetation characteristics were used to refine analyses. Processed data was georectified using SpecTIR Geographic Lookup Table (GLT) input geometry, and exported to and analyzed in ArcMap with the other data previously listed. Features were identified based on their spectral attributes, spatial properties, and through visual analysis. Inaccuracies in mapping were attributable largely to spatial resolution of Digital Elevation Maps (DEMs) which averaged out some non-drivable obstacles and parts of a drivable road, subjective human and computer decisions during the processing of the data, and grouping of spectral end-members during hyperspectral data analysis. Further refinements to the mapping process could have been made if fieldwork was done during the mapping process.Mapping and field validation found: several manmade and natural obstacles were visible from the ground, but these obstacles were too fine, thin, or small to be identified from the remote sensing data. . Examples are fences and some natural terrain surface roughness - where the terrain's surface deviated from being a smooth surface, exhibiting micro- variations in surface elevation and/or textures. Slope analysis using the 10-meter and 30-meter resolution DEMs did not accurately depict some manmade features [eg. some of the buildings, portions of roads, and fences], evident with a well-trafficked paved road showing in DEM analysis as having too steep a slope [beyond 15°] to be drivable. Some features had been spectrally grouped together during analysis, due to similar spectral properties. Spectral grouping is a process where the spectral class's pixel areas are reviewed and classes which have too few occurrences are averaged into similar classes or dropped entirely. This is done to reduce the number of spectrally unique material classes to those that are most relevant to the terrain mapped. These decisions are subjective and in one case two similar spectral material classes were combined. In later evaluation should have remained as two separate material classes. In field sample collection, some of the determined features; free-standing water and liquid tanks, were found to be inaccessible due to being on private land and/or fence secured. These had to be visually verified - photos were also taken. Further refinements to the mapping could have been made if fieldwork was done during the mapping process. Determining and mapping where a vehicle should not drive in semiarid areas is a complex task which involves many variables and reference data types. Processing, analyzing, and fusing these different references entails subjective manual and automated decisions which are subject to errors and/or inaccuracies at multiple levels that can individually or collectively skew results, causing terrain trafficability to be depicted incorrectly. That said, a usable reference map is creatable which can assist decision makers when determining their route(s)